This article focuses on 2 objectives in the analysis of efficacy in long-term extension studies of chronic diseases: (1) defining and discussing estimands of interest in such studies and (2) evaluating the performance of several multiple imputation methods that may be useful in estimating some of these estimands. Specifically, 4 estimands are defined and their clinical utility and inferential ramifications discussed. The performance of several multiple imputation methods and approaches were evaluated using simulated data. Results suggested that when interest is in a binary outcome derived from an underlying continuous measurement, it is preferable to impute the underlying continuous value that is subsequently dichotomized rather than to directly impute the binary outcome. Results also demonstrated that multivariate Gaussian models with Markov chain Monte Carlo imputation and sequential regression have minimal bias and the anticipated confidence interval coverage, even in settings with ordinal data where departures from normality are a concern. These approaches are further illustrated using a long-term extension study in psoriasis.
Keywords: estimand; long-term extension study; multiple imputation.
© 2018 John Wiley & Sons, Ltd.