Handling missing data issues in clinical trials for rheumatic diseases

Abstract

Missing data are ubiquitous in clinical trials for rheumatic diseases, and it is important to accommodate them using appropriate statistical techniques. We review some of the basic considerations for missing data and survey a range of statistical techniques for analysis of longitudinal clinical trial data with missingness. Using clinical trial data from patients with diffuse systemic sclerosis, we show that different approaches to handling missing data can lead to different conclusions on the efficacy of the treatment. We then suggest how such discrepancies might be addressed. In particular, we emphasize that the commonly used method in rheumatic clinical trials of carrying the last observation forward to impute missing values should not be the primary analysis. We review software for analyzing different types of missing data and discuss our freely available software library for analyzing the more difficult but more realistic situation when the probability of dropout or missing data may depend on the unobserved missing value.