Chapter 11 covers dealing with multiplicity when making multiple comparisons. One approach

discussed in Chapter 11 is performing post-hoc tests following an ANOVA for comparing several

groups. Post-hoc tests incorporate a built-in adjustment to keep the overall α at only 5 percent across

all comparisons. This can be especially important when conducting an interim analysis, or an analysis

done before the official end of study data collection. But when you’re testing different hypotheses —

like when comparing different variables at different time points between different groups — you are

faced with some difficult decisions to make about reducing Type I error inflation.

In sponsored clinical trials, the sponsor and DSMB will weigh in on how they want to see

Type I error inflation controlled. If you are working on a clinical trial without a sponsor, you

should consult with another professional with experience in developing clinical trial analyses to

advise you on how to control your Type I error inflation given the context of your study.

Each time an interim analysis is conducted, a process called data close-out must occur. This

creates a data snapshot, and the last data snapshot from a data close-out process produces the

final analytic dataset, or dataset to be used in all analyses. Data close-out refers to the process

where current data being collected are copied into a research environment, and this copy is

edited to prepare it for analysis. These edits could include adding imputations, unblinding, or

creating other variables needed for analysis. The analytic dataset prepared for each interim

analysis and for final analysis should be stored with documentation, as decisions about stopping

or adjusting the trial are made based on the results of interim analyses.