Sample size reduction by combining data from multiple studies investigating different subpopulations – Xynthia Kavelaars
We often find multiple studies investigating the same intervention on different groups of patients. Combining data from these trials allows us to share information about the effect of the intervention in a.) one of the specific patient groups; and/or b.) the general patient population. The aim of information sharing between trials is often twofold: A more comprehensive picture of treatment effects can be obtained, while potentially fewer participants have to be included. Naively merging datasets increases the risk of a false superiority conclusion however, so it is important to correct for differences between trials. The availability of covariates can be helpful to address this issue: If we know how subpopulations differ, we may include this information to increase information-sharing. This talk covers a procedure to combine data from two different subpopulations properly, and demonstrates how sample sizes may be reduced.