Efficient Integration of Aggregate Data and Individual Patient Data in One-Way Mixed Models

TL;DR

This paper introduces a method to efficiently combine aggregate data and individual patient data in meta-analyses using generalized linear models, optimizing data use when both data types are available.

Contribution

It proposes a novel approach for integrating AD and IPD in one-way mixed models, including a selection procedure for maximizing efficiency when combining data sources.

Findings

The method improves meta-analytic estimates by combining AD and IPD.

Efficiency of combined analysis depends on model design constraints.

A selection procedure ensures fully efficient integration of AD and IPD.

Abstract

Often both Aggregate Data (AD) studies and Individual Patient Data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta-analytic estimates of treatment effects. Moreover, often for some studies with AD, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We propose a method for combining treatment effects across trials when the response is from the exponential family of distribution and hence a generalized linear model structure can be used. We consider the case when treatment effects are fixed and common across studies. Using the proposed combination method, we evaluate the wisdom of choosing AD when IPD is available by studying the relative efficiency of analyzing all IPD studies versus combining various percentages of AD and IPD studies. For many different models design constraints under which the AD estimators are the IPD estimators, and hence fully efficient, are known. For such models we advocate a selection procedure that chooses AD studies over IPD studies in a manner that force least departure from design constraints and hence ensures a fully efficient combined AD and IPD estimator.