236,527 research outputs found
Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer\u27s disease.
Introduction: Study outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the run-in period. However, it has not been established how best to incorporate run-in data into the primary analysis of the trial.
Methods: We proposed two-period (run-in period and randomization period) linear mixed effects models to simultaneously model the run-in data and the postrandomization data.
Results: Compared with the traditional models, the two-period linear mixed effects models can increase the power up to 15% and yield similar power for both unequal randomization and equal randomization.
Discussion: Given that analysis of run-in data using the two-period linear mixed effects models allows more participants (unequal randomization) to be on the active treatment with similar power to that of the equal-randomization trials, it may reduce the dropout by assigning more participants to the active treatment and thus improve the efficiency of AD clinical trials
Sequential monitoring with conditional randomization tests
Sequential monitoring in clinical trials is often employed to allow for early
stopping and other interim decisions, while maintaining the type I error rate.
However, sequential monitoring is typically described only in the context of a
population model. We describe a computational method to implement sequential
monitoring in a randomization-based context. In particular, we discuss a new
technique for the computation of approximate conditional tests following
restricted randomization procedures and then apply this technique to
approximate the joint distribution of sequentially computed conditional
randomization tests. We also describe the computation of a randomization-based
analog of the information fraction. We apply these techniques to a restricted
randomization procedure, Efron's [Biometrika 58 (1971) 403--417] biased coin
design. These techniques require derivation of certain conditional
probabilities and conditional covariances of the randomization procedure. We
employ combinatoric techniques to derive these for the biased coin design.Comment: Published in at http://dx.doi.org/10.1214/11-AOS941 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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