  Integrative analysis of datasets generated by multiple cohorts is a
widely-used approach for increasing sample size, precision of population
estimators, and generalizability of analysis results in epidemiological
studies. However, often each individual cohort dataset does not have all
variables of interest for an integrative analysis collected as a part of an
original study. Such cohort-level missingness poses methodological challenges
to the integrative analysis since missing variables have traditionally: (1)
been removed from the data for complete case analysis; or (2) been completed by
missing data interpolation techniques using data with the same covariate
distribution from other studies. In most integrative-analysis studies, neither
approach is optimal as it leads to either loosing the majority of study
covariates or challenges in specifying the cohorts following the same
distributions. We propose a novel approach to identify the studies with same
distributions that could be used for completing the cohort-level missing
information. Our methodology relies on (1) identifying sub-groups of cohorts
with similar covariate distributions using cohort identity random forest
prediction models followed by clustering; and then (2) applying a recursive
pairwise distribution test for high dimensional data to these sub-groups.
Extensive simulation studies show that cohorts with the same distribution are
correctly grouped together in almost all simulation settings. Our methods'
application to two ECHO-wide Cohort Studies reveals that the cohorts grouped
together reflect the similarities in study design. The methods are implemented
in R software package relate.
