Journal of biopharmaceutical statistics
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Comparative Study
A profile analysis approach using MMRM in acute schizophrenia: a comparison to some traditional approaches.
The analysis of schizophrenia studies is plagued by inefficiency and bias due to much missing data. Mixed-effect models for repeated measures designs help address these problems, but to gain even more efficiency it is desirable to judiciously use additional longitudinal data in such designs by comparing treatment groups over multiple time points. Simulations were conducted to compare a profile analysis approach to other commonly used analysis methods in the presence of data missing at random. One gains efficiency by using a composite contrast over multiple time points when the treatment effect over the time points is not substantially different.
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This article discusses the problem of selecting free parameters of multiple testing procedures in confirmatory Phase III clinical trials with multiple objectives, including hypothesis weights and hypothesis ordering. We identify classes of multiple testing procedures that provide different interpretations of these parameters. ⋯ We examine the behavior of different classes of multiple testing procedures in problems with unequally weighted hypotheses and a priori ordered hypotheses and provide practical guidelines for the choice of hypothesis weights and hypothesis ordering. The concepts discussed in the article are illustrated using case studies based on clinical trials with multiple endpoints, multiple dose-placebo comparisons, and multiple patient populations.
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In clinical trials, multiple outcomes are often collected in order to simultaneously assess effectiveness and safety. We develop a Bayesian procedure for determining the required sample size in a regression model where a continuous efficacy variable and a binary safety variable are observed. ⋯ The model accounts for correlation between the two variables. Through examples we demonstrate that savings in total sample size are possible when the correlation between these two variables is sufficiently high.
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In longitudinal clinical trials for drug development, the study objective is often to evaluate overall treatment effect across all visits. Despite careful planning and study conduct, the occurrence of incomplete data cannot be completely eliminated. As a direct likelihood method, the mixed-effects model for repeated measures (MMRM) has become one of the preferred approaches for handling missing data in such designs. ⋯ When the underlying covariance is of an unstructured pattern, the optimal weighting method has increased power under MAR and missing-not-at-random (MNAR) mechanisms, and can lead to bias reduction under MNAR. This is especially true when the variance is greater at later time point, which could lead to a smaller weight. We present practical examples using the optimal weighting method to analyze two cystic fibrosis clinical trial data sets.
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In areas such as oncology, two-stage designs are often preferred as compared to one-stage designs due to the ability to stop the trial early when faced with evidence of lack of sufficient efficacy and the associated sample size savings. We present exact two-stage designs based on Barnard's exact test for differences in proportions and compare the designs to those proposed by Kepner ( 2010 ) and Jung ( 2010 ). In addition, we present tables of decision rules under a variety of assumed realities for use in trial planning. The procedure is recommended for use due to the substantial sample size savings experienced.