Explore the vast range of titles available.

Background In June 2013, a 1-day workshop on Dynamic Treatment Strategies (DTSs) and Sequential Multiple Assignment Randomized Trials (SMARTs) was held at the University of Pennsylvania in Philadelphia, Pennsylvania. These two linked topics have generated a great deal of interest as researchers have recognized the importance of comparing entire strategies for managing chronic disease. A number of articles emerged from that workshop. Purpose The purpose of this survey of the DTS/SMART methodology (which is taken from the introductory talk in the workshop) is to provide the reader the collected articles presented in this volume with sufficient background to appreciate the more detailed discussions in the articles. Methods The way that the DTS arises naturally in clinical practice is described, along with its connection to the well-known difficulties of interpreting the analysis by intention-to-treat. The SMART methodology for comparing DTS is described, and the basics of estimation and inference presented. Results The DTS/SMART methodology can be a flexible and practical way to optimize ongoing clinical decision making, providing evidence (based on randomization) for comparative effectiveness. Limitations The DTS/SMART methodology is not a solution for unstandardized study protocols. Conclusions The DTS/SMART methodology has growing relevance to comparative effectiveness research and the needs of the learning healthcare system.

Animal research suggests that medications that produce a weak dopamine D2 receptor blockade and potentiate noradrenergic activity may decrease alcohol drinking. In an open-label pilot study of subjects with alcohol dependence, we tested whether the combination of quetiapine, a weak dopamine D2 receptor antagonist, whose primary metabolite, desalkylquetiapine, is a norepinephrine reuptake inhibitor, and mirtazapine, a potent α2 norepinephrine receptor antagonist, would decrease alcohol drinking and craving. Twenty very heavy drinkers with alcohol dependence entered a trial of 8 weeks of treatment with quetiapine followed by 8 weeks of treatment with a combination of quetiapine plus mirtazapine. Alcohol use was assessed weekly with a Timeline Follow-Back interview and craving with the Penn Alcohol Craving Scale. Among the 11 completers, subjects reported improved outcomes in the quetiapine plus mirtazapine period compared to the quetiapine alone period: fewer very heavy drinking days per week (1.3 [SD = 2.4] vs. 2.1 [SD = 2.8]; t = 2.3, df = 10, p = 0.04); fewer total number of drinks per week (39.7 [SD = 61.6] vs. 53.4 [SD = 65.0]; t = 2.8, df = 10, p = 0.02); and lower craving scores (2.5 [SD = 1.4] vs. 3.2 [SD = 1.2]; t = 2.4, df = 10, p = 0.04). All subjects reported at least one adverse event; 72.7% reported somnolence. In this open-label pilot study, treatment with quetiapine plus mirtazapine was associated with a decrease in alcohol drinking and craving. These findings are consistent with our previous work in animal models of alcohol use disorders and suggest that further study of medications or combinations of medications with this pharmacologic profile is warranted.

Clinical management of chronic disease requires a dynamic treatment regime (DTR): rules for choosing the new treatment based on the history of response to past treatments. Estimating and comparing the effects of DTRs from a sample of observed trajectories of treatment and outcome depends on the untestable assumption that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date (\"sequential ignorability\"). In longitudinal observational studies, sequential ignorability must be assumed, while randomization of dynamic regimes can guarantee it. Using several clinical examples, we describe the simplest randomized experimental designs for comparing DTRs. We begin by considering an initial treatment A and a second treatment B, and discuss how a dynamic treatment regime that starts with A and leads (sometimes) to B, might be compared to either fixed treatment A or B. We also illustrate the problem of finding the optimal sequence of treatments in a DTR, when there are several choices. We describe and contrast two ways of incorporating randomization into studies to compare such regimes: baseline randomization among DTRs versus randomization at the decision points (sequentially randomized designs). We discuss estimation and inference from both baseline randomized and sequentially randomized designs and conclude with a discussion of the differences between the experimental and observational approaches to optimizing and comparing dynamic treatment regimes.

Despite high revalence rates of intimate partner violence in the lives of extremely poor women with dependent children, few studies have investigated the patterns of violence that occur over time, and the characteristics of women that serve as risk markers for partner violence. This paper describes patterns of domestic violence longitudinally and uses multivariate analyses to delineate childhood and adult risk markers for recent intimate partner violence in this population of women. Analyses draw upon a sample of 436 homeless and extremely poor housed mothers receiving welfare, in a mid-sized city in Massachusetts with a large Hispanic population of Puerto Rican descent and relatively fewer Blacks. We found that among women with complete longitudinal data (N = 280), almost two-thirds experienced intimate partner violence at some point during their adult life by the end of study follow-up, and that the abuse before and after the baseline interview was episodic and limited over time. To examine the role of individual women's factors, while controlling for partner characteristics, we used baseline data on women who had been partnered during the past year (N= 336). Among childhood predictors, we found that sexual molestation contributed most significantly to adult intimate partner violence that occurred during the past year prior to the baseline interview. Adult risk markers included inadequate emotional support from non-professionals, poor self-esteem, and a partner with substance abuse problems. Having a partner with poor work history was another independent predictor of recent abuse. Ethnicity did not significantly predict whether women were abused or not during the past year, contrary to other findings reported in the literature. Adapted from the source document.

Background Sequential multiple assignment randomized (SMAR) designs are used to evaluate treatment policies, also known as adaptive treatment strategies (ATS). The determination of SMAR sample sizes is challenging because of the sequential and adaptive nature of ATS, and the multi-stage randomized assignment used to evaluate them. Purpose We derive sample size formulae appropriate for the nested structure of successive SMAR randomizations. This nesting gives rise to ATS that have overlapping data, and hence between-strategy covariance. We focus on the case when covariance is substantial enough to reduce sample size through improved inferential efficiency. Methods Our design calculations draw upon two distinct methodologies for SMAR trials, using the equality of the optimal semi-parametric and Bayesian predictive estimators of standard error. This ‘hybrid’ approach produces a generalization of the t-test power calculation that is carried out in terms of effect size and regression quantities familiar to the trialist. Results Simulation studies support the reasonableness of underlying assumptions as well as the adequacy of the approximation to between-strategy covariance when it is substantial. Investigation of the sensitivity of formulae to misspecification shows that the greatest influence is due to changes in effect size, which is an a priori clinical judgment on the part of the trialist. Limitations We have restricted simulation investigation to SMAR studies of two and three stages, although the methods are fully general in that they apply to ‘K-stage’ trials. Conclusions Practical guidance is needed to allow the trialist to size a SMAR design using the derived methods. To this end, we define ATS to be ‘distinct’ when they differ by at least the (minimal) size of effect deemed to be clinically relevant. Simulation results suggest that the number of subjects needed to distinguish distinct strategies will be significantly reduced by adjustment for covariance only when small effects are of interest.

The principle of analysis by intent-to-treat (ITT) serves as the standard basis for design decisions as well as choice of analysis in clinical trials. ITT correctly contrasts the pragmatic consequences of the treatments offered in a study, as long as the study protocol accurately reflects the realities of clinical practice. We identify the study of ongoing treatment for chronic disease as the clinical context that most strains the ITT principle. In a placebo-controlled trial of a new drug in patients with a condition for which there are standard treatments, the ethical requirement to “rescue”patients who do poorly, and who might be taking placebo, causes “drop-in” from placebo to a standard treatment. We propose that this problem reflects a lack of fit between the standard fixed design and clinical reality, rather than a weakness of ITT. We propose that the adaptive nature of clinical decision making should be captured in the design of trials, and we show how the ITT principle can be used in such designs.

Background Clinical management of chronic disease requires a dynamic treatment regime (DTR): rules for choosing the new treatment based on the history of response to past treatments. Estimating and comparing the effects of DTRs from a sample of observed trajectories of treatment and outcome depends on the untestable assumption that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date (“sequential ignorability”). In longitudinal observational studies, sequential ignorability must be assumed, while randomization of dynamic regimes can guarantee it. Methods Using several clinical examples, we describe the simplest randomized experimental designs for comparing DTRs. We begin by considering an initial treatment A and a second treatment B, and discuss how a dynamic treatment regime that starts with A and leads (sometimes) to B, might be compared to either fixed treatment A or B. We also illustrate the problem of finding the optimal sequence of treatments in a DTR, when there are several choices. We describe and contrast two ways of incorporating randomization into studies to compare such regimes: baseline randomization among DTRs versus randomization at the decision points (sequentially randomized designs). Conclusions We discuss estimation and inference from both baseline randomized and sequentially randomized designs and conclude with a discussion of the differences between the experimental and observational approaches to optimizing and comparing dynamic treatment regimes.

Alcohol use disorder, characterized by modest levels of alcohol use, commonly occurs in patients with schizophrenia and dramatically worsens their course. Recent data indicate that the atypical antipsychotic clozapine, but not the typical antipsychotic haloperidol, decreases alcohol drinking both in patients with schizophrenia and also in the Syrian golden hamster, an animal model of moderate alcohol drinking. The present study was designed to assess the comparative effects of clozapine and haloperidol in the alcohol-preferring (P) rat, an animal model of alcoholism. First, the study investigated the comparative effects of clozapine and haloperidol on initiation of alcohol consumption in P rats, which models the early stage of alcoholism. Second, the study assessed the comparative effects of clozapine and haloperidol on maintenance of chronic alcohol consumption in P rats to provide a clue as to whether either drug may also limit alcohol consumption in alcohol-dependent patients. Clozapine attenuated the initiation of alcohol drinking and development of alcohol preference while haloperidol did not. However, neither clozapine nor haloperidol attenuated maintenance of chronic alcohol drinking. Taken together, the current data suggest that clozapine, but not haloperidol, may be effective at reducing alcohol abuse or non-dependent drinking and the P rat, used within an alcohol initiation paradigm, and may differentiate the effects of clozapine and haloperidol on alcohol drinking.

We propose a method for assigning treatment in clinical trials, called the 'biased coin adaptive within-subject' (BCAWS) design: during the course of follow-up, the subject's response to a treatment is used to influence the future treatment, through a 'biased coin' algorithm. This design results in treatment patterns that are closer to actual clinical practice and may be more acceptable to patients with chronic disease than the usual fixed trial regimens, which often suffer from drop-out and non-adherence. In this work, we show how to use the BCAWS design to compare treatment strategies, and we provide a simple example to illustrate the method.

OBJECTIVES: To identify risk and protective factors for family homelessness, a case-control study of homeless and low-income, never-homeless families, all female-headed, was conducted. METHODS: Homeless mothers (n = 220) were enrolled from family shelters in Worcester, Mass. Low-income housed mothers receiving welfare (n = 216) formed the comparison group. The women completed an interview covering socioeconomic, social support, victimization, mental health, substance use, and health domains. RESULTS: Childhood predictors of family homelessness included foster care placement and respondent's mother's use of drugs. Independent risk factors in adulthood included minority status, recent move to Worcester, recent eviction, interpersonal conflict, frequent alcohol or heroin use, and recent hospitalization for a mental health problem. Protective factors included being a primary tenant, receiving cash assistance or a housing subsidy, graduating from high school, and having a larger social network. CONCLUSIONS: Factors that compromise an individual's economic and social resources are associated with greater risk of losing one's home.