Evaluating Data from Closely Related Clinical Trials

Abstract

In the drug development process, clinical trials are conducted for different diseases or indications. Generally, the assessment of drug effectiveness is performed by only utilizing data from clinical trials within a particular indication. However, in some cases, different indications are closely related. They may be etiologically or pathophysiologically similar. Even if the indications are less closely related, the general purpose of therapy may be very similar. Evaluating data from all of these related studies together enables researchers to access the information, and give a systematic overview of the treatment effect in a broader scope. In this paper, we use an example in the allergy therapeutic area to illustrate a Bayesian approach to synthesizing evidence from trials aimed at closely related indications. This approach accounts for the heterogeneity of treatment effects across different indications as well as across different studies. It also allows us to quantify the treatment benefit difference between different indications in a more meaningful way. Demonstration of beneficial drug effects on different indications in the context of all related data can cross-substantiate a claim of effectiveness for each indication and hence strengthen the evidence of treatment efficacy.