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.
Adaptive treatment strategies have been applied to treat depression, with the STAR*D (sequenced treatment alternatives to relieve depression) treatment @cite_21 , in which patients who did not respond to less-intensive therapies were randomly assigned to more intensive treatments at higher levels; to treat schizophrenia, with the CATIE (clinical antipsychotic trials of intervention effectiveness) design @cite_6 , a three-phase study where patients were randomly assigned to new treatments at successive phases if they did not respond to earlier treatments; to treat advanced prostate cancer @cite_34 by randomizing nonfavorably-responding patients to untried chemotherapy treatments at eight-week intervals, up to four times; and many other medical settings ( smoking cessation @cite_7 , pediatric generalized anxiety disorders @cite_18 , and mood disorders @cite_29 @cite_2 ).
@cite_18 @cite_7 @cite_29 @cite_21 @cite_6 @cite_2 @cite_34
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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. 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. 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. State-of-the-art methods for optimizing cancer treatment over several weeks of external beam radiotherapy take a static–deterministic view of the treatment planning process, mainly focusing on spatial distribution of dose. Recent progress in quantitative functional imaging as well as mathematical models of tumor response to radiotherapy is increasingly enabling treatment planners to monitor predict a patient’s biological response over weeks of treatment. In this paper we introduce dynamic biologically conformal radiation therapy (DBCRT), a mathematical framework intended to exploit these emerging technological and biological modeling advances to design patient-specific radiation treatment strategies that dynamically adapt to the spatiotemporal evolution of a patient’s biological response over several treatment sessions in order to achieve the best possible health outcome. More specifically, we propose a discrete-time stochastic control formalism where we use the patient’s biological condition to model the system state and the beam intensities as controls. Three approximate control schemes are then applied and compared for efficiency. Numerical simulations on test cases show that DBCRT results in a 64–98 improvement in treatment efficacy as compared to the more conventional static–deterministic approach.