This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient costs. However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples. In particular, the requirement that the number of clusters randomised should be large would not be satisfied in many cluster RCTs performed to date.The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted bootstrap procedures were compared in terms of confidence interval coverage in a large number of simulations. Parameters varied included the intracluster correlation coefficient, the sample size and the distributions used to generate the data.The bootstrap's advantage in dealing with skewed data was found to be outweighed by its poor confidence interval coverage when the number of clusters was at the level frequently found in cluster RCTs in practice. Simulations showed that confidence intervals based on robust methods of standard error estimation achieved coverage rates between 93.5% and 94.8% for a 95% nominal level whereas those for the bootstrap ranged between 86.4% and 93.8%.In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of clusters and extremely skewed data would be necessary for the bootstrap to be considered in favour of the robust method. There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately. The greater complexity of cluster randomised controlled trials (RCTs) compared with their individually randomised counterparts has led to much methodological work concerning their design and analysis. Howevert-test are fairly robust to moderate violations of the assumptions of normality and homogeneity of variances [with replacement from this 'population' and calculation of a parameter of interest builds up a picture, the 'empirical distribution' of this parameter, based on so-called 'B bootstrap estimates' of the parameter of interest. This can be used to construct directly the required confidence interval by, for instance, reading off the 2.5% and 97.5% percentiles of the distribution. Bootstrapped confidence intervals may, therefore, be asymmetric and be better able to deal with skewed data.The first is that many trials randomise only a small number of clusters. This can sometimes produce inadequate randomisations where, for example, all clusters with a characteristic related to outcome are allocated to one treatment arm . It may ariances . The sitariances , first pariances . This reThe bootstrap approach is flexible but does assume that the data are independently and identically distributed ,8. When single outcome model. This single outcome model was primarily conceptualised as a model for cost data and the term cost is therefore used as short-hand. However, some of the scenarios presented may be equally applicable to clinical data.This paper details the results of simulation studies to evaluate how clustered cost data might be analysed. Small numbers of clusters together with skewed data were utilised to ascertain how the bootstrap performed against a method of analysis commonly used in clustered clinical data. Thus it details the generation and analysis of a , which was considered acceptable. Over 20,000 simulations per parameter set, then, the following were noted:The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted non-parametric bootstrap procedures were compared in terms of confidence interval coverage. Specifically, the comparison was of the percentage of 20,000 simulations for which the estimated 95% confidence interval contained the true value of the treatment effect. If the observed coverage were to be 95% on average across the simulations, then 20,000 simulations would from simple binomial theory give a margin of error of approximately 1. The mean value  of the estimated treatment effect and the confidence limits under each of the three procedures,and whose lower/upper limit was greater/less than this true value,2. The percentages of simulations for which the estimated confidence interval did not contain the true value of the treatment effect (zero) 3. The percentage of simulations for which the estimated confidence interval contained the true value for the treatment effect. This figure was simply 100 minus the sum of the two percentages in 2 above.Thus ideally the two figures in 2 should each be 2.5% whilst that for 3 should be 95% . It was decided to split observed non-coverage rates according to whether there was spurious positive treatment effect or a spurious negative one because skewed distributions were expected to have different implications for each of these.n individuals in each of 2k clusters. Half of these were randomised to a hypothesised intervention group, whilst the other half were randomised to a control group. A random effects model incorporating a treatment dummy variable was used:The data were constructed by assuming there were Ehij = αhi + βTh + εhij     h = 0,1; i = 1,...k;j = 1,...n;E(αhi) = 0; E(εhij) = 0jth individual in the ith cluster was randomised to receive treatment h. Each individual's outcome, Ehij, comprised three elements: the effect of treatment, βTh, the cluster-specific effect, αhi, and the individual-specific effect, εhij. In order to inform the values of these parameters, it was necessary to undertake some exposition of how cost data might be distributed in a cluster RCT.Thus the In attempting to construct realistic scenarios for cost data from a cluster RCT, three main factors were considered:1. What is the nature of the distribution of individual patient costs expected to be in the population of patients normally eligible for treatment?2. How representative of this population distribution are the cost distributions within clusters likely to be? This has implications for the ICC and the assumptions regarding the distributions.3. How might the introduction of an intervention affect 2 above?As detailed previously, the existence of one unrepresentative cluster  in one arm may affect the ICC, independently of treatment, or the treatment could directly change the ICC and the distribution .This section formally sets out the parameters which, when varied in the simulation model, attempted to capture two potential extreme scenarios as well as situations in between. Under the first scenario, all clusters are equally representative of the population, leading to a high degree of skewness within most, if not all, clusters. Under such a scenario, the ICC is likely to be small and results would be expected to be robust to any incorrect assumptions made regarding the between-cluster distribution. Under the second scenario, within-cluster costs are expected to be more homogenous and much of the skewness in the cost data at the population level is attributable to differences in the cluster mean costs. As a result, the ICC would be expected to be much larger, and the between-cluster distribution might be expected to exhibit considerable skewness. Six factors were varied:1. The between-cluster distributions,2. The within-cluster distributions,3. The ICC in the control group,4. The ICC in the intervention group,5. The number of clusters in each intervention arm,6. The number of individuals in each cluster.There were two distributional assumptions used for the cluster means. The first was the normal distribution and the second was the lognormal distribution, which, for a given mean and variance, exhibits higher kurtosis and skewness than the gamma distribution, the main alternative skewed distribution.For each of the ICC combinations given below, there were three possible combinations of the between-cluster distributions:1. The cluster means in both the control and intervention groups were normally distributed2. The cluster means in both the control and intervention groups were lognormally distributed3. The cluster means in the  control group were normally distributed, whilst in the  intervention group they were lognormally distributed.The same distribution was used for individual level data in both treatment arms in order not to further increase the number of parameter combinations possible. Lognormal data were used in order to provide a scenario applicable to individual level cost data.Given that the likely range of cost ICCs is largely unknown, values between zero and 0.5 were used. While high, the upper value is consistent with the one published source of cost ICCs . The ICCFor a given value of the ICC in the control group, the intervention ICC can remain the same or it can change in a number of ways. In particular, the intervention ICC:1. Remained the same as the control ICC,2. Doubled, as a result of an appropriate increase in the between-cluster variance,3. Doubled, as a result of an appropriate decrease in the within-cluster variance,4. Halved, as a result of an appropriate decrease in the between-cluster variance,5. Halved, as a result of an appropriate increase in the within-cluster variance.Although it is only the between-cluster variance or the within-cluster variance that changes at any one time (not both), the changes involved are large ones. Hence these extreme scenarios should cover a range of findings.The number of clusters in each group was 6, 12 or 24. These figures reflect the small numbers of clusters recruited in many cluster RCTs and, coupled with the cluster sizes given below, they allowed alternative combinations of cluster size and number of clusters to be investigated for a given total trial size.The cluster size was 25, 50 or 100. Mean cluster sizes between 50 and 100 are not unusual in health services research trials, but there is enormous variation in cluster size, depending upon treatment area and type of cluster[k clusters with replacement from the treatment group and selecting k clusters with replacement from the control group – in other words, resampling of clusters was stratified by intervention group. Under method 1 all individuals within a resampled cluster were then selected. Under method 2 a second level of bootstrap was performed on individuals within clusters selected at level one. The difference between the two randomisation group means was then calculated for each method. This was repeated to give 1000 bootstrap estimates (estimates performed on the resampled data) of the treatment effect. A bias-corrected and accelerated (BCa) confidence interval was then estimated at the same nominal 95% level as for the robust method [a method, the resulting confidence interval need not be symmetric. The bootstrap methods are described in more detail below.The first method utilises a standard procedure, where 'standard' has been taken to mean a 95% confidence interval quoted for continuous data in packages such as Stata , utilisit method . Given tyij, such thatUnder this procedure (BS1), clusters are bootstrapped and each resampled cluster is kept intact. This method is utilised by Stata  when theyij = αi + εij     i = 1,...k;j = 1,...n.αis are sampled randomly from the distribution Fα and the εijs are independently sampled randomly from the distribution Fε.The αi) = 0;     (1)E(It can be shown that for the bootstrap estimates (with superscript asterisks):When compared with (2) and (3) it should be noted that the expected variance and covariance of the resampled outcome data are slightly biased downwards. However, an estimator such as the sample mean is strongly consistent ; the level of bias is small unless the number of clusters becomes very small. (see page 102 of their book):An alternative method (BS2) involving resampling individuals as well as resampling whole clusters was also considered. This uses a first stage bootstrap applied to the estimated cluster means (sampling with replacement). The second stage, in which individuals are bootstrapped, involves resampling the deviations from the estimated cluster means. However, the estimated cluster means incorporate both within and between-cluster variability and any analysis that restricts itself to the cluster means will over-estimate the variance in these means . By incowhere c is given by; if the right hand side is negative, it is reset to zero., is then .The variance of the adjusted cluster means, The deviations from the estimated cluster means were also standardised toFinally, for all resampled clusters, the 'shrunken' mean is added to the standardised deviation for each resampled individual:This method, with the rescaling procedures will in future be referred to as the BS2 method, or double bootstrap.The primary focus of this work was on confidence interval coverage and rejection rates; the estimated confidence limits are, therefore, not presented but general inferences regarding the relative width of various confidence intervals can be made easily from the coverage and rejection rates presented below.As a summary, Tables Issues specific to variance or distribution combinations are presented below. In the three tables, within each box the first number represents the coverage of the robust confidence interval, the second represents the coverage of the BS1 method whilst the third figure represents that of the BS2 method. A number of results are immediately apparent:• All three methods produce coverage of less than 95%, the nominal level,• All three methods appear to be consistent with respect to the impact of the number of clusters per arm. In other words, as the number of clusters per arm increases, the observed coverage approaches 95% for each method,• The coverage of the robust method is within approximately 1.5% of 95%,• The BS1 method is always outperformed by the other two methods. In other words the other two methods always achieve coverage that is closer to 95%,• The BS2 method performs much better than the BS1 method but never as well as the robust method,• As the ICC in the control group increases  the robust method performs slightly worse whilst the performances of the bootstrap methods are noticeably poorer,• When examining numbers along the diagonal in each figure , the bootstrap methods perform much better for a large number of clusters and small cluster size compared with vice-versa. This is probably due to the slight downward bias in the second moments; the degree of bias is an inverse function of the number of clusters.From the results in these tables there does not appear to be much to commend the bootstrap, since both bootstrap methods are always outperformed by the robust method. Moreover, when examining the confidence interval coverage results split by distribution and variance combination (results not shown), there was only one parameter combination for which the robust method was outperformed and this was by an amount that was consistent with Monte Carlo sampling error. However, the rejection rates for confidence intervals were split to ascertain if any method was noticeably better at taking account of the skewness in the data.When examining the rejection rates for six clusters of size 25 (results not shown), those of both bootstrap methods virtually always exceeded those under the robust method. The exceptions were for a control ICC of 0.01 when the within-cluster variance increased as a result of the intervention for two of the distributional combinations. For the third distributional combination the rejection rates were the same. Since the individual level data were always lognormally distributed , an increase in the within-cluster variance for lognormally distributed data is likely to have a large effect on skewness, provided the ICC is not too large (which would dilute the effect of within-cluster factors).Thus, if the desired criterion is ability to match the nominal 2.5% rejection rate in any given direction, there are occasions when one or both bootstrap methods outperform the robust method in terms of the proportion of lower rejections. In particular, this appeared to happen when the ICC was moderate to large, together with an extremely skewed distribution of the treatment effect, typically achieved by a large change in the ICC and something other than a normal, normal  distribution combination. Under these circumstances the distribution of the treatment effect is most skewed, since the cluster means in the intervention group are exhibiting large skewness by way of a lognormal distribution whose  variance has doubled. However, this must be balanced against the poor performance of the bootstrap methods in terms of the proportion of upper rejections, which tended to be particularly high compared with those from the robust method.With a cluster size of 50 or 100 (results not shown), similar results were seen to those above, in that a large difference in the absolute value of the ICC together with extremely skewed distributions were required for the double bootstrap to achieve a rejection rate closer to the nominal level than the robust method. In particular, increases in the between-cluster variance accompanied by skewed distributions at the between-cluster level typically caused the double bootstrap to give better lower rejection rates.For 12 clusters of size 25 per arm (results not shown), all three methods performed better than for an equivalent sample size with fewer clusters per arm (6 clusters of size 50). There were very few instances in which the rejection rate exceeded that for 6 clusters of size 50. Even the BS1 method appeared to perform more consistently than when there were only six clusters per arm. Although it was still always outperformed by the BS2 and robust methods, its maximum rejection rate was 10.26%, compared with 14.24%. As before, when the ICC was very small , the double bootstrap method typically only outperformed the robust method when the within-cluster variance increased. In addition, for larger values of the ICC, changes in the between-cluster variance or the within-cluster variance could result in the double bootstrap outperforming the robust method, confirming the results obtained for six clusters of size 100. Lastly, normal distributions at the between-cluster level in both arms were sufficient to ensure that the robust method always performed better than the bootstrap.All the trends identified in previous sections were replicated for 24 clusters of size 25  data. Whilst this is of interest, policymakers are more often interested in cost-effectiveness data. These have usually involved ratio statistics, which often cause major problems for traditional estimators. Future work should, therefore, investigate how well the cluster bootstrap deals with cost-effectiveness data and hence address the question of whether the disadvantages of the cluster bootstrap identified here are outweighed by its potential advantages in dealing with ratio statistics.The author(s) declare that they have no competing interests.TNF conceived of the study, wrote and conducted the simulation programs and drafted the manuscript.TJP participated in the design and coordination of the study, read, critically reviewed and contributed to drafts of the manuscript.The pre-publication history for this paper can be accessed here:


Austrofundulus limnaeus). In stenothermal Antarctic notothenioid fish, losses in capacity for temperature-mediated gene expression, including the absence of a heat-shock response, may reduce the abilities of these species to acclimate to increased temperatures. Differences among species in thermal tolerance limits and in the capacities to adjust these limits may determine how organisms are affected by climate change.Temperature-adaptive physiological variation plays important roles in latitudinal biogeographic patterning and in setting vertical distributions along subtidal-to-intertidal gradients in coastal marine ecosystems. Comparisons of congeneric marine invertebrates reveal that the most warm-adapted species may live closer to their thermal tolerance limits and have lower abilities to increase heat tolerance through acclimation than more cold-adapted species. In crabs and snails, heart function may be of critical importance in establishing thermal tolerance limits. Temperature-mediated shifts in gene expression may be critical in thermal acclimation. Transcriptional changes, monitored using cDNA microarrays, have been shown to differ between steady-state thermal acclimation and diurnal temperature cycling in a eurythermal teleost fish ( Understanding the roles played by physiological adaptation to temperature in governing the distribution patterns of species has taken on a new urgency because of the potential effects of global warming on both aquatic and terrestrial ecosystems. There is already compelling evidence for widespread changes in ecosystems due to climate change. Recent meta-analyses have shown that species' distribution patterns, the structures of ecosystems, and the timing of annual events (phenology) such as migration and reproduction have changed markedly during the past century, when average global temperatures rose by approximately 0.6°C -4. If thIn this brief review, I discuss studies performed with a taxonomically varied group of aquatic ectotherms that shed light on several questions related to the roles played by physiological adaptations in setting species' distribution patterns. The more clearly we understand the mechanistic basis of biogeography, the better prepared we will be to predict the effects of climate change on distribution patterns and ecosystem structure. The questions I address include the following. First, how do thermal tolerance limits differ among species adapted to different temperatures? Which species are most likely to be threatened by increases in habitat temperatures, i.e., which species currently live closest to their thermal tolerance limits? Second, what are the "weak links" in physiological systems that appear most likely to set thermal tolerance limits? Can we identify physiological, biochemical, and molecular level effects that account for thermal tolerance ranges? Third, how do capacities for acclimation to changes in habitat temperature differ among species? Which species have the greatest – or the least – ability to acclimate to increases in temperature? Mechanistically speaking, what types of acclimatory changes are needed to adapt physiological systems to permit tolerance of new thermal conditions? Fourth, what types of changes in gene expression are needed to achieve thermal acclimation? How does gene expression during acclimation to rapid, diurnal change in temperature differ from the response to slower, seasonal time-scale change in temperature? How do stenothermal and eurythermal species differ in their capacities for altering gene expression in temperature-adaptive manners? Have extreme stenotherms like Antarctic animals that have evolved for many millions of years under highly stable, cold temperatures lost key abilities to acclimate to increasing temperatures?To address these questions, I focus primarily on studies that our laboratory has performed on aquatic ectothermic species that have been chosen for study because they seemed to fit well the August Krogh Principle, which can be paraphrased as follows: For any question a biologist asks, Nature can provide a most appropriate study organism. Through study of such "Kroghian" species, it has been possible to obtain at least initial answers to the several questions raised above.Petrolisthes) from the eastern Pacific Ocean [Petrolisthes are abundant (46 species occur in the eastern Pacific), found over wide ranges of latitude, and occur in subtidal and intertidal habitats. Among these congeners, body temperatures extend from slightly below 0°C to over 40°C; for some eurythermal species, the range of body temperatures across different seasons can be over 32°C [One of the challenges facing a comparative or evolutionary physiologist wishing to elucidate adaptive differences among species is the need to separate effects due to phylogeny from true adaptive variation. One means of achieving this end is to study groups of closely related organisms from widely different habitat conditions, for which a comprehensive phylogeny exists. A group of species that is especially "Kroghian" from this standpoint are porcelain crabs  in Fig. Petrolisthes to increase thermal tolerance through acclimation to increased temperature are less in intertidal species than in more cold-adapted subtidal congeners [The thermal tolerance limits of field-acclimatized temperate , subtropical  and tropical (Panama) congeners of habitats . The preund Fig. : the mosongeners . Thus, wversus reciprocal temperature (K)) occurs. Once the ABT is exceeded, heart function of porcelain crabs does not recover from heat stress. ABT values for the temperate congeners P. cinctipes  and P. eriomerus  are 31.5°C and 26.5°C, respectively. The maximal habitat temperatures for the two species are approximately 31°C and 16°C, respectively [50, and ABT for heart function are essentially the same, 31–32°C. It bears emphasizing that ABT values for other physiological processes, for example, mitochondrial respiration [One physiological system that is a "weak link" in the thermal tolerance of these species is heart function. As shown in Fig. ectively ,10. Thuspiration ,12 and epiration , may be 50  of P. eriomerus rose by 2.3°C when acclimation temperature was increased by 10°C, but the most warm-adapted species, P. gracilis, increased ABT (CTmax) by only 0.5°C. The eurythermal intertidal species did exhibit greater capacities than the subtidal species to acclimate to lower temperatures, however, as shown by their greater ability to lower the temperature at which heart beat ceased (CTmin)  Fig. . DespiteTegula [Tegula congeners exhibit significant differences in thermal limits of protein synthesis and onset temperatures for production of heat-shock proteins that reflect their vertical distributions [Tegula congeners had significantly different thermal limits of heart function: the low- to mid-intertidal congener, T. funebralis, had a higher ABT of heart function (31.0°C) than two subtidal congeners, T. brunnea and T. montereyi  [T. funebralis approximated the highest body temperature recorded for this species in the field, 32°C [T. funebralis had a lower ability to increase ABT during warm acclimation than the subtidal species [The differences in thermal tolerance and capacities for warm acclimation noted with porcelain crabs were mirrored in studies done with another set of congeneric marine invertebrates found at different vertical positions along the subtidal to intertidal gradient, turban snails of the genus Tegula . Intertiibutions ,16. In actively) . The ABTld, 32°C . Body teld, 32°C , so thesld, 32°C , T. fune species .Petrolisthes eriomerus has an upper lethal temperature approximately 6°C below the upper limit of body temperature recorded for the intertidal species P. cinctipes. Interspecific variations in LT50 and ABT values also mirror the biogeographic patterning found across latitude. For instance, north temperate congeners of Petrolisthes would be unable to survive under the habitat conditions found in the northern Gulf of California or Panama. The close agreement among LT50, ABT, and maximal habitat temperatures for intertidal species suggests that further increases in habitat temperature could have strong impacts on the persistence of these species in their habitats, especially when the limits of acclimatory ability are reached. Because the greatest thermal stress occurs during emersion periods during the warmest time of the day, and because solar heating is more critical than ambient water temperature, the precise consequences of global warming are difficult to predict. Furthermore, because of latitudinal variation in the timing of tidal cycles, these and other intertidal species may face greater threats from climate change at mid-latitudes than at lower latitudes [These interspecific differences in thermal tolerance limits for whole organism survival and maintenance of heart function provide important lessons concerning biogeographic and vertical patterning and the potential effects of climate change. First, the differences in thermal tolerance between subtidal and intertidal species indicate that the former species would be unable to persist at the upper habitat temperatures commonly found in the intertidal zone during daytime low tides in warm seasons. For example, the temperate subtidal species atitudes . DespiteThe abilities of ectotherms to cope satisfactorily with increases in habitat temperature are based on several factors, including the proximity of habitat temperatures to the edges of the thermal tolerance range and the abilities to shift the tolerance range through acclimatization. In the case of porcelain crabs and turban snails, acclimatory ability was shown to vary among species. This finding that even closely related congeneric species differ in acclimatory ability raises questions about the mechanistic basis of phenotypic plasticity and the roles of evolutionary thermal history in establishing this plasticity.The ability to modify the phenotype in response to a change in body temperature is certain to depend strongly on a capacity for modulating gene expression in an adaptive manner. Although comprehensive surveys of temperature-induced shifts in gene expression have not been done for any marine organisms, studies of model species, defined here as species for which the genome has been sequenced and relatively well annotated, are beginning to reveal the pervasiveness of stress-induced shifts in gene expression. The studies of Gasch and colleagues  on yeastTrematomus bernacchii, the lack of capacity to acclimate to elevated temperatures may stem in part from deficiencies in its ability to alter gene expression as its body temperature changes. Unlike virtually all other species, T. bernacchii is unable to increase the synthesis of any class of heat-shock protein following thermal stress [T. bernacchii may be unable to effect adequate chaperone-mediated restoration of the native structures of heat-denatured proteins. A build-up of aggregates of denatured proteins is likely to be cytotoxic and lead to the eventual death of cells [Despite the likely occurrence among species of generally similar requirements for temperature-adaptive shifts in gene expression, species that fall into different regions of the stenotherm to eurytherm spectrum may have distinctly different capacities for acclimatory regulation of transcription. In the context of effects of climate change, one of the most important interspecific differences relates to the ability of extreme stenotherms, which may have evolved for millions of years under stable thermal conditions, to alter gene expression in the face of temperature change. The Antarctic notothenioid fishes are a primary case in point. These species have evolved for 10–14 million years in a thermally stable "ice bath," in which annual temperature variation is usually less than 1–2°C . McMurdol stress . Lackingof cells . The absof cells , suggestof cells  or myoglof cells  is anothof cells ,28. In gAustrofundulus limnaeus, revealed wide-scale shifts in gene expression, as noted earlier for yeast [versus intermittent heat stress. Although both acclimation regimes led to changes in transcript abundance for proteins associated with alterations in lipid composition, differences between cycling and steady-state acclimation were noted in the types of lipids that appear to be produced. For steady-state acclimation, the expected changes in transcripts for enzymes involved in acyl chain double bond content (saturation) were observed, consistent with many previous studies of homeoviscous acclimation in membranes [Even among eurythermal ectotherms, there may be a variety of capacities for modulating gene expression to compensate for changes in body temperature. Although few data are currently available to test this point, acclimatory responses to rapid, diurnal variations in body temperature may require different shifts in gene expression from those that characterize slower, e.g., seasonal time-scale, responses to temperature change. A recent study  of tempeor yeast . A totalor yeast . Under sembranes . During Austrofundulus limnaeus was for the message encoding high mobility group b1 protein (HMGB1)Fig. . High moGB1)Fig.  and duriAustrofundulus limnaeus and in a recent study of carp [The changes in gene expression noted in  of carp  represen50s) and the more limited abilities to acclimate to increases in temperature noted for intertidal species. The physiological determinants of upper thermal limits are certain to be multifarious, but heart function stands out as a key contributor to these limits. In porcelain crabs and turban snails, upper lethal temperatures coincide closely with temperatures of heart failure. Such cardiac effects also could contribute to a shift from aerobic to anaerobic processes of ATP generation, which has been proposed as one important mechanism of death from high and low temperatures [Ectothermic species differ widely in thermal tolerance limits and in their abilities to adjust these limits in temperature-adaptive manners. Comparisons of congeneric species from different latitudes and different positions along the subtidal to intertidal gradient have provided important insights into adaptive variation and the threats posed by increased habitat temperature. In what at first view may seem paradoxical, warm-adapted congeners may be more threatened by increased temperatures than their cold-adapted subtidal relatives. This difference stems from two factors: the proximity of current habitat temperatures to upper lethal temperatures (LTeratures ,33. Accl


An earthquake measuring 6.3 on the Richter scale struck the city of Bam in Iran on the 26th of December 2003 at 5.26 A.M. It was devastating, and left over 40,000 dead and around 30,000 injured. The profound tragedy of thousands killed has caused emotional and psychological trauma for tens of thousands of people who have survived. A study was carried out to assess psychological distress among Bam earthquake survivors and factors associated with severe mental health in those who survived the tragedy.This was a population-based study measuring psychological distress among the survivors of Bam earthquake in Iran. Using a multi-stage stratified sampling method a random sample of individuals aged 15 years and over living in Bam were interviewed. Psychological distress was measured using the 12-item General Health Questionnaire (GHQ-12).In all 916 survivors were interviewed. The mean age of the respondents was 32.9 years (SD = 12.4), mostly were males (53%), married (66%) and had secondary school education (50%). Forty-one percent reported they lost 3 to 5 members of their family in the earthquake. In addition the findings showed that 58% of the respondents suffered from severe mental health as measured by the GHQ-12 and this was three times higher than reported psychological distress among the general population. There were significant differences between sub-groups of the study sample with regard to their psychological distress. The results of the logistic regression analysis also indicated that female gender; lower education, unemployment, and loss of family members were associated with severe psychological distress among earthquake victims.The study findings indicated that the amount of psychological distress among earthquake survivors was high and there is an urgent need to deliver mental health care to disaster victims in local medical settings and to reduce negative health impacts of the earthquake adequate psychological counseling is needed for those who survived the tragedy. An earthquake measuring 6.3 on the Richter scale struck the city of Bam in Iran on the 26th of December 2003 at 5.26 A.M. It was devastating, the city was destroyed, left over 40,000 dead and around 30,000 injured, as well as destroying approximately 20,000 homes, leaving more than 45,000 people homeless . The proThis was a population-based study of psychological distress among Bam earthquake survivors aged 15 years and over at five months after the event. The pre-earthquake population of Bam was reported to be 97,000 and surprisingly it was increased to around 120,000 at the post-earthquake period (listing from the authorities). This was due to the fact that because of worry many people living in rural areas were moved to the city of Bam after earthquake. At the time of screening about 80% of people stayed inside their homes and the remaining 20% were moved to four specially designated sites with around 4000 tents. However, to select a representative sample of individuals living in Bam a stratified multi-stage area sampling was applied. The stratification was used because people living in the southern region of the city were less affluent and exposed to more damage as compared to those living in the northern part of the city. Every household or tent within the city had the same probability to be sampled. For the purposes of the first stage, units (census sections) were randomly selected after stratifying by region and size of residence. Then, within each census section the homes and tents to be sampled were selected using the procedure of random routes. Finally the last stage sampling units  were selected randomly from all persons living in the same home or tent. Random sampling was based on a list of all households (N = 23000) derived from the electricity bills. A sample of 1150 households (5%) was selected as explained. However, we were actually able to screen 1000 households, giving a success rate of 87%. The main reason for the failed attempts for contact was due to the fact that the targeted house was uninhabited because the residents had moved out and there was no possibility to trace the survivors. In addition considering logistic difficulties we did not replace them with other households.A team of trained interviewers collected data in a week and all participants were interviewed in their homes or designated camps. However, the data were collected in a disorganized environment and thus data gathering strategy was based on offering help to survivors. Since there was no validated measure of PTSD in the Iranian language, only psychological distress was measured using the Iranian version of the 12-item General Health Questionnaire, GHQ-12 . The scaData were collected from 1000 randomly selected households (600 homes and 400 tents). In total 999 survivors were interviewed, and data for 916 respondents were complete, giving a response rate of 91.7%. The mean age of the respondents was 32.9 years (SD = 12.4), and mostly were male (53%), married (66%) and had secondary school education (50%). The mean score of the survivors on the GHQ-12 was 8.7 (SD = 3.2). The findings showed that 58% of the respondents suffered from severe psychological distress as measured by the GHQ-12. Forty-one percent of the respondents reported that they lost three to five members of their family in the earthquake. The characteristics of the respondents are shown in Table To examine differences in psychological distress among sub-groups of the study sample Student's t-test and one-way analysis of variance were performed. The findings showed that there were significant differences between people with different characteristics. People with old age, females, less educated individuals, divorced or widowed and unemployed respondents, and those with loss of more family members in the earthquake reported more severe psychological distress. The results are shown in Table To investigate factors associated with severe psychological distress all the significant findings from the univariate analysis were entered into the logistic regression analysis. The analysis indicated that being female , illiterate , unemployed , and the loss of more family members  were associated with more severe psychological distress. The results are shown in Table This was a population-based study measuring psychological distress among survivors of the Bam earthquake in Iran. Psychological morbidity was higher among females, less educated respondents and unemployed individuals. Perhaps such observation might relate to the fact that these groups of people were exposed to a relatively homogenous set of psychological stressors, leading to relatively homogenous mental health outcomes ,11. In aMost notably it was found that people who lost family members reported significant severe psychological distress compared to those who did not. Also there was a distinct pattern of psychological distress among those who lost their family members in the earthquake showing a dose-response relationship between loss and the risk of more psychological distress. One might argue that the relationship between loss of relatives and psychological distress may be a reflection of the relationship between severity of trauma exposure and psychopathology. Since no other variables reflecting trauma-exposure  were entered in the regression equation, 'loss of relatives' was the only variable tapping that, as there is high correlation between loss of first degree relatives (with whom survivors often share the same roof) and exposure to serious damage or collapse of the house. In addition studies have indicated that loss is a strong determinant of PTSD among earthquake survivors and thus it is argued the observation that the risk of PTSD is linked to the amount of loss is an important issue that needs to be incorporated in the development of any effective preventive strategy . As discPsychological distress among earthquake survivors alongside experience of other problems could be considered a serious issue for people's health status living in such difficult conditions. Evidence suggests that severe earthquakes even can cause long-standing morbidity . HoweverIt is recommended that the mean GHQ score for the whole population of respondents provide a rough guide to the best cut-off threshold . Thus coThe results reported here is an estimate for the overall population experiencing the earthquake. Studies have shown that there is variation in psychological morbidity among earthquake survivors by epicenter proximity and rate of property damage ,18. HoweThe present study had certain methodological limitations. Firstly, the results were based on the GHQ-12 scores and we did not measure PTSD symptoms. In this respect also it is important to acknowledge that the GHQ is a general measure of mental health and it is not a measure of diagnostic depression. Thus, since depression is also common in earthquake survivors, our study is limited in not including a validated diagnostic measure of depression. Secondly, no information was obtained on important trauma-exposure variables such as extent of fear or perceived life-threat during the earthquake, rubble experience, disability or injury, et cetera. Thirdly, data on important demographic variables such as past psychiatric illness, and family psychiatric illness were not collected. However in spite of these limitations, the results from this study are useful. This study is the only paper addressing reports on the psychological consequences of the most disastrous earthquakes of the last 50 years. In addition, to our knowledge the international literature does not contain any study on the psychological status of Iranian earthquake survivors, despite the fact that Iran is an earthquake-prone country. It seems that the future research should carefully assess the psychological distress and disruption experiences of the survivors in order to implement necessary interventions. Given that Iran is a country that suffers catastrophic earthquakes relatively frequently, there is need to develop and validate standard instruments such as PTSD measures to include in the future research.In conclusion the findings from this study indicated that the survivors of Bam earthquake suffer from psychological distress three times higher than the normal population. In addition female gender, lower education, unemployment, and loss of family members were found to be associated with more severe psychological morbidity among survivors. This suggests that to reduce negative health impacts of the earthquake adequate psychological counseling is needed for those who survived the tragedy.GHQ-12: The 12-item General Health Questionnaire; PTSD: Post-traumatic stress disorder; SD: Standard deviation; OR: Odds ratio, CI: Confidence interval.The author(s) declare that they have no competing interests.AM was the principle investigator and contributed to the study design, the data analysis and wrote the paper. All other investigators contributed to the study design, the data collection and analysis. All authors reviewed and contributed to the final manuscript.The pre-publication history for this paper can be accessed here:


Traditional two dimensional journal publishing may no longer be enough, and may be hindering, the interpretation of the complex datasets and analyses generated by microarray experiments in medicine Statistical Applications in Genetics and Molecular Biology (3: article 37), discuss a paradigm for the presentation of complex data—in this case, from microarray analyses. The title of the article, “A Compendium to Ensure Reproducibility in High-Dimensional Classification Tasks,” may not lend itself easily to a clinical audience, but the underlying message to clinicians could not be more important: that, currently, studies involving large datasets, especially ones that have a clinical outcome, are so poorly reported (or possibly so poorly done) that many are not reproducible. This problem was also the topic of a recent meeting in Heidelberg, “Best Practice in Microarray Studies” (http://www.biometrie.uni-heidelberg.de/workshops/bestpractice/index.htm).“Publishing results in traditional paper based way in a journal hides too much information.” This is the verdict of Markus Ruschhaupt and colleagues who, in a paper in As microarrays have become mainstream research tools in biology and medicine, the large datasets and complex analyses from these studies have presented challenges: for authors in analyzing the data, for reviewers and editors in deciding on the suitability of papers for publication, for journals in determining how much data needs to be presented within the paper itself, for other researchers in reproducing the data, and, finally, for readers in deciding how to assess the data presented. The results from several high-profile papers have already proved difficult to reproduce, even by those with sufficient time and computing expertise.Where do such analyses leave the new science of molecular pathology? Ruschhaupt and colleagues comment that “the literature on the induction of prognostic profiles from microarray studies is a methodological wasteland.” Much the same could be said of other applications of molecular biology to clinical samples. A systematic review of molecular and biological tumor markers in neuroblastoma (Clin Cancer Res 10: 4–12) found that its conclusions were limited by “small sample sizes, poor statistical reporting, large heterogeneity across studies…and publication bias.” John Ioannidis and colleagues (Lancet 362: 1439–1444) did a similar analysis of 30 microarray studies with major clinical outcomes in cancer. They showed that the studies were small—median sample size was 25 patients, and validation was incomplete in most studies. They recommended that molecular prognostic studies be classified as phase 1 (early exploratory probing associations), phase 2 , or phase 3 (large confirmatory studies with pre-stated hypotheses and precise quantification of the magnitude of the effect), and that only studies that had undergone phase 3 testing should be considered robust enough for use in clinical practice. Most current studies should be considered as phase 1 or, at best, phase 2.So, despite considerable hype, the published studies are far from the level of evidence that would be accepted for virtually any other medical test. In a review in 2003 (Hematology [Am Soc Hematol Educ Program] 2003: 279–293), Rita Braziel and colleagues concluded, “rapid identification and neutralization of spurious results is essential to prevent them from becoming accepted facts.”But these problems are not new in medical research. In 1994 (BMJ 308: 283–284), Doug Altman, who was instrumental in developing the CONSORT guidelines for reporting of clinical trials, said that “huge sums of money are spent annually on research that is seriously flawed through the use of inappropriate designs, unrepresentative samples, small samples, incorrect methods of analysis, and faulty interpretation,” and “that quality control needs to be built in from the start rather than the failures being discarded.”PLoS Medicine is keen to work with authors towards making such reporting possible. But although the time might have gone when the two-dimensional journal article could suffice for complex papers, clinicians should nonetheless apply the same critical assessment that they would for any other clinical tool. If a result is too good to be true, it probably is.So how can we ensure that the wealth of data pouring out of microarray and other molecular diagnostic studies is turned into meaningful knowledge? The Microarray Gene Expression Data Society has proposed a set of guidelines (MIAME) for the reporting of microarray data, and that all such data should be deposited in public databases. But as Ruschhaupt and others have shown, disclosure of results and data is not enough, since there is little consensus on the appropriate statistical analyses and many are developed on a case by case basis, which may not be reproducible, even by the authors. Some researchers advocate the use of standard statistical packages, which allows the reader to repeat an entire analysis quickly and, hence, assess the robustness of the results. Some authors have produced a transcript of their statistical analyses as a supplement to their articles . At the very least authors should have a protocol with a prespecified plan for patient selection and statistical analysis—accepted practice for clinical trials, but not yet for other medical research. An ultimate aim for reporting would be the type of compendium discussed by Ruschhaupt and colleagues—“an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions.” One such compendium is illustrated in a paper by Robert Gentleman (Stat Appl Genet Mol Biol 4: article 2).


Diagnostic discordance for osteoporosis is the observation that the T-score of an individual patient varies from one key measurement site to another, falling into two different diagnostic categories identified by the World Health Organization (WHO) classification system. This study was conducted to evaluate the presence and risk factors for this phenomenon in a large sample of Iranian population.Demographic data, anthropometric measurements, and risk factors for osteoporosis were derived from a database on 4229 patients referred to a community-based outpatient osteoporosis testing center from 2000 to 2003. Dual-energy X-ray absorptiometry (DXA) was performed on L1–L4 lumbar spine and total hip for all cases. Minor discordance was defined as present when the difference between two sites was no more than one WHO diagnostic class. Major discordance was present when one site is osteoporotic and the other is normal. Subjects with incomplete data were excluded.In 4188 participants , major discordance, minor discordance, and concordance of T-scores were seen in 2.7%, 38.9% and 58.3%, respectively. In multivariate logistic regression analysis, older age, menopause, obesity, and belated menopause were recognized as risk factors and hormone replacement therapy as a protective factor against T-score discordance.The high prevalence of T-score discordance may lead to problems in interpretation of the densitometry results for some patients. This phenomenon should be regarded as a real and prevalent finding and physicians should develop a particular strategy approaching to these patients. Osteoporosis is defined as a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture ,2. This 'T score' is a statistical definition which indicates the difference between patient's BMD and mean bone density of normal population in the age of 20 – 30 (reference population) [Discordance in diagnosis of osteoporosis is defined as presence of different categories of T scores  in two skeletal sites of an individual patient . This phActually, one of the reasons for measuring BMD in several sites is the presence of discordance, which can affect the diagnosis and therapeutic plan in an individual person. Various studies have analyzed the prevalence and impact of T-score discordance on different aspects of management of osteoporosis -9. HowevGiven this background and concerning the need for the estimation of the impact of this phenomenon in our country, we aimed to evaluate the presence and risk factors for T-score discordance in a large sample of Iranian population.Participants in this study were 4229 persons who underwent bone densitometry in outpatient clinic of Endocrinology & Metabolism Research Center in Tehran from 2000 to 2003. A considerable proportion of these cases were healthy post-menopausal women referred by clinicians for densitometric evaluations. All study participants signed the informed consent for any scientific approach to their medical registered data. Our Institutional Review Board approved this study.A standardized questionnaire was filled before densitometry for all participants. Demographic data (including age and sex) as well as other known or suspicious risk factors for osteoporosis  were collected. All participants had their standing height measured using a stadiometer to the nearest 0.5 cm. Weight was measured on a standard weighting scale with a precision of 0.5 kg. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. All the BMD measurements were done for diagnostic purposes and none of the participants were on the treatment with bone active agents (hormone replacement therapy was not considered a bone active agent).BMD was measured at the lumbar spine and total hip with dual X-ray absorptiometry (DXA) using a Lunar DPXMD densitometer  by a trained operator according to the manufacturer's instruction. The instrument was calibrated weekly by using appropriate phantoms. Precision error for BMD measurements was 1–1.5% in the lumbar and 2–3% in the femoral regions. The device normative data of US population for spine BMD and NHANES III study for femur BMD were used as reference values.P values less than 0.05 were taken to indicate statistical significance. Statistical analyses were performed using Stata Statistical Package, version 8.0 .All the data gained from densitometry and questionnaires were entered into a comprehensive relational database. The participants with incomplete data were excluded from the study. To compare presence of various risk factors in participants with and without T-score discordance, chi-square test and independent sample t-test were used firstly. Potential risk factors were entered to a multivariate binary logistic regression analysis and the resulted odds ratios with 95% confidence intervals were reported. In sum, 4188 persons were enrolled in the study. Characteristics of all participants are summarized in Table Totally, 518 participants were diagnosed in osteoporotic range in hip area and 1036 participants in the lumbar area. T-score classifications are presented in Table T-score discordance was more prevalent in women than men . The mean age of participants with discordance (54.8 years) was higher than the other group . In 3848 female participants, the number of post-menopausal women with diagnostic discordances (951 of 2027) was significantly higher than pre-menopausal participants with discordance . In multivariate analysis  would show T-score discordance between hip and spine sites. Most of these discordances (38.9%) are from minor category, presenting difference on only one class, and could be due to minor variation in BMD techniques or some minor physiologic dissimilarity. Minor discordance generally does not influence the overall prognosis of patients; however, in the case of patients with one site normal and the other osteopenic, follow up of patients with hip osteopenia seems reasonable [The multivariate analysis we have implemented to the data could aid clinicians and diagnosticians to approach patients with different characteristics. According to our results, BMD measurement in both sites is necessary at least for older patients and post-menopausal women especially those with delay in menopause. Hormone replacement therapy, however, could decrease the diagnostic discordance and patients receiving estrogen and progesterone are more likely to have similar results in DXA scans of lumbar and femoral areas. This could be the result of drug effects on the BMD of lumber area .Generally, five different causes have been proposed for occurrence of discordance . PhysiolMajor discordance was observed in 2.7% of our participants, which is in agreement with the results of similar studies. In both major and minor discordances, lower BMD for lumbar spine was more prevalent. This could be due to several reasons. The difference between velocities of bone loss in different parts of human body could be the main reason . TrabecuIn 30% of our participants, the lumbar T-score was higher than hip T-score and this culminated in poorer hip diagnoses in 9.2% of participants. This phenomenon could be regarded as 'inverse discordance' and several factors may be involved in its occurrence. One of these reasons is the prevalent vitamin D deficiency in our participants. A recent nationwide study with random sampling from five major cities in Iran reported a high prevalence (about 80%) for vitamin D deficiency in Iranian population . Other sThe observation of 'inverse discordance' could not be regarded as a direct influence of more significant bone loss in femoral region. A known phenomenon named 'birth cohort effect' can play a role . This inThis study, as every other cross-sectional study, has a number of limitations. We could not rule out the possibility of referral bias for this study. As the study was performed in a referral center affiliated to a teaching hospital, the assumption of similarity of study population to exact community is not reasonable and we could not generalize the results to the Iranian population. The other limitation is the choice of multivariate analysis used in this study. With the current analysis, prediction of the presence or absence of T-score discordances is possible. However, prediction of the situation of one site according to results of the other site or choosing one site to measure BMD need further evaluations and analyses which was behind the scope of this study. Future studies using more powerful statistical analyses with larger sample sizes are needed to establish these imperative questions.The importance of existing discordance on the prognosis and fracture risk of patients needs further prognostic studies with long follow-up designs. The high prevalence of T-score discordance could induce some problems for the physicians in decision-making regarding these patients. In general, high prevalence of discordance in this study and similar studies suggests some defects in the cut-off values for definition of osteoporosis and osteopenia proposed with the WHO . To elimIn summary, this study indicates that about 40% of participants evaluated for bone density changes in a referral center may show diagnostic discordance, majority of them from minor class. This phenomenon should be regarded as a real and prevalent finding and physicians should become familiar with this topic. Clinicians should look for possible cause or causes of this occurrence and develop a particular strategy approaching to these patients.This study was supported by a grant from Endocrinology & Metabolism Research Center of Tehran University of Medical Sciences.In advance, suggestion of the design of the study was from AS. Data extraction and initial analysis were done by NKT and AH. AM performed additional analyses and wrote the first draft of the paper. AS and BL both had helpful and valuable comments in revising the paper. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Coalescent theory is a general framework to model genetic variation in a population. Specifically, it allows inference about population parameters from sampled DNA sequences. However, most currently employed variants of coalescent theory only consider very simple demographic scenarios of population size changes, such as exponential growth.Here we develop a coalescent approach that allows Bayesian non-parametric estimation of the demographic history using genealogies reconstructed from sampled DNA sequences. In this framework inference and model selection is done using reversible jump Markov chain Monte Carlo (MCMC). This method is computationally efficient and overcomes the limitations of related non-parametric approaches such as the skyline plot. We validate the approach using simulated data. Subsequently, we reanalyze HIV-1 sequence data from Central Africa and Hepatitis C virus (HCV) data from Egypt.The new method provides a Bayesian procedure for non-parametric estimation of the demographic history. By construction it additionally provides confidence limits and may be used jointly with other MCMC-based coalescent approaches. The coalescent is a very versatile stochastic model of the genetic variation in a set of sequences sampled from a population. It allows to accommodate a wide range of assumptions about rates and modes of evolution, and of population history -5.As the observed sequence data are positively correlated due to common ancestry, coalescent theory also provides a framework for understanding the relationship between a population's history and its genealogy. For instance, it has long been noted that genealogies of samples taken from exponentially growing populations tend to be "star-like" with short branch lengths near the root of the tree. In contrast, the inter-node distances in genealogies from constant-size populations typically are much more evenly spaced. Thus, coalescent theory quantifies the imprint that demographic development of a population leaves in the data. While the original theory was outlined for constant population size ,2, Slatkstatistically inferring the demographic history as a function of time from the sampled sequences , and for any given k the it contains k free position parameters and k + 2 free height parameters. Note that, unlike in the skyline plot, we do not constrain the change-points a1,..., ak to lie on the grid points defined by the inter-node distances wi. Moreover, we also allow that the number of internal nodes k changes during sampling of the population function from the posterior. Hence, k is technically a hyper-parameter that controls the roughness of the resulting spline. As will be clear from the outline of the MCMC algorithm below, note that the final point estimate  obtained from posterior sampling will be a mixture of linear splines (i.e. a smooth and possibly nonlinear function) rather than a single spline.In our suggested procedure we approximate the sampled demographic history L employed in our procedure is the product of the densities of the waiting times between subsequent coalescence events, i.e. . This function depends via Eq. 2 on the effective population size Ne(t), and hence indirectly on the spline parameters ai, hi and k. Because Ne(t) is represented by a linear spline, calculation of the likelihood can be done in a computationally efficient fashion.The likelihood k, i.e.Following  we emploc is a normalizing constant to ensure that P(k) is a proper distribution. For the hard upper limit of the number of change-points we use kmax = 30. The parameter λ acts as a smoothing parameter, set in a typical analysis to about λ = 0.1 - 1.0.where λ we also suggest a hierarchical Bayes approach where λ is drawn from a Gamma distributionAs an alternative to using a fixed a and scale parameter b  = ab ≈ 1 and Var(λ) = ab2 ≈ 2).with some shape parameter a priori uniformly distributed in the interval . As a simple trick to avoid very small inter-node distance we generate 2k + 1 random variables, and set the change-points aj = zj]  is determined by drawing from the corresponding uniform distribution. Third,  is accepted with probabilityFirst, a change-point a* of the new change-point is found by uniformly drawing from , and let the neighboring nodes left and right of a* have positions aj and aj+1. Second, the corresponding new height h* is generated by randomly disturbing the current height Ne(a*) on the position a* according to Ne(a*) + zNe(a*) where z is a uniformly distributed random variable on the interval . Note that the birth step increases the dimension of the parameter vector from 2k + 2 to 2k + 4 as a new change-point and a new height are generated.First, the position The corresponding acceptance probability of the birth step is computed according to Eq. 5 with likelihood and prior ratios as above, and with proposal ratioand Jacobi determinanta* chosen from a1,..., ak with probability . Second, the corresponding height h* is also removed from the vector of spline parameters. The acceptance probability for the death step isThis is the inversion of the birth step and consists of removing a change-point. First, where the proposal ratio and the Jacobi determinant is the same as for the birth step.Ne(t) = c, where c is some rough estimate of population size, and k = 0. Second, 100,000 repeats of the MCMC algorithm are performed, of which the first 5,000 are ignored to allow for a "burn-in" period.In order to obtain an estimate of the effective population size in time we now proceed as follows. First, the Markov chain is started with an initial state that corresponds to a completely flat demographic function, i.e. ai, hi and k are obtained.Third, the remaining samples are thinned out by a factor of 1:50 to remove auto-correlation. As a result, 1900 independent samples from the joint posterior of the spline parameters  and associated confidence bands we compute the distribution of the effective population size at a number of fixed equidistant time points t1, t2,..., t1000 ∈ . Finally, we report as summary statistics the corresponding median and the lower and upper 2.5% quantiles.Subsequently, in order to obtain a point estimate wn, wn-1,..., w2.)In this paper we have introduced non-parametric sampling of demographic histories assuming a fixed underlying genealogical tree  height distribution has to be set in advance (and independent of the T*). For the case of 0 <t <T* sampling of heights then proceeds as described above, while for T* <t <T – the region with no data from a given sampled tree – the heights are simply drawn from the respective prior distribution.Note that each sampled tree may have a different depth In order to test the potential of the proposed reversible jump MCMC algorithm we first applied it to synthetic data simulated according to various demographic scenarios. Subsequently, we reanalyzed two viral data sets from Central Africa and Egypt.The proposed framework has been implemented by us for the case of a single underlying genealogy. The program is written in the statistical computer language R  and is iTo install the APE package, simply run the R program, and enter at the R promptinstall.packages("ape")This downloads the APE package from the Internet. The proposed reversible jump MCMC approach is implemented in the function "mcmc.popsize" of which an extensive description along with examples can be obtained online by typinglibrary("ape")help(mcmc.popsize)into the R command window. The APE package also includes routines for plotting the inferred population function .Note that the use of this R program is only valid if the phylogenetic error is low – this is typically the case when the evolutionary rate is high and the available sequences are long . If the phylogenetic error is not negligible compared to the coalescent error, please use software such as BEAST .Ne(t) = 100) as well as exponential population growth (Ne(t) = l000et-), using 25 and 100 sampled lineages, respectively. To estimate the population size function we employed the proposed MCMC algorithm and the classic and generalized skyline plot. In the former the smoothing parameter λ was drawn from the hierarchical model with default parameters (a = 0.5 and b = 2).In the simulation setup we followed Pybus et al.  and StriNe(t) is a very good point estimator of the true demographic history. In addition, the 95% confidence band is also automatically obtained by the MCMC method. Interestingly, it can be immediately seen that the uncertainty in Ne(t) increases with a growing distance from the present. This simply reflects the fact that near the root of the tree for constant population size there are only few coalescent events.Figure In Figure In Figure Next, we applied our method to infer the demographic history from a set of HIV-1 sequences from Central Africa. These data was originally used by Vidal et al.  who examk = 9.25, i.e. the estimated effective degree of freedom is much less than that implicitly assumed in the classic skyline plot.Figure A comparison with the generalized skyline plot  is shownIn Egypt 10%-20% of the general population are infected with the Hepatitis C virus (HCV) . This enWe repeated their analysis with the reversible jump MCMC approach developed in this paper. We downloaded the sequence data from the HCV sequence database  and infeNe(t) in the log-plot changes). Near the present, the rate decreased again. Also note the broadening of the confidence interval since 1940 which reflects the sparsity of available observations. This implies that the claim of Tanaka et al. [Generally, the star-like shape of the inferred tree already is indicative of exponential growth. This is confirmed by both the skyline plot as well as by our analysis Figure . Moreovea et al.  that thea et al. .Ne(t). Unlike its predecessors, the classic and generalized skyline plots, it returns a smooth and realistic estimate of the demographic history and thus overcomes the constraints due to assuming a step function. Moreover, it automatically provides confidence limits. Nevertheless, the procedure is still computationally fast and can be run on any standard PC hardware.We have presented a new approach to non-parametric inference of demographic history from an inferred genealogy. This method is based on reversible jump MCMC sampling of the population size function Ne(t) are not taken into account.In our examples we demonstrated the advantage of non-parametric estimation of demographic history. Parametric estimation always assumes a certain functional form of population growth which may lead to problematic statements (cf. the HCV data set), in particular if the confidence bands of the estimated function λ) that controls the a priori degree of smoothing, and this is adjusted accordingly by the investigated data. In addition, a further advantage of our MCMC approach is that – in contrast to the skyline plot – at least in principle it is straightforward to incorporate it in more general MCMC sampling schemes that also take account of the uncertainty in the genealogy.From the methodological point of view, model selection via rjMCMC has the advantage that the effective dimension, i.e. the degree of smoothing, is automatically chosen in a data-driven manner. There is only one parameter (During the referee process we have learned that the authors of the software package BEAST  have devThis paper summarizes the main results from a master's thesis of R.O. supervised by K.S. and L.F. Accordingly, K.S. and L.F. jointly devised the project and R.O. carried out all analyses and simulations. All authors participated in the development of methodology. R.O. and K.S. wrote the manuscript. All authors approved of the final version.


Late-life depression is a common, chronic and recurring disorder for which guidelines recommend long-term therapy. The safety and efficacy of duloxetine for the treatment of major depressive disorder (MDD) were evaluated using data from elderly patients  who participated in a large, multinational, open-label study.17), the Beck Depression Inventory-II (BDI-II), the Patient Global Impression of Improvement (PGI-I) scale, and the Sheehan Disability Scale (SDS). Safety and tolerability were evaluated using discontinuation rates, spontaneously reported adverse events, and changes in vital signs, ECG, and laboratory analytes.Patients meeting DSM-IV criteria for MDD received duloxetine 80 mg/d (40 mg twice daily (BID)) to 120 mg/d (60 mg BID) for up to 52 weeks. Efficacy measures included the Clinical Global Impression of Severity (CGI-S) scale, the 17-item Hamilton Rating Scale for Depression . Significant improvement (p < .001) in both clinician- (CGI-S) and patient-rated (PGI-I) measures of improvement were observed at Week 1 and sustained throughout the study. Observed case response rates at Weeks 6, 28, and 52 were 62.9%, 84.9%, and 89.4%, respectively, while the corresponding rates of remission were 41.4%, 69.8%, and 72.3%. Adverse events led to discontinuation in 27 (26.7%) patients. Treatment-emergent adverse events reported by >10% of patients included dizziness, nausea, constipation, somnolence, insomnia, dry mouth, and diarrhea. Most events occurred early in the study. Mean changes at endpoint in blood pressure and body weight were less than 2.0 mm Hg, and -0.1 kg, respectively.Mean changes in HAMDIn this open-label study, duloxetine was effective, safe, and well tolerated in the long-term treatment of MDD in patients aged 65 and older. Late-life depression is a common and disabling condition which represents a substantial public health concern . The preDespite advances in available antidepressant treatments, limitations still exist in both efficacy and safety. Tricyclic antidepressants (TCAs) generally provide robust efficacy, but a number of side effects associated with this class of medications are of particular concern in older patients . Selective serotonin reuptake inhibitors (SSRIs) have provided an improved tolerability profile compared to the TCAs through lower rates of adverse events, and substantially lower toxicity in overdose . FurtherDuloxetine is a potent dual reuptake inhibitor of serotonin (5-HT) and norepinephrine (NE) . The effThe safety and tolerability of duloxetine have also been demonstrated under double-blind conditions. In placebo-controlled trials of duloxetine in patients aged 18 and older (doses from 40 – 120 mg/d) the most frequently reported adverse events were nausea, headache, dry mouth, fatigue, insomnia, and dizziness, while the overall safety profile of duloxetine was comparable to that of available SSRI medications . A compaHowever, these acute placebo-controlled trials of duloxetine were of 9 weeks duration or less. An NIH consensus panel has recommended that geriatric patients be given continuing antidepressant treatment for at least 6 months for a first episode and for at least 1 year for recurrent episodes , while sDiagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) [This was a 52-week, open-label, single-arm study of outpatients (aged ≥ 18 years) meeting (DSM-IV)  criteriaThis report focuses upon data taken from the subset of patients aged 65 years and older (n = 101) within the larger study described above.The study protocol was approved by the ethics committee at each site in accordance with the principles of the Declaration of Helsinki. All patients provided written informed consent prior to the administration of any study procedures or study drug. All patients were required to have a Clinical Global Impression of Severity (CGI-S) score ≥ 3 at the screening and baseline study visits.Patients were excluded for the following reasons: a previous or current diagnosis of schizophrenia, schizophreniform disorder, schizoaffective disorder, or bipolar disorder; presence of an Axis II disorder that would interfere with protocol compliance; serious medical illness; taking benzodiazepines on a daily basis for ≥ 2 weeks prior to enrollment; a history of substance dependence within the last year; or a positive urine drug screen. Subjects judged to be at risk for suicide were also excluded.Patients were not permitted to receive other antidepressant, antimanic, or antipsychotic agents during the study. Episodic use  of benzodiazepines was permitted. The use of benadryl, chloral hydrate, cough and cold medications, and narcotics, was allowed on an episodic basis only. Subjects were permitted to take antihypertensives, antiarrhythmics, antibiotics, and multivitamins among other medications while in the study.a priori specified as the primary outcome), the HAMD17 total score [17 subscales , the Beck Depression Inventory-II (BDI-II) [responders if they had a decrease from baseline of at least 50% in HAMD17 total score. Patients were defined as remitters if they had a HAMD17 total score ≤ 7.Efficacy was assessed using the CGI-S scale  (a prior(BDI-II) , and the(BDI-II) . Patient(BDI-II) , which iSafety measures included spontaneously reported adverse events, serious adverse events , vital signs, electrocardiograms (ECGs), and laboratory analyses. Adverse events and vital signs were collected at each visit.Lilly reference ranges were used to define limits for abnormal laboratory values , and potand a decrease from baseline of ≥ 20 mm Hg;(i) Low supine (or standing) systolic BP: ≤ 90 mm Hg and an increase from baseline of ≥ 20 mm Hg(ii) High supine (or standing) systolic BP: ≥ 180 mm Hg and a decrease from baseline of ≥ 15 mm Hg;(iii) Low supine (or standing) diastolic BP: ≤ 50 mm Hg and an increase from baseline of ≥ 15 mm Hg.(iv) High supine (or standing) diastolic BP: ≥ 105 mm Hg Patients were considered hypertensive at baseline if they had a historical diagnosis, secondary condition, or adverse event at the baseline visit consistent with a clinical diagnosis of hypertension or high blood pressure.ECGs were collected at baseline and Weeks 4, 28, 52 or at early discontinuation. Patients at 2 sites in Mexico and 1 site in Columbia also had ECGs over-read by a cardiologist at a central location. For these ECGs, QT intervals were corrected (QTc) using Fridericia's correction (QTcF). All other patients had ECGs read by the site for classification as either normal or abnormal. Limits for PCS QTc values were an increase in QTcF of ≥ 30 msec and any postbaseline value ≥ 450 msec for males or ≥ 470 msec for females .Mean changes from baseline to last observation in laboratory analytes, vital signs, and ECG intervals were assessed using ANOVA with models that included investigator. Longitudinal mean changes and categorical changes  were assessed via a likelihood-based repeated measures approach. Models for mean changes included investigator, visit, baseline value, and baseline-by-visit interaction.Mean change in CGI-S score was compared between younger (age <65) and elderly (age ≥ 65) patients using the repeated measures analysis as previously described, with age group and age group-by-visit interaction added to the model. Differences between young and elderly patients in rates of treatment emergent adverse events were assessed using Fisher's exact test.This report was based on data from 101 patients aged 65 and older. The oldest patient was 87 years of age, while the median age was 70. Patient characteristics at baseline are summarized in Table Mean changes from baseline for all efficacy outcomes were highly significant  at all assessment times Table . In the A comparison of visitwise mean changes in CGI-S score between elderly patients  and those patients in the study aged <65 years , personal conflict/other reasons (9.9%), and noncompliance (5.0%). The adverse events leading to discontinuation in >1.0% of enrolled patients at a duloxetine dose of 80–120 mg/d were somnolence (4.0%), dizziness (3.0%), diarrhea (2.0%), hypertension (2.0%), and vomiting (2.0%). Two-thirds of the discontinuations due to adverse events (18/27) occurred within 2 weeks of initiation of therapy.A total of 9 enrolled patients reported serious adverse events during the study. Most of these events were considered by the investigator to be unrelated to duloxetine exposure. The serious adverse events reported by more than 1 patient were hip fracture (2), and confusion (2), while there were single reports of agitation, angina pectoris, cerebrovascular disorder, coronary artery atherosclerosis, dementia, dizziness, hypomania, and myocardial ischemia. Individual occurrences were few, thus no clear temporal pattern of incidence of each event could be determined.Treatment-emergent adverse events occurring in >5% of patients during the open-label therapy phase (Weeks 1 through 52) are summarized in Table Rates of occurrence of other adverse events of importance in an elderly population were low: 2 patients experienced a fall, while there were single reports of syncope and postural hypotension. When analyzed by age group, patients aged 65 and older were found to report a significantly lower incidence of insomnia and headache than those patients aged <65 Table . No otheMean changes from baseline to last observation for standing and supine systolic and diastolic blood pressures were less than 2 mm Hg and not significantly different from zero: supine systolic BP -1.5 mm Hg (p = .364), supine diastolic BP -1.8 mm Hg (p = .141), standing systolic BP -1.9 mm Hg (p = .269), standing diastolic BP -0.1 mm Hg (p = .907). Using repeated measures analysis, mean changes in blood pressure were <4 mm Hg at every visit from baseline to endpoint.A mean change analysis was utilized to compare blood pressure in patients who were hypertensive (n = 40) versus non-hypertensive (n = 58) at baseline. Baseline hypertensive patients exhibited small mean decreases (<4 mm Hg) in both standing and supine systolic and diastolic blood pressures from baseline to endpoint, while patients who were not hypertensive at baseline demonstrated mean changes in these same measures of 0.3 to -1.1 mm Hg Figure .Mean baseline-to-endpoint increases were observed for supine pulse  and standing pulse  but these values did not differ significantly from zero.Rates of occurrence of potentially clinically significant (PCS) values for systolic and diastolic blood pressures were generally low. The incidence of PCS low standing systolic blood pressure was 5/96 (5.2%), while all other assessed blood pressure and pulse readings had incidences of PCS values <2.5%.There were no significant changes in cardiac intervals detected by ECG. Mean changes from baseline to last observation were: PR -3.3 msec (p = .363), QRS -2.5 msec (p = .420), QT 5.0 msec (p = .730), and QTcF 6.2 msec (p = .553). No patient experienced a PCS QTcF value during the course of the study.After 52 weeks of treatment, the mean change in weight from baseline to last observation was -0.1 kg (p = .741), while a mean weight change of +0.3 kg was determined using MMRM analysis  values.All patients who proceeded past Week 52 received no study drug for 2 weeks until Week 54 via abrupt discontinuation (no taper). Discontinuation-emergent adverse events occurring in ≥ 5% of patients were dizziness (8.9%), anxiety (7.9%), headache (5.0%), and insomnia (5.0%).17, BDI-II, PGI-I, SDS) at all visits. By way of comparison, significantly greater improvements for duloxetine compared with placebo were observed in HAMD17 total score, HAMD17 subscales and CGI-S score in two 9-week, placebo-controlled studies of duloxetine (60 mg once daily (QD)) in patients aged 55 years and older [The current analysis focused upon 101 depressed patients aged 65 years and older who received long-term, open-label treatment with duloxetine (80 mg/d or 120 mg/d). Efficacy was demonstrated on all assessed outcome measures, both clinician- and patient-rated. Highly significant improvements were seen in both patient- and clinician-rated depression and health outcome scales , and global improvement  [Onset of efficacy is an important consideration in antidepressant trials, but in the absence of a placebo arm it is especially difficult to define and assess . HoweverS scale) . It has S scale) . Indeed,In this study, observed case response and remission rates following 6 weeks of open-label duloxetine therapy  were comparable to the response and remission rates  observed in older patients in two 9-week double-blind, placebo controlled trials of duloxetine (60 mg QD) . FurtherIn light of the recommendation that elderly patients receive at least 12–18 months of antidepressant therapy , the lonThe incidence and pattern of treatment-emergent adverse events during Weeks 1 to 8 of this study were generally similar to those observed in acute-phase, placebo-controlled trials in older patients . The mosAdministration of medication to elderly patients necessitates consideration of the physiological changes which accompany aging. Such changes can result in substantial differences in adverse event profiles between older and younger patient populations . In thisAntidepressants with benign cardiovascular profiles may be particularly suitable for the treatment of an elderly population, in which heart disease is more prevalent than in younger patients . In thisWeight change is an important consideration in older patients being treated with antidepressants , especiaIt is important to consider all of the safety findings described here in light of the dosing and design requirements of the study. The doses used in this open-label study were up to 2-fold greater than the once-daily 60 mg duloxetine dose which has been shown to provide robust efficacy in older patients in placebo-controlled trials . The dosResults from this open-label study of depressed patients aged 65 and older suggest that duloxetine is safe and well tolerated in long-term use. Statistically significant and clinically relevant improvements in all assessed efficacy measures were observed at each patient visit. Furthermore, the efficacy and adverse event profile of duloxetine appears to be comparable in older (age ≥ 65) and younger patients (age 18–64). These results, together with those obtained from acute phase, double-blind, placebo-controlled trials, support the efficacy of duloxetine in the treatment of major depression in older patients.Drs. Wohlreich, Mallinckrodt, and Watkin are employees of Eli Lilly and Company. Dr. Hay was employed by Eli Lilly and Company at the time of the study.MMW, CHM, JGW, and DPH participated in interpretation of data and drafting of the manuscript. CHM carried out the statistical analyses. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: