  Data sharing barriers are paramount challenges arising from multicenter
clinical trials where multiple data sources are stored in a distributed fashion
at different local study sites. Merging such data sources into a common data
storage for a centralized statistical analysis requires a data use agreement,
which is often time-consuming. Data merging may become more burdensome when
causal inference is of primary interest because propensity score modeling
involves combining many confounding variables, and systematic incorporation of
this additional modeling in meta-analysis has not been thoroughly investigated
in the literature. We propose a new causal inference framework that avoids the
merging of subject-level raw data from multiple sites but needs only the
sharing of summary statistics. The proposed collaborative inference enjoys
maximal protection of data privacy and minimal sensitivity to unbalanced data
distributions across data sources. We show theoretically and numerically that
the new distributed causal inference approach has little loss of statistical
power compared to the centralized method that requires merging the entire data.
We present large-sample properties and algorithms for the proposed method. We
illustrate its performance by simulation experiments and a real-world data
example on a multicenter clinical trial of basal insulin treatment for reducing
the risk of post-transplantation diabetes among kidney-transplant patients.
