cal trial data or for the subsequent sensitivity analysis that are described in the next chapter can be widely used, either at the U.S. Food and Drug Administration (FDA) or by trial sponsors, unless they are made available in one or more of the standard statistical software packages. It is beyond the scope of this report to describe and review specific software packages or routines. Many of the commonly used commercial and open-source packages used in the analysis of trials for the regulatory setting (SAS, SPSS, Stata, and R) allow for the analysis of incomplete data, using methods such as direct likelihood, Bayesian analysis, generalized estimating equations, inverse probability weighting, and multiple imputation.
Statistical software is evolving at a rapid pace to keep up with new developments in methodology and to implement proven methods. However, although progress is being made, the current suite of available tools remain lacking regarding augmented inverse probability weighting (IPW), missing not at random (MNAR) models, and analysis of the sensitivity to assumptions concerning the mechanism for missing outcome data. Given the urgency of the greater application of MNAR models and sensitivity analysis, we encourage the development and release of software tools to address these deficiencies. We again emphasize the importance of understanding and communicating the assumptions underlying analyses that are implemented in whatever software package is being used to draw inference about treatment effects. In most cases, communication of this information will necessitate referring to technical documentation for a specific analysis routine or procedure.
There is no universal method for handling incomplete data in a clinical trial. Each trial has its own set of design and measurement characteristics. There is, however, a set of six principles that can be applied in a wide variety of settings.
First, it needs to be determined whether missingness of a particular value hides a true underlying value that is meaningful for analysis. This may seem obvious but is not always the case. For example, consider a longitudinal analysis of CD4 counts in a clinical trial for AIDS. For subjects who leave the study because they move to a different location, it makes sense to consider the CD4 counts that would have been recorded if they had remained in the study. For subjects who die during the course of the study, it is less clear whether it is reasonable to consider CD4 counts after time of death as missing values.
Second, the analysis must be formulated to draw inference about an appropriate and well-defined causal estimand (see Chapter 2). The causal estimand should be defined in terms of the full data (i.e., the data that were