Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.
There is growing interest in how best to adapt and re-adapt treatments to individuals to maximize clinical benefit. In response, adaptive treatment strategies (ATS), which operationalize adaptive, sequential clinical decision making, have been developed. From a patient's perspective an ATS is a sequence of treatments, each individualized to the patient's evolving health status. From a clinician's perspective, an ATS is a sequence of decision rules that input the patient's current health status and output the next recommended treatment. Sequential multiple assignment randomized trials (SMART) have been developed to address the sequencing questions that arise in the development of ATSs, but SMARTs are relatively new in clinical research. This article provides an introduction to ATSs and SMART designs. This article also discusses the design of SMART pilot studies to address feasibility concerns, and to prepare investigators for a full-scale SMART. As an example, we consider an example SMART for the development of an ATS in the treatment of pediatric generalized anxiety disorders. Using the example SMART, we identify and discuss design issues unique to SMARTs that are best addressed in an external pilot study prior to the full-scale SMART. We also address the question of how many participants are needed in a SMART pilot study. A properly executed pilot study can be used to effectively address concerns about acceptability and feasibility in preparation for (that is, prior to) executing a full-scale SMART.  Multiple treatments are available for nearly all the mood disorders. This range of treatment options adds a new dimension of choice to clinical decision making. In addition to prescribing the best initial treatment, clinicians should have an algorithm for deciding if and when to make subsequent changes in treatment to take advantage of second-line treatment options when necessary. This article aims to 1) show that a wide variety of clinical decisions can be framed as choices among adaptive (within-patient) threshold-based strategies or algorithms, illustrating the generality of the concept; 2) illustrate two ways to design randomized clinical trials to compare treatment strategies with each other to decide which strategy is best; and 3) discuss some of the advantages offered by these designs, in terms of both patient acceptability and adherence to experimental protocols. Abstract STAR*D is a multisite, prospective, randomized, multistep clinical trial of outpatients with nonpsychotic major depressive disorder. The study compares various treatment options for those who do not attain a satisfactory response with citalopram, a selective serotonin reuptake inhibitor antidepressant. The study enrolls 4000 adults (ages 18–75) from both primary and specialty care practices who have not had either a prior inadequate response or clear-cut intolerance to a robust trial of protocol treatments during the current major depressive episode. After receiving citalopram (level 1), participants without sufficient symptomatic benefit are eligible for randomization to level 2 treatments, which entail four switch options (sertraline, bupropion, venlafaxine, cognitive therapy) and three citalopram augment options (bupropion, buspirone, cognitive therapy). Those who receive cognitive therapy (switch or augment options) at level 2 without sufficient improvement are eligible for randomization to one of two level 2A switch options (venlafaxine or bupropion). Level 2 and 2A participants without sufficient improvement are eligible for random assignment to two switch options (mirtazapine or nortriptyline) and to two augment options (lithium or thyroid hormone) added to the primary antidepressant (citalopram, bupropion, sertraline, or venlafaxine) (level 3). Those without sufficient improvement at level 3 are eligible for level 4 random assignment to one of two switch options (tranylcypromine or the combination of mirtazapine and venlafaxine). The primary outcome is the clinician-rated, 17-item Hamilton Rating Scale for Depression, administered at entry and exit from each treatment level through telephone interviews by assessors masked to treatment assignments. Secondary outcomes include self-reported depressive symptoms, physical and mental function, side-effect burden, client satisfaction, and health care utilization and cost. Participants with an adequate symptomatic response may enter the 12-month naturalistic follow-up phase with brief monthly and more complete quarterly assessments. Abstract The National Institute of Mental Health initiated the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) program to evaluate the effectiveness of antipsychotic drugs in typical settings and populations so that the study results will be maximally useful in routine clinical situations. The CATIE schizophrenia trial blends features of efficacy studies and large, simple trials to create a pragmatic trial that will provide extensive information about antipsychotic drug effectiveness over at least 18 months. The protocol allows for subjects who receive a study drug that is not effective to receive subsequent treatments within the context of the study. Medication dosages are adjusted within a defined range according to clinical judgment. The primary outcome is all-cause treatment discontinuation because it represents an important clinical endpoint that reflects both clinician and patient judgments about efficacy and tolerability. Secondary outcomes include symptoms, side effects, neurocognitive functioning, and cost-effectiveness. Approximately 50 clinical sites across the United States are seeking to enroll a total of 1,500 persons with schizophrenia. Phase 1 is a double-blinded randomized clinical trial comparing treatment with the second generation antipsychotics olanzapine, quetiapine, risperidone, and ziprasidone to perphenazine, a midpotency first generation antipsychotic. If the initially assigned medication is not effective, subjects may choose one of the following phase 2 trials: (1) randomization to open-label clozapine or a double-blinded second generation drug that was available but not assigned in phase 1; or (2) double-blinded randomization to ziprasidone or another second generation drug that was available but not assigned in phase 1. If the phase 2 study drug is discontinued, subjects may enter phase 3, in which clinicians help subjects select an open-label treatment based on individuals' experiences in phases 1 and 2. Background: Despite the availability of psychosocial evidence-based practices (EBPs), treatment and outcomes for persons with mental disorders remain suboptimal. Replicating Effective Programs (REP), an effective implementation strategy, still resulted in less than half of sites using an EBP. The primary aim of this cluster randomized trial is to determine, among sites not initially responding to REP, the effect of adaptive implementation strategies that begin with an External Facilitator (EF) or with an External Facilitator plus an Internal Facilitator (IF) on improved EBP use and patient outcomes in 12 months. Methods Design: This study employs a sequential multiple assignment randomized trial (SMART) design to build an adaptive implementation strategy. The EBP to be implemented is life goals (LG) for patients with mood disorders across 80 community-based outpatient clinics (N = 1,600 patients) from different U.S. regions. Sites not initially responding to REP (defined as <50 patients receiving ≥3 EBP sessions) will be randomized to receive additional support from an EF or both EF IF. Additionally, sites randomized to EF and still not responsive will be randomized to continue with EF alone or to receive EF IF. The EF provides technical expertise in adapting LG in routine practice, whereas the on-site IF has direct reporting relationships to site leadership to support LG use in routine practice. The primary outcome is mental health-related quality of life; secondary outcomes include receipt of LG sessions, mood symptoms, implementation costs, and organizational change. We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients be initially randomized among four chemotherapies, and that those who responded poorly be re-randomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs’ overall success rates, with success defined by the occurrence of successful responses in each of two consecutive courses of the patient’s therapy. Of the 150 study participants, 47 did not complete their therapy as per the algorithm. However, 35 of them did so for reasons that precluded further chemotherapy, that is, toxicity and or progressive disease. Consequently, rather than comparing the overall success rates of the DTRs in the unrealistic event that these patients had remained on their assigned chemotherapies, we conducted an analysis that compared viable switch rules defined by the per-protocol rules but with the additional provision that patients who developed toxicity or progressive disease switch to a non-prespecified therapeutic or palliative strategy. This modification involved consideration of bivariate per-course outcomes encoding both efficacy and toxicity. We used numerical scores elicited from the trial’s principal investigator to quantify the clinical desirability of each bivariate per-course outcome, and defined one endpoint as their average over all courses of treatment. Two other simpler sets of scores as well as log survival time were also used as endpoints. Estimation of each DTR-specific mean score was conducted using inverse probability weighted methods that assumed that missingness in the 12 remaining dropouts was informative but explainable in that it only depended on past recorded data. We conducted additional worst- and best-case analyses to evaluate sensitivity of our findings to extreme departures from the explainable dropout assumption.