Power calculations for delta-adjusted pattern-mixture models

Abstract

Pattern-mixture models provide a general and flexible framework for sensitivity analyses of nonignorable missing data in longitudinal studies. The delta-adjusted pattern-mixture models handle missing data in a clinically interpretable manner and have been used as sensitivity analyses addressing the effectiveness hypothesis, while a likelihood-based approach that assumes data are missing at random is often used as the primary analysis addressing the efficacy hypothesis. We describe a method for power calculations for delta-adjusted pattern-mixture model sensitivity analyses in confirmatory clinical trials. To apply the method, we only need to specify the pattern probabilities at postbaseline time points, the expected treatment differences at postbaseline time points, the conditional covariance matrix of postbaseline measurements given the baseline measurement, and the delta-adjustment method for the pattern-mixture model. We use an example to illustrate and compare various delta-adjusted pattern-mixture models and use simulations to confirm the analytic results.