important part of good clinical-trials practice.” Therefore, an important objective in the design and implementation of a clinical trial is to minimize missing outcome data.
As briefly discussed in Chapter 1, there are a variety of reasons for discontinuation of treatment, and for discontinuation of data collection, which we refer to as “analysis dropout,” in clinical trials. The frequency of missing data depends on the health condition under study, the nature of the interventions under consideration, the length of the trial, and the burden of the health evaluations and how much they are facilitated. Common reasons for dropout include (1) inability to tolerate the intervention, (2) lack of efficacy for the intervention, and (3) difficulty or inability to attend clinical appointments and complete medical evaluations. As noted in Chapter 1, in some trials, treatment dropout leads to analysis dropout because data collection is discontinued. In many studies, this is the major reason for missing data. Other reasons include subjects who withdraw their consent, move out of the area, or who otherwise experience changes in their lives that preclude or complicate further participation.
This chapter primarily concerns the sources of missing outcome information and how the frequency of missing outcome values can be reduced. However, missing values of covariates and other auxiliary variables that are predictive of the outcome of interest should also be reduced,1 and the techniques discussed here can be helpful for that purpose as well. There is clearly a need for more research on the specific reasons underlying missing data, a topic addressed in Chapter 3.
A clinical trial typically measures outcomes that quantify the impact of the interventions under study for a defined period of time. Inference focuses on summaries of these measures (such as the mean) for the target population of interest. These summary quantities are often called parameters, or estimands. For example, consider a trial in which the primary outcome measure is change in blood pressure between baseline and 6 weeks after the initiation of treatment. The estimand of interest might be the difference in the mean change in blood pressure over 6 weeks for the target and control populations. An estimate of this parameter is the difference in sample means for participants in the treatment group and participants in the control group. This estimate is unbiased if the assignment to treatment is random, and there are no missing data (Little and Rubin, 2002). The goal