A Bias Correction in Testing Treatment Efficacy under Informative Dropout in Clinical Trials
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*Fanhui Kong, FDA 


In clinical trials of drug development, patients are often followed for a certain period of time, and the outcome variables are measured at scheduled time intervals. In such trials, patient dropout is often the major source for missing data. In this talk, for a time-saturated treatment effect model and an informative dropout scheme that depends on the unobserved outcomes only through the random-coefficients, we propose a grouping method to correct the biases in the estimation of treatment effect. In a simulation study, we compare the new method with the traditional methods of the observed case (OC) analysis, the last-observation-carried-forward (LOCF) analysis, and the mixed-model-repeated-measurement (MMRM) approach, and find it improves the current methods and gives more stable results in the treatment efficacy inferences.