Multiple analyses in clinical trials: sound science or data dredging?

Clinical trials typically require the collection of many data to describe the participants and for measuring their response to an intervention. In addition to the primary analysis of treatment effect, investigators can use these data to perform multiple analyses, but there are important pitfalls with their use.1,2 Here, we discuss three common types of secondary analyses: analyses of multiple outcome variables; analyses of trial outcomes that account for prognostic factors (adjusted analyses); and using trial data to answer secondary research questions (see definitions in Box 1). The use of trial data for population subgroup analyses has been discussed earlier in this series.3,4