Modeling latent variables such as physical disability is challenging since its measurement is performed through proxies. This poses significant methodological challenges. The objective of this article is to present three different methods to predict latent variables based on classical summed scores, individual item responses, and latent variable models.This is a review of the literature and data analysis using "layers of information". Data was collected from the North Carolina Back Pain Project, using a modified version of the Roland Questionnaire.The three models are compared in relation to their goals and underlying concepts, previous clinical applications, data requirements, statistical theory, and practical applications. Initial linear regression models demonstrated a difference in disability between genders of 1.32 points  on a scale from 0–23. Subsequent item analysis found contradictory results across items, with no clear pattern. Finally, IRT models demonstrated three items were demonstrated to present differential item functioning. After these items were removed, the difference between genders was reduced to 0.78 points . These results were shown to be robust with re-sampling methods.Purported differences in the levels of a latent variable should be tested using different models to verify whether these differences are real or simply distorted by model assumptions. Clinical researchers frequently use statistical models in an attempt to model outcomes that are not directly measured, also known as latent variables. Examples of such latent variables include mental health, quality of life, and physical disability. Although groups of items (questions) known as outcome scales can be assumed to measure latent variables, it is methodologically challenging to aggregate item responses into scores that accurately and reliably represent the latent variable.The aim of this study is to point that the choice of models with biased assumptions can lead to different conclusions regarding the associations between latent variables and predictors. Three alternative methods are presented: Prediction of latent variables measured as summed scores using linear regression models, prediction of individual item responses using logistic regression models and propensity scores to control for differences in item responses, and prediction of latent variables using Item Response Theory models with covariates. Since all three methods are statistically sophisticated, they will be described using the technique of "layers of information", and used to evaluate the purported association between gender and disability. Specifically, we will test whether this association can be explained by different reporting patterns.The method of "layers of information" was designed to explain complex statistical methods to audiences with a variety of previous quantitative backgrounds. Each layer is associated with a progressive level of complexity; thus, ensuring that readers with different needs can understand the technique to a level that will enable them to at least understand the statistical method of a clinical study (first layer) and ultimately to apply the statistical method to a new research study (last layer). In the current study, we have used five layers of information: (1) General description, (2) Examples of previous clinical applications, (3) Data requirements, (4) Statistical Theory, and (5) Analysis and Reporting.A latent construct is a concept not directly measured, but that can be estimated through proxy measures. Physical disability is an example since its level is frequently inferred from responses given to a series of items in an outcomes scale measuring patients' ability to perform activities of daily living. Because latent variables cannot be directly measured and predicted, several statistical techniques were devised to approach this problem Figure .The most common approach is to simply add patients' responses to each item; thus, creating a summed score. Summed scores are then used to determine significant predictors in a regression model Figure .Two assumptions underlie this strategy. First, we assume that the contribution of each item to the latent variable is known. For example, in a disability scale where patients are questioned about their ability to "raise a glass of water" and to "raise a 40-pound bag", researchers assume that they know the exact amount of disability associated with each of the activities stated by these items. In a scale that does not discriminate between the level of disability associated with each item, the assumption would be that answers to each of these items would represent the same amount of disability, when, in fact, they may not.The second assumption when using summed scores is that each item measures the latent construct without any interference from extraneous factors. For example, it is assumed that two individuals with the same neck disability level, but different educational levels would have similar answer patterns for an item such as "I feel pain in my neck after reading for more than two hours". In this example this assumption might not be true since individuals with different educational levels may have different levels of exposure to a two-hour reading session and consequently have a different perception of the disability caused by such activity. Therefore, in spite of having the same disability level, they would probably provide different answers to the same item. This phenomenon is known as Differential Item Functioning, previously known as item bias.A second approach is to use answers from each item and then determine how each predictor is associated with individual item responses Figure . AlthougThe last and most recent approach is to use statistical models that will concomitantly determine the latent construct level and its association with the predictor of interest Figure . The maiIn a study designed to predict factors associated with post-treatment disability after lower-extremity soft tissue sarcoma, Davis  calculatIn a study evaluating the prediction of visual disability based on individual objective measures of visual impairment, Bandeen-Roche  regresseAlthough these models bring new insights into the association between individual physical activities and their respective predictors, they cannot clarify whether these were true predictors or whether they simply presented different reporting patterns.To our knowledge, although multiple previous clinical research projects have used IRT for the determination of scale scores , no prevFirst, a latent construct has to be measured through a set of proxy variables. These indicators may have responses in various formats, including dichotomous (yes/no), ordinal , or nominal . IRT models assume that the latent construct is continuous and, in most cases, unidimensional, meaning that one single latent construct is assumed.Predictors can be continuous or categorical variables.Previous studies have estimated that, for logistic regression models, one should have at least 10 events per predicting variable , while fy using a linear combination of predictor variables xj, where j represents several predicting variables 1, 2, ..., p. It is assumed that no missing values are present for every observation. The fitted values, or predicted summed scores, are then the sum of coefficients βj multiplying each of the xj plus an intercept β0, although the later may be absent in some models. This model can be represented by:Differences in summed scores according to a set of predictor or covariates can be described using linear regression. In these models, the summed score is represented by y - β0 + β1 x1 +...+ βp xpith of n observations are yi, xi1 ,..., xip, then the fitting criterion chooses the βj to minimize:Ordinary least-squares models estimate the coefficients to minimize the squared sum of residuals. If the response and predictors corresponding to the ith observation as:The standard statistical theory of linear models makes the first formula more explicit by writing the model for the ci are independently and identically distributed; the ci have mean zero and finite variance σ2; the ci have a normal distribution.This model makes the following assumptions: The Individual responses to dichotomous items can be predicted by generalized linear models using a binomial distribution and, most commonly, a logit link function that will bound the probability of an answer to be between 0 (answer = no) and 1 (answer = yes). The logit link can be expressed by:π is the probability of a positive answer andx is a vector with item responses . To linearize the function, the dichotomous response for each item can be algebraically transformed to:where p(0|x), where 0 is the latent construct.Notice that, in contrast to linear models, the logistic model does not have an error term since it models the probability of an event directly that will determine the variability of the binary outcome. Logistic models are estimated by maximum likelihood, which is a method to estimate regression coefficients that will maximize the likelihood of obtaining the data  for responses of the same patient to other items. This adjustment can be accomplished by propensity scores , which rIt is important to notice that although the covariates are used as predictors for the item-response, it is still impossible to infer whether this association was distorted by an association between item responses and extraneous variables rather than the association between item responses and the latent trait.i providing a positive answer to an item j is represented by:Although multiple models have been described for the regression of latent variables on predictors , we willβj represents the difficulty of item j and ui represents the trait level associated with subject i. This equation holds true in the simplest IRT model known as Rasch or one-parameter logistic (1PL). Other models – two-parameter logistic, ordinal logistic – among others – are used according to the types of response alternatives presented by each item.Where λ to represent the extent to which item j can discriminate between subjects of different trait levels, we obtain:Adding one additional parameter Finally, if we add a predictor to this equation we will haveγ is the regression coefficient for predictor x. This model allows several advantages over the two models previously described in this layer, including the absence of assumptions from summed scores as well as the summarization of all items into a single latent variable. The most frequent assumptions in IRT models are that a single construct is measured and that observations are independent, conditional on the latent variable. Different IRT models will have different assumptions about the extent to which assumptions of summed scores can be relaxed. For example, 1-Parameter. Logistic Regression models assume that each item measures the latent trait with equivalent strength.where One important practical aspect, when making use of IRT models with predictors, is to check quadrature point approximation used in the random-effects estimator. As a rule of thumb, if the coefficients do not change by more than a relative difference of 0.01%, then the choice of quadrature points does not significantly affect the outcome and the results may be confidently interpreted. Two aspects of random-effects models have the potential to make the quadrature approximation inaccurate: large group sizes and large correlations within groups .To illustrate a practical application of the previously described models, we will use data from a cohort study of patients with low-back pain to evaluate the gender-disability association. Specifically, we will evaluate whether female patients either have more severe disability or simply whether they are more likely to give positive answers to some items while having equivalent physical disability levels.Several studies have found that, compared to men, women are usually associated with higher initial disability and pain scores after low-back pain episodes ,15. HoweA description of the cohort used for this analysis is presented in detail elsewhere . BrieflyBriefly, our sample is composed by 1,633 individuals with a diagnosis of low-back pain. Most patients are females (52.3%), married (69.9%), white (83.0), and with medical insurance (68.3%).For linear and logistic regression models the data were placed in wide format, with individual variables representing patient responses to each item. For IRT models the data were presented in long compressed format Figure .When comparing the crude association between summed scores and gender, it was found that female patients had scores that were on average 1.46  points higher than their male counterparts in a 0–23 scale. This association was further tested in a linear regression model Figure  controllSince the distribution of summed scores of the modified Roland Questionnaire was not normal, we used regression diagnostics using plots to determine that the relationship between predicted and observed values did not display any violations of the regression assumptions. This was confirmed by a Ramsey regression specification error test (RESET) for omitted variables (p = 0.7371) although the Breusch-Pagan / Cook-Weisberg test demonstrated a trend towards heteroscedacity (p = 0.0777).In order to further verify the robustness of this association, an ordinal logistic regression model was used with cut-points at 0–7 (low summed score), 8–15 (medium summed score), and 16–23 (high summed score). This model was considered to adequately comply with the proportionality assumption (p = 0.776). Results for the ordinal regression model demonstrated that the predicted probability of a male having low, intermediate, and high scores were progressively decreasing: 0.38, 0.33, and 0.28, respectively. This pattern was in contrast with women, where the probabilities were ascending: 0.32, 0.33, and 0.35, respectively.In summary, all results from models using summed scores point to a significant association between female gender and high disability scores. It is unclear; however, whether this association can be explained by high disability levels or simply different report patterns between men and women.As a next step, the association between individual item responses and gender was evaluated using logistic regression models stratified by propensity scores adjusting for responses to other items Figure . PropensThe analysis across propensity strata demonstrated contradictory results, with male patients being significantly associated with positive responses to items 4  and 8 ("I only stand for short periods of time because of my back problem or leg pain (sciatica)"), while female patients were significantly associated with positive responses on items 7 "), 15 , 17 ("I stay in bed most of the time because of my back or leg pain (sciatica)"), and 19 . No single item was consistently associated with gender across all propensity score strata.A new model was then built adjusting for scores pooled across strata. The results demonstrated that most items were not associated with either gender, items 4  and 8 ("I only stand for short periods of time because of my back problem or leg pain (sciatica)") being positively associated with male gender while items 7 ") and 15  being associated with female gender Figure .Since logistic regression models do not control for the latent variable one cannot test whether the association between gender and individual item responses is related to an association with disability or simply caused by women being more likely to provide a positive response to a certain item in spite of having the same degree of disability.Finally, IRT models Figure  were useTo test the hypothesis that some items might present different reporting patterns, we tested for interaction terms between each item and gender. Our results demonstrated that items 7 ", Figure A new IRT model was then calculated, but now excluding all items with differential reporting patterns. The difference in disability reporting between men and women was reduced , indicating that gender was no longer significantly associated with disability. In fact, when the same items were excluded from the summed score, a multiple linear regression model demonstrated that the difference between female and male patients had been reduced to 0.78 points  on the original 0–23 scale (p = 0.06), a reduction of 53.4% compared to the original difference.Bootstrapping methods were used in the linear regression model to verify whether the association was robust after multiple sampling procedures had been applied to the models. The results demonstrated a variation of only 13.2%; thus, indicating that these results are robust provided that the sample is representative of the study population.In conclusion, one could infer that although women still have slightly more disability than men, much of the previously reported differences using the modified Roland were inflated by the presence of items with different reporting patterns in scales measuring disability.We used three different regression models to investigate the association between gender and disability. Although summed models demonstrated a significant association between gender and disability, these models did not allow us to test whether this purported difference was related to the latent construct disability or to items presenting with differential item functioning. Analysis of the association within individual items demonstrated inconsistent associations with gender, with some items presenting a strong positive association with male gender while others had a positive association with female gender. Since these associations were made with the item response rather than the latent variable, it was impossible to verify whether these were valid representations of the construct of interest, associations with disability, or simply the effects of differential item functioning. Last, we examined the association between gender and disability measured as a latent variable. After removing items with differential item functioning, the association with gender was lessened and no longer significant. Therefore, we concluded that although a small difference between genders in relation to the disability associated with low back pain does exist, much of it is caused by differential item functioning than a true association with the disability construct.In summary, we advocate that the measurement of the association between latent variables and covariates be systematically performed using a combination of regression models to ensure that observed associations are not distorted by differential item functioning.RP: design, analysis, manuscript writing; MT: design, analysis, manuscript revision; UG: design, analysis, manuscript revision; LDH: design, manuscript revision; DOJ: design, manuscript revision; TC: data collection, design, manuscript revision.


Mycoplasma hominis vaa gene encodes a highly variable, surface antigen involved in the adhesion to host cells. We have analysed the structure of the vaa locus to elucidate the genetic basis for variation of vaa.The vaa on existing physical maps of five M. hominis isolates by pulsed field gel electrophoresis revealed that vaa is located in a genomic region containing the majority of other characterized membrane protein genes of M. hominis. Sequencing of an 11 kb region containing the vaa locus of M. hominis isolate 132 showed the presence of conserved housekeeping genes at the borders of the region, uvrA upstream and the hitABL operon downstream to vaa. Analysis of 20 M. hominis isolates revealed that the vaa upstream region was conserved whereas the downstream region was highly variable. In isolate 132 this region contained an open reading frame (ORF) encoding a putative 160 kDa membrane protein. Homologous ORFs were present in half of the isolates, whereas this ORF, termed vmp (variable membrane protein), was deleted from the locus in the remaining isolates. Compellingly, the conserved upstream region and variable downstream region of vaa correlates with the genetic structure of vaa itself which consists of a conserved 5' end and a variable 3' end containing a variable number of exchangeable sequence cassettes.Mapping of vaa locus contains a divergent genetic islet, and indicate pronounced intraspecies recombination. The high variability level of the locus indicate that it is a chromosomal 'hot spot', presumably important for sustaining diversity and a high adaptation potential of M. hominis.Our data demonstrate that the  The mycoplasmas are wall-less prokaryotes characterized by small genomes 580 – 2200 kb) and a low G+C content, generally below 30%. They are the smallest self-replicating organisms known with cell diameters normally in the range of 0.3–0.8 μm , and are200 kb anMycoplasma hominis is an opportunistic human pathogen observed as a commensal of the urogenital tract. Primarily, urogenital infections giving rise to spontaneous abortions, pelvic inflammatory disease, and acute pyelonephritis have been associated with M. hominis, but extragenital infections resulting in infant meningitis, arthritis, and septicemia have been reported [reported .M. hominis is a very heterogeneous species as measured by a pronounced antigenic variation [M. hominis surface proteins has been elucidated in some cases. The large membrane protein (lmp) gene family displays size variation by insertion/deletion of intragenic repeats of approximately 500 bp. The lmp genes are arranged in two clusters, lmp1-2 and lmp3-4 in the M. hominis genome of most analysed isolates with a distance between the clusters of more than 110 kb [lmp family is expressed in each of the M. hominis isolates tested and decrease in the number of repeats were found to correlate to the amount of spontaneous agglutination of M. hominis cells [ariation -7. The mn 110 kb . At leasis cells ,10.vaa (variable adherence-associated) gene encodes a size and phase variable M. hominis adhesin [-3–10-4. In the ON-state 8 adenines are observed in the poly-A tract, whereas 7 or 9 results in the out-of-frame OFF-state [vaa gene is present in each M. hominis isolate [vaa [vaa gene types have been observed in more than 100 analysed clinical isolates [The  adhesin -14. Phas adhesin . This isFF-state . A singl isolate . The sizate [vaa ,13. Eachate [vaa . Based ovaa and variation mechanisms in M. hominis in general, the vaa locus was characterized by mapping of the genomic position in five isolates and sequencing of a 11 kb region containing the vaa gene from isolate 132. Furthermore, the vaa locus of 20 M. hominis isolates was investigated by PCR and sequencing. In contrast to the more conserved vaa upstream region this analysis revealed that the downstream region also exhibits major variation caused by insertion/deletion and sequence variation of a large ORF encoding a putative membrane protein. Thus the vaa locus seems to constitute a 'hot spot' for variation in the M. hominis genome.To obtain a better understanding of the genomic basis for variation of vaa gene was mapped on exsisting physical and genetic maps of 5 M. hominis isolates: 132, 4195, 7488, PG21, and 93 dATP-labelled DNA fragments representing different parts of selected vaa genes . All probes hybridized to a single fragment in all digests, corresponding to the same region of the genome for all 5 isolates . The band differences observed could be explained by the presence or absence of an EcoRI site in the variable vaa gene. Using a probe comprising the cassette region of the vaa category 3 (vaa-3) gene (probe 4) band size variation was observed in the 5 isolates for both enzymes  protein encoded by hitL. This system was previously characterized in M. hominis by Henrich and coworkers [hitABL operon was located almost 5 kb downstream to the vaa gene of M. hominis 132 , encoding a hypothetical 35 kDa protein, showed high similarity to a range of hypothetical proteins of similar size in the database. All of these proteins contained a HAD hydrolase superfamily motif. The highest similarity was to a hypothetical protein of Mycoplasma pulmonis . No significant homologues were found for ORFs 2 to 5. ORF5 was shown to encode a hypothetical protein containing an N-terminal signal peptide with a signal peptidase II cleavage site typical of prokaryotic prolipoproteins and may thus encode a lipoprotein having a size of 29 kDa and a pI of 9.6. Interestingly, a transmembrane helix was predicted in the C-terminal part of this putative lipoprotein using the program TMHMM (membrane probability of 1 for aa 243 to 252) [Analysis of the contig harboring the mes Fig. . At the kDa Fig. . This OR to 252) .vaa revealed the presence of a large open reading frame of 4 kb, ORF6, encoding a hypothetical protein with a molecular weight of 160 kDa , and other myosin proteins in the database. In the region between ORF6 and the hitABL operon, a tRNA(His) gene was identified by database search  of Bacillus subtilis. Intriguingly, the 5' end of the gene displayed a high similarity to the orthologue from Streptococcus pneumoniae (94% identity from bp 10 to 45), even higher than to the corresponding region in Bacillus subtilis (92% identity) and Mycoplasma pneumoniae (86% identity). The transcriptional direction of ORF6, tRNA(His) and the hitABL operon was opposite that of vaa in analogy to the ORFs of the vaa upstream region . It was not possible to classify the profiles according to the vaa type or other known M. hominis groupings. Thus, despite a higly conserved organization and length of the ORFs in the vaa upstream region, there is an underlying sequence variation, presumably corresponding to the background variation level present in the M. hominis genome.To examine the variability of the ed Figs.  and 4B. vaa and hitB, respectively, were used in PCR, a pattern revealing different product sizes was observed . The isolates were divided into vmp groups based on the above PCR results according to the size of the vmp gene observed in the different sized downstream PCR products. The vmp gene having a size of 4 kb was named vmp category 1 or simply vmp-1, and the isolates  showing high homology of the stem regions to that observed for vmp-1. The stem-loop structure was located approximately 500 bp downstream of the stop codon of vmp-2 but interestingly, the homology between vmp-2 and vmp-3 extends beyond the stop codon in vmp-2. Careful analysis reveals that a poly-A tract in the 3'-end of the vmp genes has an extra  adenine in vmp-2 compared to vmp-3, which causes a premature termination of translation of the vmp-2 gene. If the extra adenine of the poly-A tract was deleted the ORF would continue for approximately 500 bp, corresponding to vmp-3, and the termination codon would be situated close the putative rho-independent transcriptional termination stem-loop structure. The remaining 10 isolates were divided into two groups based on a genetic fingerprint in the vaa-hitABL intergenic region of isolates lacking vmp  gene was different for vmp-1 and vmp-2, the latter positioned between tRNA(His) and hitABL, whereas the more similar vmp-2 and vmp-3 genes were positioned identically  sequence box between the insertion site in isolate PG21 and the stem region of the stem-loops of vmp-2 and vmp-3. Furthermore, a 165 bp deletion  intergenic region of isolate 7488 compared to isolate PG21. This deletion was located immediately downstream to the region deleted in isolate 93. In contrast, analysis of the vmp-1 insertion site did not show insertion at the stem-loop and poly-T structures when compared to the vaa-hitABL intergenic regions of isolates PG21 and 93. Sequence regions of 60 bp downstream to the stem-loop structure and 90 bp upstream to the poly-T tract which did not show any homology to the vaa-hitABL intergenic region of isolate PG21 were observed at the borders of the vmp-1 gene. Interestingly, comparison of the insertion site of the vmp-1 region including the 60 bp and 90 bp bordering sequences with the vaa-hitABL intergenic region of isolate PG21 revealed that insertion resulted in a deletion in the intergenic sequence corresponding to the 300 bp deletion . Half of this region was repeated once in Vmp-3 Fig. . The N-tSequence analysis revealed that the deduced Vmp proteins have a predominantly alpha-helical structure, and a coiled-coil region extending throughout almost the entire length of the proteins was identified Fig.  and 6C. vmp-1 gene of these isolates. These data thus demonstrate that Vmp is expressed in M. hominis. Furthermore, the antibody reacted with a 100 kDa protein in all isolates except PG21. This is presumably a protein that has been shown previously to bind Ig-molecules unspecifically, and is not present in PG21 [vmp gene, and isolates 4195 and 7488 having a Vmp-2 and Vmp-3 type, respectively, did not react with the antibody, as expected from the low homology of the C-terminal region  gene, and the lack of correlation of vmp type and absence of vmp to vaa type suggests frequent recombination in this locus  or genetic islands (>10 kb), are variable sites when comparing genomes of different isolates of a given species. Often, genetic islets/islands carry pathogenesis factors and are specific for virulent clones of the species. Such factors include adhesins, toxins and restriction/modification systems. Frequently, the genetic elements are inserted into tRNA genes, show a GC content diverging from the surrounding regions and are flanked by repeated elements [vmp gene was located on either side of the tRNA(His) gene in isolates 132 and 7488, respectively, the GC content of vmp was similar to the remaining part of the vaa locus analyzed and the M. hominis genome in general (28%) and despite a thorough analysis, no obvious flanking structures such as direct or inverted repeats were observed which could indicate a site specific mechanism of insertion. Thus, the vmp gene may be mycoplasma specific and insertion/deletion of vmp at the vaa locus seems mediated by homologous recombination.The location of elements . Althougvmp fragments by PCR from four out of five isolates categorized as having a vmp-1 gene type and likewise for four out of five isolates having a vmp-2 or vmp-3 gene type. The pronounced heterogeneity observed between the vmp genes could explain the missing reaction of the remaining two isolates as being caused by sequence variation of the individual gene types as observed for a number of other M. hominis membrane protein genes, but it is also possible that additional vmp gene types exists.It was possible to amplify Mycoplasma pneumoniae. This protein is cytoplasmic, part of the primitive cytoskeleton of this mycoplasma and truncation of the gene resulted in loss of cytadherence [M. hominis in contrast to the cytoplasmic HMW2.The size and predicted structure of Vmp is interesting. The coiled-coil motif extending through most of the protein, is a highly versatile motif involved in protein-protein interactions . This modherence . HMW2 isdherence . In contM. hominis.The identification of a novel putative membrane protein displaying sequence variation is intriguing, and furthermore the remarkable size and structure displayed by the Vmp protein is interesting and should prompt investigations on the biological function of this protein in vaa locus of M. hominis contains a divergent genetic islet encoding a large, putative membrane protein called Variable membrane protein (Vmp). This genetic islet is only present in the locus of half of the 20 islolates tested, and three distinct, homologous Vmp types were observed. The composition of the locus was analysed and it was found that the vaa gene has a conserved upstream region and a highly variable downstream region, which contains the genetic islet. This locus organization corresponds to the organization of the vaa gene itself having a conserved 5' end and a variable 3' end. Thus, the mechanism underlying variation of the vaa gene seems to be intraspecies recombination exchanging variable regions of vaa and downstream regions of vaa, giving rise to a variable and dynamic 'hot spot' in the M. hominis genome.We have demonstrated that in some isolates, the M. hominis isolates were analysed . The M. hominis isolates were cultivated in BEa medium (heart infusion broth (Difco), 2.2% (w/v); horse serum, 15% (v/v); fresh yeast extract, 1.9% (w/v); benzylpenicillin, 40 IU ml-1; L-arginine, 0.23% (w/v); phenol red 0.0023% (w/v)). The pH of the medium was adjusted to 7.2 and the medium was sterilized by filtration [M. hominis isolates were harvested by centrifugation at 15,000 rpm for 45 min at culture volumes greater than 1.5 ml or at 20,000 rpm for 15 min for culture volumes smaller than 1.5 ml. E. coli OneShot competent cells and the pCRII plasmid vectors were used for TA-cloning (Invitrogen).Twenty ltration . The M. M. hominis isolates PG21, 4195, 132, 93 and 7488. The M. hominis isolates were grown in BEa medium to log phase and harvested. The cell pellets were washed and resuspended in PBS buffer . Melted NA agarose (Amersham Pharmacia Biotech) was mixed with the cell suspension in a plastic mold on ice. The agaroseblocks hereby formed were incubated overnight with 1 mg/ml Proteinase K (Roche) in lysis buffer . Subsequently, each block was washed twice in lysis buffer followed by one wash in TE buffer  and cut into eight blocks of identical size. The blocks were digested overnight with 40 units of one of five restriction enzymes  and 12 mg of BSA. Subsequently, the blocks were inserted into the slots of 1% NA agarose gels and the holes sealed with melted NA agarose. HindIII digested λ DNA and a λ DNA ladder (FMC) was used as molecular weight markers. The gels were used for PFGE using the CHEF-DRII separation system (Bio-Rad).PFGE was performed on genomic DNA from the five M. hominis isolates was isolated using the method described in [M. hominis cells were harvested and subsequently lysed on ice in a buffer containing 0.7% (w/v) N-laurylsarcocine, 10 mg RNase ml-1 (Sigma), 20 mM Tris pH 7.5 and 20 mM EDTA. Proteinase K (150 mg ml-1) was added and the cell lysate was incubated at 55°C for 2 hrs and 37°C for 1–2 hrs followed by phenol, phenol/chloroform and chloroform extractions [-1) treatment of harvested M. hominis at 55°C for 1 h. After the incubation the solution was heated to 100°C for 5 min to inactivate the enzyme. Plasmids from transformed E. coli were prepared as described in [DNA from the ribed in . Brieflyractions . DNA preribed in , for seqvaa types with sizes of 1220 bp (probe 1), 600 bp (probe 2), 800 bp (probe 3), and 660 bp (probe 4) were used for TA-cloning, performed according to manufacturers instructions (Invitrogen) [vaa-1 gene from M. hominis 7808, including the three cassettes III, IV and V [vaa-1 gene of M. hominis PG21 and probe 3 contained most of the vaa-3 gene of M. hominis V2785 including cassettes V and VII. TA-cloned PCR fragments or linear PCR fragments were used as DNA probes and labeled with radioactive (α-32P)dATP by nick-translation, performed as follows. 0.5–1 μg DNA was mixed with 1 × nick-translation buffer (50 mM Tris HCL (pH 7.2), 10 mM MgSO4, 0.1 mM DTT, 50 mg BSA, 60 mM of dTTP, dCTP, and dGTP respectively, 5 units of DNA polymerase I (Gibco), 0.5 ng DNase I (Roche), 20 mCi (α-32P)dATP (Du Pont) and ddH2O up to 50 μl. The reaction was incubated at 14–16°C for 1 h. Incorporation of radioactive nucleotides was verified by TLC and terminated by addition of TE-buffer with 0.5 M EDTA. The radioactive probes were denatured by heating to 100°C for 5 min and hybridization performed in 2 × SSC (1 × SSC is 0.15 M NaCl and 0.015 M sodium citrate), 0,5% SDS, 100 mg/ml yeast RNA, 5 × Denhardts solution ) at 60°C in a hybridization oven. The membranes were washed in 6 × SSC and 0.5% SDS. The membranes were placed in sealed plastic bags and exposures of X-ray films were performed at room temperature or at -20°C.PCR-products derived from different itrogen) . Probe 1IV and V . Probe 2HindIII or EcoRI and separated on 0.7% agarose gels. The gels were stained with ethidium bromide and photographed under UV irradiation. Preceding the alkaline denaturation, partial hydrolysis of the DNA in the PFGE gels was performed by soaking in a 0.25 M HCl solution to enhance the transfer of large DNA fragments. DNA transfer to Hybond-N membranes (Amersham Biosciences) was carried out as described in [Genomic DNA samples of the isolates PG21, 4195, 132, 93 and 7488 were cleaved with either ribed in .vmp-2/3 PCR product where Taq polymerase was used (PE Biosystems). Custom oligonucleotide primers were purchased from DNA Technology . PCR products were purified using the Wizard kit (Promega) according to manufacturers instructions. PCR conditions used for inverse PCR and amplification of downstream and upstream regions of the 20 isolates were as follows: 2 min at 92°C, 10 cycles of 10 s at 92°C, 30 s at 55°C, 8 min at 68°C, 20 cycles of 10 s at 92°C, 30 s at 55°C, 8 min at 68°C with 5 s added to the elongation time pr. cycle. Finally, an extension step of 7 min at 68°C was performed. The primers used for amplification of the 5.5 kb upstream product were F1 (CAGTACATGTTAATCCCAGAA GTATAGTTGG) and R1 (GCTGGATAATCGCCGTATGAACCTGC). The R1 primer was also used for amplification of the 4 kb and 0.6 kb PCR products in combination with the primers F2 (GGATCTTCTTTGTGGTCTTCC) and F3 (GGGATAGTTAGTAAAG TTGGAATAGCC), respectively. For amplification of the downstream region in the 20 isolates, the primers F4 (GCAGGTTCATACGGCGATTATCCAGC) and R4 (GCCACTTGCGGTTCTTCC) were used. For the amplification of the 0.6 kb vmp-1 PCR product, the primers F6 (CCACTGATACGTGATTTAAAAAGAAAAG) and R3 (GGTATTGTTTCTTTATCTAAGATGTTTTCAAATTC) were used with the following PCR conditions: 4 min at 94°C, 30 cycles of 15 s at 94°C, 30 s at 50°C, 1 min at 72°C and a final extension of 5 min at 72°C. For amplification of the 1.2 kb vmp-2/3 PCR product, similar conditions were used with an annealing temperature of 57°C and an elongation time of 2 min, and the primers were F5 (GAACAATTAAAAACATTAATTGGCTTAA GTGATG) and R2 (GTTTTATCTACATTGTTTTCGGATAAGG).PCR was performed using the Expand™ High Fidelity PCR System from Roche according to the manufacturer's instructions, except for the amplification of the 1.2 kb AluI and AseI (New England Biolabs) according to manufacturers instructions and analysed on 1 × TBE/2% agarose gels.The 5.5 kb upstream PCR products from the 20 analysed isolates were subjected to restriction endonuclease analysis employing the enzymes Sequencing reactions were carried out bidirectionally using the ABI PRISM Dye Terminator Cycle Sequencing Ready Reaction Kit (Perkin Elmer) on purified plasmid DNA (TA-cloned PCR products) or directly on the purified PCR products according to the instructions supplied by the manufacturer. Sequencing was performed on an ABI PRISM 377 DNA Sequencer from Perkin Elmer.vmp-1 from isolate 132 encoding aa 1281 to 1404 of the Vmp-1 protein. Cloning and expression of the construct was performed using the pET-30 Ek/LIC vector according to manufacturers instructions . The His-tagged fusion protein was purified using a nickel chelated column  under denaturing conditions as previously described [Oligonucleotide primers  were designed in order to amplify by PCR, the region of escribed . Sera co, were used to predict signal sequences and transmembrane helices, respectively [[Computer analysis of the obtained DNA sequences was performed using the Wisconsin Package Version 9.0, Genetics Computer Group (GCG), Madison, Wisc., sequence analysis software package . Data baectively ,24. Enerctively [.The DNA sequences obtained in this study were deposited to the EMBL database under the following accession numbers: AJ416752, AJ545046 AJ629113, AJ629114 and AJ629115.The individual parts of the work presented in the paper were conducted as follows: The ideas and designs of the experiments were developed by all the authors. Pulsed field gel electrophoresis and Southern blottings were performed by JE and TB. PCR, sequencing, and sequence analysis were performed by TB and AB. The fusion protein, polyclonal antisera and immunoblotting were made by TB. The manuscript was primarily written by TB and discussed with and approved by all authors.Multiple sequence alignment. Alignment of the three Vmp types and Lmp1 and Lmp3 from type strain PG21 using ClustalW.Click here for file


Helicobacter pylori, occurring throughout the world and causing gastroduodenal diseases, is one of the most common chronic bacterial agents in humans. The purpose of this study was to measure the general practitioners' (GPs) knowledge and practices pertaining to H. pylori infection.H. pylori.A cross-sectional type questionnaire survey was conducted in all of 19 primary health care centres (PHCC) in Samsun, Turkey, between November 1 and December 31, 2003. The questionnaire was sent to 124 GPs and 109 (87.9 %) of those filled in. They were requested to answer the questions on the knowledge, sources of medical information, diagnostic tests and treatment to H. pylori, being cited by 86 (78.9%) of GPs. Ninety-two (84.4%) of the GPs reported having used one or more tests and 17 (15.6%) never used any test for the diagnosis of H. pylori infection. Only 9.8% had used stool antigen test for diagnosis. GPs reported that they would prescribe symptomatic treatment without ordering diagnostic tests for 29 (26.6%). 54.1% of the GPs explain that they sent patients with H. pylori infection to a specialist, and most used a triple drug regimen containing a PPI. Treatment duration varies between 7 to 28 days. 80.7 of the GPs treat patients for 14 days.Medical journals were the most frequently used source of information on H. pylori infection during post-graduation period should be improved in PHCCs.GPs may not have enough knowledge about the importance of stool antigen test or possibility of usage of this test. GPs have not sufficient knowledge about the difference between symptomatic and asymptomatic individuals. It is thought that GPs preferred to treat the patients with suspected ulcer empirically or to send them to a specialist because of the limited diagnostic conditions. The efforts to educate the GPs about the algorithms regarding the management of  Helicobacter pylori, occurring throughout the world and causing gastroduodenal diseases, is one of the most common chronic bacterial agents in humans [H. pylori is increasing, most ulcers are related to H. pylori infection [H. pylori infection have been promoted in order to improve early detection and treatment of ulcers in dyspeptic patients [n humans . Althougnfection ,3. For mpatients ,5.H. pylori infection in patients with chronic gastritis and peptic ulcer disease in 1983 has fundamentally changed concept of the etiologic, pathogenesis and management of upper gastrointestinal (UGI) diseases [H. pylori related information and the development and publication of international, regional and national guidelines [H. pylori infection and the pathogenesis, diagnosis and treatment of UGI diseases [H. pylori infection and treat if positive, and when to refer patients to a specialist [The successful isolation of diseases . This haidelines . Subsequdiseases . The majH. pylori infection.It is thought that too many patients with dyspeptic symptoms apply to GPs in Turkey. This study was performed a survey of GPs' to assess their knowledge and practices pertaining to H. pylori knowledge section contained 6 questions. The items assessed respondents' knowledge of diagnosis of infection, case selection for treatment and treatment options in H. pylori. Participating GPs were asked to offer the type(s) of diagnostic tests such as ELISA, histology, biopsy urease test (BUT), urea breath test (UBT) or culture of biopsy specimen.A cross-sectional study was conducted in all of 19 primary health care centres (PHCC) in Samsun, Turkey, between November 1 and December 31, 2003. The questionnaire was sent to all GPs (n = 124). 109 of 124 (87.9 %) GPs from different PHCCs completed the survey. The material used was adapted from the questionnaire devised by Sharma et al. [H. pylori and also treat the infection when the test results were positive for H. pylori.A list of 7 different clinical presentations was given. It was asked whether the respondents would offer testing for H. pylori infection among the list of four drug combination regimens included proton pump inhibitor (PPI)-based triple therapies. [H. pylori infection before or after the survey in this study period. Data were given as mean ± standard deviation (SD) and percentage.The respondents were asked to select a regimen for the management of erapies. ,11,12. GThe mean age and working year of the GPs was 31.7 ± 5.4 and 7.2 ± 5.0 years, respectively; 59 (54.1%) of the GPs were women.H. pylori, being cited by 86 (78.9%) of GPs. Pharmaceutical company-sponsored symposia (70.6%), textbooks (64.2%), conferences (20.2%) and on-line sites (6.4%) were the other major source of information used by the GPs. These numbers add up to more than 100% because the GPs had been checked more than one item.Medical journals were the most frequently used source of information on H. pylori infection. Of those, 44.1% had used UBT, 34.5% had used BUT, 23.8 % had used ELISA and 9.8% had used the stool antigen test. The practitioners included in the survey had not equally access to all diagnostic tests mentioned in the study.Ninety-two (84.4%) of the GPs reported having used one or more tests and 17 (15.6%) never used any test for the diagnosis of H. pylori infection in the 9 different clinical situations and the proportions of those who would offer treatment based on a positive test result, were summarized in Table-H. pylori infection to a specialist.The proportions of GPs who would test patients for Treatment regimens of choice are listed in Table-H. pylori detection methods, including ELISA and the UBT, has enabled GPs to diagnose and treat H. pylori infection. The inadequate treatment of peptic ulcer disease results in therapy failures, high recurrence rates, the emergence of resistant bacterial strains, and increased health care costs, therefore clinical application of current knowledge is crucial [UGI symptoms are common reasons for patients to visit GPs. In recent years, the development of non-invasive  crucial ,14.H. pylori infection [H. pylori infection same as Huang J et al.'s study [There are several tests used for diagnosis of nfection ,13,15,16's study . UBT is 's study .H. pylori infection are recommended following resection of early gastric cancer and for low-grade gastric MALT lymphoma. Retesting after treatment may be prudent for patients with bleeding or otherwise complicated peptic ulcer disease [It is worrisome that considerably fewer perceived a need for testing and subsequent treatment in patients with a new diagnosis and past history of duodenal ulcer Table . Both th disease .H. pylori infection, 64.1% of GPs reported that they would offer testing for H. pylori infection and 31.5% of them reported that they would treat H. pylori infection based on a positive test result in asymptomatic individuals. These findings suggest that GPs have not sufficient knowledge about the difference between symptomatic and asymptomatic individuals. On the other hand, in any person testing positive for the infection, treatment may be offered after a full discussion about its potential risks and benefits [Although it is not recommended to test the asymptomatic individuals for benefits .H. pylori therapy was almost never recommended for suspected ulcer disease without the prior use of diagnostic tests [H. pylori infection. 54.1% of the GPs, whether ordering diagnostic tests or not, explain that they send patients with suspected or diagnosed H. pylori infection to a specialist. In the light of these findings, it is thought that GPs preferred to treat the patients with suspected ulcer, empirically or to send them to a specialist because of the limited diagnostic conditions, the lack of rapidly diagnostic tests at PHCCs, or they thought that they should be treated by a specialist.Anti-ic tests ,18. In tH. pylori peptic ulcers are treated with drugs that kill the bacteria, reduce stomach acid, and protect the stomach lining. Antibiotics are used to kill the bacteria. Two types of acid-suppressing drugs might be used: H2 blockers and PPI. H2 blockers and PPI have been prescribed alone for years as a treatment for ulcers. When used alone, these drugs do not eradicate H. pylori and, therefore, do not cure H. pylori-related ulcers. Bismuth subsalicylate, a component of Pepto-Bismol, is used to protect the stomach lining from acid. It also kills H. pylori [. pylori ,7,12,19.H. pylori infection but 15.6% never test. Urea breath test is a commonly used investigative tool for H. pylori infection. Triple therapy consisting of a proton pump inhibitor, clarithromycin and amoxicillin is the most commonly used treatment combination for H. pylori infection.The highest eradication rates are achieved with the following regimens: a PPI, clarithromycin, and either amoxicillin or metronidazole for 2 week; ranitidine bismuth citrate, clarithromycin, and either amoxicillin, metronidazole, or tetracycline for 2 week; a PPI, bismuth, metronidazole, and tetracycline for 1 to 2 week ,11,12,20H. pylori infection among GPs. Our data suggested that pharmaceutical company-sponsored symposia were used very frequently among GPs.It was found that most of the information was being obtained in traditional teaching formats such as medical journals, pharmaceutical company-sponsored symposia, textbooks and conferences. Other studies ,13,17) c,17 c13,1H. pylori infection [Patients with dyspeptic complaints are mostly managed in primary care. The most prescriptions for dyspepsia are empirical without testing due to limitations of diagnostic facilities around the world . Similarnfection ,17.H. pylori is not adequate. The choice of optimal therapeutic decision depends on the appropriate definition of the disease. In order to provide accurate diagnosis and treatment of H. pylori infection, it's suggested that efforts to educate the GPs about the algorithms regarding the management of H. pylori infection during post-graduation period should be improved in PHCCs.The diagnosis of and treatment of peptic ulcer disease related to The author(s) declare that they have no competing interests.SC participated in the design and coordination of the study; ATS provided drafted the manuscript and performed the statistical analysis. YP drafted the questionnaire and participated in study design and coordination. HL conceived the study, participated in its design and drafted the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Reminders at the point of care, computerized or not, have been demonstrated to be effective in changing physicians prescription behavior.The quality control of oral anticoagulant therapy (OAT) during the initiation and maintenance treatment is generally poor. Physicians' ordering of OAT (especially However, few studies have addressed the benefit of personalized reminders versus non personalized reminders, whereas the personalized reminders require more development to access patient record data and integrate with the computerized physician order entry system.The Hospital Information System of George Pompidou European Hospital integrates an electronic medical record, lab test and drugs order entry system. This system allows to evaluate such reminders and to consider their implementation for routine use as well as the continuous evaluation of their impact on medical practice quality indicators.The objective of this study is to evaluate the impact of two types of reminders on overtreatment by oral anticoagulant: a simple reminder of text formatted dose adjustment table and a personalized recommendation for oral anticoagulant dose and next date of INR control, adapted to patient data. Both types of reminders appear to the physician at the moment of drug ordering.fluindione or warfarin, will be included in the study between November 2004 and May 2006.The study is an alternating time series experiment with three 6 months periods, each one including every 2 months according to a Latin square scheme: a control period without any reminder, a period with the simple non personalized reminder, a period with personalized reminder. All patients hospitalized in departments using the computerized physician order entry system and ordered Main outcome will be the proportion of overcoagulation, as expressed by the proportion of observation time with INR over 4.5, assuming INR change linearly. Secondary outcome is the incidence of major haemorrhagic events. Data will be collected thanks to Hospital Information Systems databases.Data will be analyzed taking into account patient and physician clustering effect. According to a study carried out by French pharmacovigilance centres, haemorrhage subsequent to oral anticoagulant treatment (OAT) is the most common drug-related side effect resulting in hospitalisation in public hospitals in France . On the basis of these findings, the AFSSAPS  has made the prevention of iatrogenic effects related to OAT one of its priorities. Many of these events are consequences of interactions between different drugs, resulting in inappropriate doses [Implementation of a system of support when prescriptions are made out is likely to improve prescription practices and to decrease the frequency of side effects. It should be possible to integrate a support tool into the drug prescription system, by using nomograms to adjust OAT doses.et al. [The efficiency of reminders issued at the time of prescription has been demonstrated by a various studies -8. Theseet al.  reviewedet al. . We haveet al.  and in tet al. . ComputeSeveral types of reminders can be issued at the time of prescription :• simple, general information concerning the recommendations that should be taken into account ,• "check list": includes questions or a precise list of practices that the doctor must tick to show that it has been done,• reminders including clinical data concerning a specific patient that must be taken into account for a given procedure .The advantage of personalised reminders over non-personalised reminders has not been demonstrated in the literature. However, the production of personalised reminders necessitates better integration of existing information and is thus more expensive to develop. It is important to determine whether this personalised tool results in a better quality of care than non-personalised tools.Several randomised clinical trials have tried to evaluate decision support systems for the prescription of OAT, but failed to draw any conclusions about their efficiency for several reasons: heterogeneity and complexity of the systems evaluated, experimental designs difficult to apply and not necessarily adapted, and too few patients included -17.Reminders issued at the time of prescription have been shown to be effective by experimental studies, but the difficulties of maintaining the effectiveness of interventions designed to improve clinical practices remains a major problem. We evaluated the effect of an active decision support system for the prescription of low molecular weight heparin as prophylaxis for venal thrombosis in an orthopaedic surgery department. In this study, the system was and was not used during alternate periods. It showed that such programs affect practices without affecting learning . Other aThe hospital information system currently collates prescriptions and results of biological tests and imaging procedures. Eight hundred computers, both laptops and fixed posts, are used to in care procedures .® program is at the centre of care delivery. It is used by doctors and nurses:The Dx-Care• to prescribe laboratory examinations and imaging tests for a patient,• to visualise the results of laboratory tests,• to establish and to consult nursing schedules,• to archive a structured observation,• to prescribe drugs.® is integrated with other applications to allow circulation of information between departments, laboratories and the pharmacy. Prescriptions for laboratory tests are transmitted to the Netlab® program which manages such tests  The laboratories return the results using this same program, which retransmits them to Dx-Care®. Furthermore, prescriptions of drugs are transmitted to the Phedra program, which is used by the pharmacy to manage prescriptions. The prescription is validated by the pharmacy and this validation is then transferred to Dx-Care®. The lab test prescription facility has been available in the hospital information system since 2000 and is used by all departments of the hospital. The drug prescription facility has been implemented later on, since January 2003, and its use is still increasing .Dx-CareThe hospital information system thus allows to install decision support systems that are activated whenever a prescription is issued and routinely to collect evaluation criteria of prescription practices. If possible and validated, the use of the hospital information system to evaluate care procedures will make it possible to collect data regularly, and routinely to assess methods for the improvement of care practices.With the aim of quality of care and preventing risks, the hospital is developing a system, based on the Intranet network, of declaration of undesirable events. This system must be able to record all undesirable events and incidents linked to the use of health products  and care  as well as those due to the patient environment  and the job of health care professionals .When a health care professional decides to report an undesirable event, he or she must complete a dedicated form available on the Intranet with all relevant information. This incident form includes an item entitled "complications associated with anti-coagulants". When the doctor clicks on this item, a form specific to haemorrhagic accidents following anti-coagulant treatment appears (see form in appendix).Among departments which already started to use the computerised drug order entry system, several (cardiology and vascular medicine departments) are heavy "consumers" of anti-thrombosis drugs  in arterial thrombosis and venous thrombosis, and the way in which patients receiving anticoagulants are monitored and handled in cases of overdose. The procedure concerning curative OAT included a nomogram for adjusting doses of 1. To evaluate the effect on the frequency of overanticoagulation of the implementation in the computerised physician order entry system of two types of tool to adjust OAT doses .2. To assess any advantages of using the personalised tool rather than the non-personalised tool.1. To evaluate the frequency of haemorrhagic accidents in the context of the study.2. To evaluate the feasibility of long-term implementation of the intervention.The study is an alternate time series experiment which consists of three successive six-month periods . Each phase will consist of:• a two-month period without active support during which evaluation criteria will be collected (period A),• a two-month period with non-personalised active support (period B),• a two-month period with personalised active support (period C).To limit the impact of a learning effect on appropriate OAT management practice within the department over time , the order of these three periods was determined by using a Latin square plan . This experimental design can be considered valid for an impact study in this context whereas a randomised controlled design is difficult to apply with just one hospital .This study will include all the patients who are prescribed OAT for any indication and are hospitalised in clinical care departments where physicians are using the hospital information system to prescribe drugs.The following table shows the number of INR examinations prescribed by these departments in a three-month period. It provides an estimation of the approximate proportion of overdoses among the INR that exceeded 2, which is supposed to be found in patients treated with OAT in these units.All doctors authorised to prescribe drugs in the participating departments will be included in the study: residents and fellows, registered and non registered university hospital doctors. Each six-month period will coincide with an internship semester.®, the doctor selects the required drug from an exhaustive list. This opens up a dialogue box in which the doctor types the dose, the frequency of intake and the mode of administration. From this window, it is possible to add a text comment or to consult particular protocols that have been defined by the departments.To prescribe a drug using Dx-CareIt is planned to integrate two types of decision support systems into the computerised prescription program:1) non-personalised active system: when the drug is selected a window automatically opens giving the prescribing the nomogram for the adjustment of OAT doses in the form of a table (see Tables 2) personalised active system: when the drug is selected a window automatically opens suggesting a dose recommended according to the nomogram (taking into account the doses previously received by the patient and the patient's INR), together with a date for next INR control and an explication.Proportion of patient observation time with INR results > 4.5, assuming linear change of INRs.Intra-cranial haemorrhage or spontaneous haemorrhage necessitating surgery or a transfusion or decreasing haemoglobin concentration by more than 2 g/dl.® application allows biological laboratories to receive prescriptions and to return results. All of the INR results can be extracted from the Netlab® database accompanied by information making it possible to identify the patient, the treatment and dose received, the prescribing doctor, the hospitalisation unit, the date the test was prescribed. Data about overdoses can therefore be collected systematically by regular database searches.The NetlabFurthermore, the storage of the information in a computerised tool will make it possible to determine previous doses and INR results each time a drug is prescribed.When a health care professional decides to declare an undesirable event, he or she fills in a specific, pre-formatted form available on the Intranet. This form includes a list of events that must be declared at the GPEH.The declaration form includes an item entitled "complication of haemorrhagic accidents". When the doctor clicks on this item, a specific form for the declaration of a haemorrhagic accident associated with anti-coagulant treatment appears (see form in appendix).The determination of the number of participants necessary requires the definition of the statistical unit of interest, information about the incidence of the evaluation criteria in the study population and a hypothesis about the efficiency of the intervention.In this study, the main aims are to guide each prescription and to reduce the number of anti-coagulant overdoses: the simplest statistical unit to study is therefore the INR result. This unit will be used to calculate the sample size.This choice is not, however, perfect and the efficacy results will be presented using other indicators of the quality control of anti-coagulant treatments:Given the low incidence of major haemorrhagic accidents (not currently measured at the GPEH but probably below 1%), it is not possible in this study to estimate the number of subjects necessary to demonstrate an effect of intervention on the "haemorrhagic accident" endpoint. Recording haemorrhagic accidents will give the frequency of such accidents, which will then be used for realistic estimates of power and sample size if further studies are carried out.In previous studies evaluating the efficacy of tools to aid the prescription of OAT, the unit considered was not always the same, taking into account the number of INR per patient and the time between INR measurements to greater or lesser extents. The most recent studies considered the number of patient-days according to the method described by Rosendaal ,16,21. Ta priori lower than inter-physician variability.We may also carry out an analysis for each prescribing doctor given that the intervention targets doctors directly. This will involve adjusting the effect of the intervention to the fact that intra-physician variability is During a six-month period (January to June 2004), 4 920 INRs were requested by the six departments which already routinely use the computerized physician order entry system. The frequency of overtreatment can approximately be estimated from the percentage of INR > 4.5 among INR >2. Among the 2620 INR > 2, 330 (12%) were higher than 4.5 , for a basal incidence of the judgement criterion of 12% are and for the following hypotheses on relative reduction of the risk (RRR) of overdose, are:• RRR 30%: 2500• RRR 40%: 1300• RRR 50%: 800• RRR 60%: 500Carrying out approximately 5000 tests over six months will make it possible to detect an intervention effect of less than 30% in this period. The experimental design includes three six-month periods and should thus ensure adequate power.Statistical analyses will be performed with the STATA statistical software Standard statistical tests will be used to compare the baseline characteristics of the departments and patients.The main analysis concerns the effect of the intervention on the number of dangerously high INRs. The analysis will be carried out using a mixed effect analysis of variance model, in which the effect linked to the period will be considered fixed and that linked to the prescription tool will be considered random .Rosendaal's method will be used to analyse the number of patient-days with INR over the target .According to French policy, this study was exempt from medical ethics committee approval. The anti-coagulants being evaluated are prescribed as recommended by clinical studies validated within the GPEH. These recommendations are available on the hospital's Intranet and are thus accessible to all doctors. They conform to standard practices. Neither the patients nor the doctors will be randomised. The interventions are simply different means of giving valid information to physicians. Using funding from the PHRC , we carried out two research studies related to this project. In the first (PHRC 95), an intervention aimed at modifying the way in which emergency department doctors handle ankle injuries, the study design was a randomised controlled study and the randomisation unit was the hospital . In the OAT: Oral Anticoagulant TherapyINR: International Normalized RatioGPEH: Georges Pompidou European HospitalPHRC: Hospital Clinical Research ProgramIC and PD, conceived, wrote the protocol and prepared the manuscript.GC is the statistical expert and performed the power calculationsGC and ABR revised the protocol and the manuscript.The pre-publication history for this paper can be accessed here:


Over 750 million children have iron-deficiency anemia. A simple powdered sachet may be the key to addressing this global problem A simple powdered sachet may be the key to addressing a global problem Recent World Health Organization (WHO)/United Nations Children's Fund estimates suggest that the number of children with iron-deficiency anaemia (IDA) is greater than 750 million In the developing world, there are three major approaches available to address iron deficiency: dietary diversification so as to include foods rich in absorbable iron, fortification of staple food items (such as wheat flour), and the provision of iron supplements. When dietary or fortification strategies are not logistically or economically feasible, supplementation of individuals and groups at risk is an alternative strategy. For the past 150 years or more, oral ferrous sulphate syrups have been the primary strategy to control IDA in infants and young children Our research group at the Hospital for Sick Children in Toronto conceived the strategy of “home fortification” with “Sprinkles”—single-dose sachets containing micronutrients in a powdered form, which are easily sprinkled onto any foods prepared in the household. We hypothesized that this would be a successful method to deliver iron and other micronutrients to children at risk In Sprinkles, the iron (ferrous fumarate) is encapsulated within a thin lipid layer to prevent the iron from interacting with food. This means that there are minimal changes to the taste, color, or texture of the food upon adding Sprinkles. Other micronutrients including zinc, iodine, vitamins C, D, and A, and folic acid may be added to Sprinkles sachets. Any homemade food can be fortified with the single-dose sachets, hence the term “home fortification”. Two formulations have been developed, a nutritional anaemia formulation  and a coTo investigate the bioavailability of the iron in Sprinkles, we used a dual stable isotope method and showed that anaemic infants absorbed iron from Sprinkles about twice as efficiently as nonanaemic infants when delivered in a maize-based diet in West Africa. The study was conducted in collaboration with the Kintampo Health Research Centre of the Ministry of Health in Accra, Ghana. The geometric mean iron absorption from two doses of iron  was 8.3% range, 2.9%–17.8%) in infants with anaemia and 4.5%  in infants without anaemia %–17.8% iIt has been suggested that zinc may compete with iron for the same receptor sites on intestinal mucosal cells in the proximal duodenum, thereby compromising the absorption of both minerals Over the past five years, we have completed seven community-based trials in four different countries ,17,18,19We further examined this through quantile-quantile plots of haemoglobin concentrations at the end of the studies for Sprinkles and ferrous sulphate drops . The oveDuring our studies we also asked about the caregivers' perception of their infants' responses to Sprinkles as compared to drops, the Sprinkles' impact on the food to which they were added , the use of sachets as a delivery vehicle, and the perceived side effects of Sprinkles ,18,19. IAs the results of the first studies showing the efficacy of Sprinkles became available, the need for a reliable high-quality supply became apparent. In 2000, the H. J. Heinz Company of Pittsburgh, Pennsylvania, United States, expressed an interest in the Sprinkles program as a component of their corporate social responsibility program. Since 2001, the H. J. Heinz Company has provided support and expertise in the evaluation of consumer needs and a supply of Sprinkles for research, while the H. J. Heinz Company Foundation has provided financial support for research activities. Through a formal process of technology transfer, local overseas Sprinkles production has been encouraged. Currently, an independently licensed copacker is supporting local production for a national program in Guyana, and plans are in place for technology transfer to Bangladesh and Pakistan.The final stage, the scale-up process, is by far the most challenging. First, this process involves dialogue with the Ministries of Health, scientific community, civil society, and other private partners. Second, it is important to identify sustainable methods of distribution that are able to reach and provide Sprinkles to the most vulnerable populations in the developing world. From our experience in Mongolia, we have determined that it is feasible to distribute Sprinkles in partnership with a non-governmental organization called World Vision. Sprinkles sachets distributed in Mongolia over a two-year period included both iron and vitamin D. Sprinkles have been successfully distributed by World Vision field staff to over 15,000 children in seven districts. Coverage has been over 80%, at a cost of about US$0.03 per sachet. In the project area, the prevalence of anaemia (haemoglobin < 115 g/l) and rickets decreased from 42% to 24% and 48% to 33%, respectively Notwithstanding these positive results on anaemia control, without committed, long-term financial input from national governments, international agencies, or nongovernmental organizations, sustainability is not guaranteed. Clearly, sustainability over the long term can most likely be achieved if a program becomes self-financing. This may be achieved through public- and private-sector partnerships that use effective social marketing models or possibly through programs which include microcredit in order to reach poorer population groups.When strategizing how to scale up Sprinkles from small-scale research projects to large-scale programs, we quickly realized that our research group did not have the necessary funding, experience, or personnel needed to influence health policy, develop a social marketing strategy, or maintain a distribution network at a countrywide level. We have thus partnered with organizations that specialize in each of these areas to help achieve our goal of sustainable distribution.Shastha Shebika). In both of these countries, Sprinkles would be produced locally through public–private partnerships via a technology transfer agreement. The cost per sachet of locally produced Sprinkles should range from US$0.010 to US$0.015, depending on the volume of production, as compared to US$0.020 to US$0.025 if imported.For example, the government of Pakistan is planning to distribute Sprinkles through their ongoing Lady Health Worker Program, which is the largest public-sector primary health-care program implemented by the Federal Ministry of Health. In Bangladesh, BRAC , the largest national non-governmental organization in the country, is planning to distribute Sprinkles through their ongoing Female Community Health Worker program (popularly known as Each stage in the evolution of the Sprinkles intervention has been evaluated in a controlled manner. We determined that the use of encapsulated iron did not appreciably change the taste or color of the food to which it was added, we showed that the haemoglobin response in anaemic infants was equivalent to the current standard of practice, and we documented the acceptability of Sprinkles among caregivers who used Sprinkles in their homes. Finally, through various partnerships, we have developed a successful model to scale up the intervention for countrywide use. Our challenge for the future is to demonstrate the cost-effectiveness of this new intervention and to advocate for the adoption of Sprinkles in the nutrition policy of developing countries.


Allelic-loss studies record data on the loss of genetic material in tumor tissue relative to normal tissue at various loci along the genome. As the deletion of a tumor suppressor gene can lead to tumor development, one objective of these studies is to determine which, if any, chromosome arms harbor tumor suppressor genes.n0 versus n1. In these cases, frequentist test statistics based on the likelihood ratio statistic have unknown distributions and are therefore not applicable. Our simulation study shows that Bayes factors favor the right model most of the time when tumor suppressor genes are present. When no tumor suppressor genes are present and background allelic-loss varies, the Bayes factors are often inconclusive, although this results in a markedly reduced false-positive rate compared to that of standard frequentist approaches. Application of our methods to three data sets of esophageal adenocarcinomas yields interesting differences from those results previously published.We propose a large class of mixture models for describing the data, and we suggest using Bayes factors to select a reasonable model from the class in order to classify the chromosome arms. Bayes factors are especially useful in the case of testing that the number of components in a mixture model is Our results indicate that Bayes factors are useful for analyzing allelic-loss data. The goal of studies of allelic loss is to determine those loci in tumor tissue where genetic material has been lost. A tumor suppressor gene (TSG) is much more likely to lie on a chromosome arm where there has been significant allelic loss than elsewhere ,2. The sEsophageal adenocarcinoma is a form of cancer involving the cells along the lining of the esophagus. The cause of esophageal adenocarcinoma is not well understood. The incidence of this cancer has been increasing rapidly. In fact, it is one of the fastest growing cancers in the United States over the past 20 years ,3,4. A sWe examine three data sets of allelic-loss on esophageal adenocarcinomas that attempt to identify the tumor suppressor genes (TSGs) involved in the development of this disease. These data sets have been previously analyzed and published. We refer to each data set by the last name of the first author of the publication. Some of the data sets record allelic loss on multiple loci per chromosome arm for some of the arms. However, because the number of loci evaluated per chromosome arm is not random , we consider only one locus per chromosome arm. In these cases, we choose data from the most informative locus for that chromosome arm.Our general approach to analyzing allelic-loss data can be described in two main steps. The first step is to choose an appropriate model for the data using Bayes factors. The second step is to classify the chromosome arms as harboring TSGs or not according to the selected model. The details involved in these two steps are described below.A natural way to model allelic-loss data is in terms of a mixture of two distributions: one distribution corresponds to chromosome arms that harbor TSGs and the other corresponds to arms that do not. It is reasonable to expect considerable variability in the loss rates of arms that harbor TSGs due to the existence of multiple pathways leading to the same tumor type . For exaXi be the number of tumors with allelic-loss for the ith chromosome arm, and let ni be the number of informative tumors for the ith chromosome arm, for i = 1, 2,...,N, where N is the number of chromosome arms in the study. The density function for Xi is written as follows:We propose a class of mixture models that account for the variation inherent in this type of data. Specifically, the class of models we propose is a mixture of two beta-binomial distributions. Let θ ≡  is a vector of unknown parameters, η is the mixing probability, πj is the average loss rate, and ωj is the dispersion parameter for j = 0,1.where ω0 → 0 and ω1 → 0). If only one of the dispersion parameters goes to 0 (ω0 → 0 or ω1 → 0), the distribution reduces to a mixture of a beta-binomial and a binomial distribution. Note that the model has only one component when the mixing parameter is zero (η = 0).The distribution converges to a mixture of two binomial distributions as both dispersion parameters go to 0 /Pr(X|H0). Thus, as Bayes factors are proportional to the posterior odds of one model to another, they are desirable measures to use for model selection. Note that if the prior odds are assumed to be 1, then the Bayes factor is equivalent to the posterior odds.Equation (1) shows that the posterior odds is calculated as the product of a term known as the Bayes factor and the prior odds. The Bayes factor is the marginal likelihood of the data under One can think of the Bayes factor as a Bayesian likelihood ratio statistic. Like the likelihood ratio statistic, the Bayes factor is a ratio of likelihoods under two models being considered. However, while the likelihood ratio statistic is the ratio of two maximized likelihoods for two competing, nested models, the Bayes factor is the ratio of two likelihoods integrated or averaged over the entire parameter space and the models need not be nested. An important consideration with a Bayesian approach is that a prior distribution is assumed for all of the parameters in the model. The advantage to this is that one can incorporate prior information into determining which model is more appropriate. This is a disadvantage, however, if the Bayes factor is sensitive to the prior and if the prior has been chosen incorrectly.lnB10 > 2 implies positive evidence in favor of the alternative model.Large Bayes factors are evidence in favor of the alternative hypothesis. Kass and Raftery (1995) discuss guidelines for interpreting the measure . FollowiComparing a uni-component model to a two-component model would address the question of whether there is one versus two groups of chromosome arms. Further, comparing a two-component beta-binomial model to a two-component binomial model would address whether there is overdispersion in either group. The advantage of this is that it provides insight into the number of chromosome arm groups, whereas standard applicable frequentist tests will only indicate whether there is one or more groups ,9.Xi ~ ηf1 + (1 - η)f0, then it can be shown using Bayes' rule thatProvided there is sufficient evidence to indicate that there are two groups of chromosome arms, it is desirable to identify which chromosome arms belong in which group. Classification of the chromosome arms can be done by calculating the conditional probability of group membership of each arm under a given model. If  is the maximum likelihood estimate (MLE) of θ, Zi is the group membership of the ith chromosome arm and Zi = 1 implies that the ith chromosome arm is in the TSG group. For the analyses here, chromosome arms with conditional probabilities exceeding 0.5 are classified in the TSG group. Also note that MLEs are computed using the nlminb function in S-Plus.where Table ln(Bayes factor) for data generated under each of the scenarios described in Table H1 (models appearing in the numerator of the Bayes factor). The columns of the matrix correspond to models considered under H0 (models appearing in the denominator of the Bayes factor).Table For data generated from a two-component binomial model (Scenario 1), the true model is mostly favored over the uni-component models. In fact, when comparing the true model to a uni-component beta-binomial model, the latter model is only favored 5% of the time. This can be viewed as a false-negative rate. Note that the Bayes factors never provide evidence in favor of a uni-component model in comparisons with either of the other two-component models for data from this scenario. Furthermore, the true model is selected 75% of the time over the two-component beta-binomial model. The Bayes factors are ambiguous, however, when comparing the true model to a two-component beta-binomial/binomial model, where neither is favored 69% of the time.For data that follow a uni-component beta-binomial distribution (Scenario 2), the results are inconclusive 62% of the time when comparing the true model to the two-component binomial model. For twenty-two percent of the data sets the right model is favored, but 16% of the time, the two-component model is selected. Thus, this comparison results in a 16% false-positive rate. Similar results are found when comparing the true model to a two-component beta-binomial/binomial model. The Bayes factors favor the correct model over the two-component beta-binomial model roughly half the time and favor neither model the other half. Comparisons between the two-component models and the one-component binomial model not surprisingly show a strong preference for the two-component models, as they better accommodate the variability of the data.The third quarter of Table For data generated under Scenario 4, we expect the two-component beta-binomial model to be chosen over the other models in the class as this model is closest to the truth. The results show that when this model is compared to the two-component binomial or the one-component beta-binomial, it is mostly favored, and these models are never selected. As the two-component beta-binomial model is fairly similar to the two-component beta-binomial/binomial model, however, most of the time neither model is chosen over the other. The two-component beta-binomial is favored only 35% of the time, while the two-component beta-binomial/binomial is favored 9% of the time. Interestingly, when comparing the one-component beta-binomial to the two-component binomial, the one-component model is chosen 72% of the time and the two-component binomial model is chosen only 5% of the time. This suggests that the measure is fairly sensitive to the overdispersion in the two groups. Another example of this is a comparison between the two-component beta-binomial/binomial model and the one-component beta-binomial model. In this case, the two-component model is only favored 54% of the time, where the uni-component model is a better fit to 5% of the data sets, and both models are equally good fits to the data 41% of the time.This simulation study demonstrates that the Bayes factors are an appropriate method of model selection. They perform particularly well for data generated from the two-component models. In particular, most of the time, the correct model is chosen, and furthermore, reasonable false-negative rates are observed for comparisons made on data generated from the two-component binomial model as well as the two-component beta-binomial/binomial model. Data generated from a one-component beta-binomial model produces interesting results. Although the false-positive rates are reasonable when comparing the one-component beta-binomial model to the other two-component models , there is a large percentage of time, when neither model is favored . Since both models are often good fits to the data, it would be difficult to decide with confidence whether or not there is a second group of arms in these cases.In this section, we apply the methods discussed to three allelic-loss data sets. Specifically, we use Bayes factors to choose a reasonable model or set of models for the data in order to address whether TSGs exist on any of the chromosome arms, and we classify the chromosome arms as harboring TSGs or not based on the selected model(s).ln(Bayes factors) exceeding 2 when compared to models outside the set and with 2ln(Bayes factors) less than 2 when compared to models within the set. Details of the analysis for each data set are described below, with slightly more emphasis placed on the first data set.Table The Barrett data set records allelic loss on 20 esophageal adenocarcinomas and two high-grade dysplasias. Figure ln(Bayes factors) for the pairwise comparisons of the models for each of the three data sets. In addition, the posterior probability of each model is presented assuming a prior probability for the models such thatTable P(2 Component Model) = P(1 Component Model) = 1/2This givesP(2 bb) = P(2 bb/bin) = P(2 bin) = 1/6P(1 bb) = P(1 bin) = 1/4.For the Barrett data set, the two-component models are strongly favored over the one-component models, clearly indicating a group of arms that exhibit higher than background loss rates. In particular, the Bayes factors demonstrate that the two-component beta-binomial/binomial model provides the best fit. Note that the posterior probability of this model is considerably higher than that of the others, providing further evidence of its superiority. = 0, reducing the two-component beta-binomial model to a two-component beta-binomial/binomial model. The parameter estimates for these two models are identical and imply that the beta-binomial distribution corresponds to the TSG loss and the binomial distribution corresponds to the background loss. The estimate of the probability that a chromosome arm is in the TSG group is 0.097. The estimated background loss rate is 0.228, and the expected background loss rate for arms with TSGs is estimated at 0.708 with a loss rate variance of 0.07. The fit from the two-component binomial model gives a slightly lower mixing parameter estimate and a slightly higher estimate of the TSG loss rate.Table The conditional probabilities of group membership based on the two-component beta-binomial/binomial model yield the same classification rule as that based on the other two-component models. Chromosome arms 5q, 9p, and 17p are classified in the TSG group. The conditional probabilities of group membership for these chromosome arms are quite similar across the three models.The Gleeson data set consists of 38 esophageal adenocarcinomas. Allelic-loss data were recorded on 39 chromosome arms . A histogram of the proportion of tumors with allelic loss is presented in Figure For the Gleeson data set, the two-component beta-binomial/binomial model, the two-component binomial model and the uni-component beta-binomial model are all favored over the two-component beta-binomial model and the uni-component binomial model  exhibit lower than the average background loss rate in the Barrett data set. However, 9p and 17p are categorized along with 5q in the TSG group. Furthermore, although not classified in the TSG group, chromosome arm 18q exhibits the fourth highest allelic-loss rate in the Barrett data set.The Hammoud data set consists of 30 esophageal adenocarcinomas on 39 chromosome arms (the same arms included in the Gleeson data set). A histogram of the Hammoud data set is presented in Figure The pairwise comparisons using the Bayes factors for the Hammoud data set , the authors consider a uni-component binomial distribution for the background loss [996, the The analytic approach employed by Gleeson et al. 1997) is to select a chromosome arm with a corresponding allelic-loss rate above an arbitrarily chosen cut-off of 50% as criterion for potentially harboring a TSG . With th997 is toResults from the Bayes factors for the Gleeson data set are not completely clear. They cast doubt on whether the true underlying distribution really has two components or whether the two-component models chosen also provide a reasonable fit  to overdispersed data exhibiting only background loss. Recall the simulation study where we demonstrate that for data arising from a uni-component beta-binomial model, the Bayes factors indicate that both the true model and the two-component binomial model are often both reasonable fits to the data. This motivates incorporating Bayesian model averaging (BMA) into the inference process . An alteP(Hj|X). For example, suppose chromosome arm 13q is suspected of harboring a TSG from past experiments and we desire a probability that Zq 13= 1 based on these data. Because of model uncertainty we may be hesitant to compute the probability based solely on one model. Instead, we could estimate this probability as:Furthermore, one could use Bayesian model averaging when estimating the conditional probability of group membership for each of the chromosome arms. Maximum likelihood estimates from different high probability models could lead to different inferences about parameters. Thus, this approach of averaging the conditional probability over the various models to classify the arms or weighting the parameter estimates by the posterior probability of a given model may be more desirable than choosing a single best model from which to make inference. Specifically, one could weight estimates by j indexes over all of the models considered. This is a potential alternative to classifying the chromosome arms using the classical maximum likelihood approach that needs to be further explored. It is interesting to note that the two-component beta-binomial mixture model was never chosen for any of the data sets. Although it was certainly favored over the one-component binomial model in all data sets and over the uni-component beta-binomial model in the Barrett data set, it was never chosen to be in the set of candidate models. The class of models considered here is based on our beliefs of the biology of the data. However, the ability to screen the tumor cell genome for chromosome arms which harbor TSGs lies in a better understanding of the background distribution. Characterizing the background distribution would allow a more definitive identification of arms exhibiting abnormal loss.where The three data sets to which we apply our methods were previously published and analyzed using other techniques ,4,11.Computing Bayes factors can be challenging as non-trivial integration is often required to estimate the marginal probabilities under each model considered. Specifically, calculating Bayes factors involves integrating the likelihood over the entire parameter space for each model considered. Thus, the integrals tend to be high-dimensional. In general, we need to computeI = ∫ Prπ(λ|H)dλ.This can be quite computationally intensive. When the integral is of high dimension (> 6), quadrature methods can be unreliable . In addiπ(λ). The simple Monte Carlo estimate of the integral is the averaged likelihood at the sampled parameter values orAnother method of estimating integrals is simple Monte Carlo, that involves sampling from the prior distribution, π*(λ), the importance sampling function [This has been shown to be a good estimate for likelihoods that are relatively flat. However, if the posterior is concentrated relative to the prior, the variance of the estimate will be large, and convergence to a Gaussian will be slow . Thus, sfunction ,14. The  is known as the importance sampling ratio. The simple Monte Carlo estimate is a special case of importance sampling where π*(·) is chosen to be the prior distribution. However, the importance sampling estimate can be an improvement over the simple Monte Carlo estimate if π*(·) is chosen such that the sampling is more efficient, e.g., if π*(·) is centered around the mass. There has been some success with importance sampling in a non-mixture model setting [where  setting .Our solution is to first write the likelihood in its complete-data form. The likelihood for the mixture of two beta-binomial distributions is written as follows:z = T and the zis are unobserved group membership indicators such that zi = 0 if xi is from the background component and zi = 1 if xi is from the TSG component. Then the marginal probability of X becomeswhere I denotes the marginal probability of the data (or integrated likelihood) and where g is the prior distribution of θ.where Z. The idea behind the method is to use P where  is the MLE of θ to provide information on the important groupings, i.e., which chromosome arms are likely to be clustered together. While the membership vectors are sampled independently, the membership values within a group are sampled dependently, making these groupings more likely to be maintained than if the values were sampled independently.We then estimate this integral using a method we developed called the Uniform Distance Method (UDM). This method is a variant on importance sampling and involves a combination of either quadrature or exact integration and sampling of the membership vectors, The development and assessment of UDM is discussed in detail in Desai (2000) and demonstrates solid performance in estimating these integrals . SoftwarTSG, tumor suppressor gene; MLE, maximum likelihood estimate; 2 bb, two-component beta-binomial model; 2 bb/bin, two-component beta-binomial/binomial model; 2 bin, two-component binomial model; 1 bb, uni-coniponent beta-binomial model; 1 bin, uni-component binomial model; BMA, Bayesian model averaging; UDM, uniform distance methodBoth MD and MJE contributed substantially to the development of the models and the methodology. MD performed the simulation study and analysis of the three data sets. Both authors have read and approved the final version of the manuscript.