76 PART 2 Examining Tools and Processes

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 with-

out 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 vari-

ables 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.