Stratification of summary statistic tests according to missing data patterns.

Abstract

Summary statistics, such as slope or area under the time-response curve, reduce the dimensionality of repeated measures data and can thereby simplify the comparison of groups in longitudinal studies. Since summary statistic distributions vary according to the amount, timing, and type of any missingness that occurs, one must choose between analysing the data unconditionally or conditionally on the missingness patterns. This paper uses simulations to compare such unstratified and stratified summary statistic analyses with respect to their size and power under models that allow for both non-informative and informative missingness mechanisms. Of particular interest is the robustness of these methods to violations of the assumptions that one must make if they are to have proper test size. It is found that stratification of the analysis tends to result in an increase of power, and improves the robustness to violations of missing data assumptions.