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Dropout Rates in Randomized Clinical Trials of Antipsychotics: A Meta-analysis Comparing First- and Second-Generation Drugs and an Examination of the Role of Trial Design Features
Author(s) -
Jonathan Rabinowitz,
Stephen Z. Levine,
Orna Barkai,
Ori Davidov
Publication year - 2008
Publication title -
schizophrenia bulletin
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.823
H-Index - 190
eISSN - 1745-1707
pISSN - 0586-7614
DOI - 10.1093/schbul/sbn005
Subject(s) - dropout (neural networks) , antipsychotic , randomized controlled trial , medicine , confidence interval , meta analysis , odds ratio , exacerbation , clinical trial , psychology , psychiatry , schizophrenia (object oriented programming) , computer science , machine learning
Dropout is often used as an outcome measure in clinical trials of antipsychotic medication. Previous research is inconclusive regarding (a) differences in dropout rates between first- and second-generation antipsychotic medications and (b) how trial design features reduce dropout. Meta-analysis of randomized controlled trials (RCTs) of antipsychotic medication was conducted to compare dropout rates for first- and second-generation antipsychotic drugs and to examine how a broad range of design features effect dropout. Ninety-three RCTs that met inclusion criteria were located (n = 26 686). Meta-analytic random effects models showed that dropout was higher for first- than second-generation drugs (odds ratio = 1.49, 95% confidence interval: 1.31-1.66). This advantage persisted after removing study arms with excessively high dosages, in flexible dose studies, studies of patients with symptom exacerbation, nonresponder patients, inpatients, and outpatients. Mixed effects models for meta-analysis were used to identify design features that effected dropout and develop formulae to derive expected dropout rates based on trial design features, and these assigned a pivotal role to duration. Collectively, dropout rates are lower for second- than first-generation antipsychotic drugs and appear to be partly explained by trial design features thus providing direction for future trial design.

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