Schizophrenia trials: past, present and future

Adams, Clive E

doi:10.1017/s1121189x00005649

Cited by 5 publications

(5 citation statements)

References 53 publications

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“…Of the 705 participants who left the CATIE study, 509 dropped out of the study before entering stage 2 (issue 4 and 5 in the preceding text). Dropout is generally high in studies of antipsychotics for schizophrenia . Additionally, in the CATIE study, the primary outcome was time until all‐cause treatment discontinuation, providing little incentive for clinicians to keep nonadherent patients enrolled.…”

Section: Introductionmentioning

confidence: 99%

A multiple imputation strategy for sequential multiple assignment randomized trials

et al. 2014

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Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient’s health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient-specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges, and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness (CATIE), one of the most well-known SMARTs to date.

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Section: Introductionmentioning

confidence: 99%

A multiple imputation strategy for sequential multiple assignment randomized trials

et al. 2014

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“…Dropout in studies of antipsychotics in patients with schizophrenia is often high: between 30% and 60% (Adams, 2002). Of the 1076 patients who were included in our analysis 429 (40%) dropped out of the CATIE study before month 12.…”

Section: Methodsmentioning

confidence: 99%

Estimating the Optimal Dynamic Antipsychotic Treatment Regime: Evidence from the Sequential Multiple-Assignment Randomized Clinical Antipsychotic Trials of Intervention and Effectiveness Schizophrenia Study

Shortreed

Moodie

2012

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Treatment of schizophrenia is notoriously difficult and typically requires personalized adaption of treatment due to lack of efficacy of treatment, poor adherence, or intolerable side effects. The Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) Schizophrenia Study is a sequential multiple assignment randomized trial comparing the typical antipsychotic medication, perphenazine, to several newer atypical antipsychotics. This paper describes the marginal structural modeling method for estimating optimal dynamic treatment regimes and applies the approach to the CATIE Schizophrenia Study. Missing data and valid estimation of confidence intervals are also addressed.

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“…The illness places a high level of burden on individuals afflicted with the disease, as well as those who care for them. In studies of antipsychotics in patients with schizophrenia, dropout is often high, between 30 and 60 % (Adams, 2002). CATIE was designed to be an 18 month longitudinal study with 1460 participants randomized to stage 1 treatment.…”

Section: Catie a Smart Studymentioning

confidence: 99%

“…A recent overview of studies involving patients with schizophrenia revealed that the average number of people in such trials was 62 (a median of 60). A mere 58 out of 1,941 trials had sample sizes larger than 300 (Adams, 2002). In light of this, with a sample size of 1460, we can consider CATIE a large randomized trial, especially in the field of schizophrenia research.…”

Section: Catie a Smart Studymentioning

confidence: 99%

Informing sequential clinical decision-making through reinforcement learning: an empirical study

et al. 2010

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This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of a highly variable observation set. Finally, we discuss approaches to summarizing the evidence in the data for recommending a particular action and quantifying the uncertainty around the Q-function of the recommended policy. We present the results of applying these methods to real clinical trial data of patients with schizophrenia.

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Schizophrenia trials: past, present and future

Cited by 5 publications

References 53 publications

A multiple imputation strategy for sequential multiple assignment randomized trials

A multiple imputation strategy for sequential multiple assignment randomized trials

Estimating the Optimal Dynamic Antipsychotic Treatment Regime: Evidence from the Sequential Multiple-Assignment Randomized Clinical Antipsychotic Trials of Intervention and Effectiveness Schizophrenia Study

Informing sequential clinical decision-making through reinforcement learning: an empirical study

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