Introduction

Until recently, guidelines for the analysis of clinical trial data provided only limited advice on how to handle missing data, and analytic approaches tended to be simple and ad hoc. The calculations required to estimate parameters from a balanced data set with the same number of patients in each treatment group at each assessment time are far easier than the calculations required when the numbers are not balanced, as is the case when patients drop out. Hence, the motivation behind early methods of dealing with missing data may have been as much to restore balance and foster computational feasibility in an era of limited computing power as to counteract the potential bias from the missing values.

However, with advances in statistical theory and in computing power that facilitates implementation of the theory, more principled approaches can now be easily implemented. This chapter begins with sections describing the simpler methods, including complete case analyses and single imputation methods such as last and baseline observation carried forward. Subsequent sections cover more principled methods, including multiple imputation, inverse probability weighting, and modeling approaches such as direct likelihood.