Joint integrative analysis of multiple data sources with correlated vector outcomes

TL;DR

This paper introduces a distributed quadratic inference framework for joint analysis of multiple data sources with correlated outcomes, improving efficiency and computational speed in estimating covariate effects.

Contribution

It develops a novel distributed estimation method combining quadratic inference functions and meta-estimation for correlated multi-source data analysis.

Findings

Method improves estimation efficiency.

Framework is computationally fast.

Successfully applied to multi-cohort study data.

Abstract

We propose a distributed quadratic inference function framework to jointly estimate regression parameters from multiple potentially heterogeneous data sources with correlated vector outcomes. The primary goal of this joint integrative analysis is to estimate covariate effects on all outcomes through a marginal regression model in a statistically and computationally efficient way. We develop a data integration procedure for statistical estimation and inference of regression parameters that is implemented in a fully distributed and parallelized computational scheme. To overcome computational and modeling challenges arising from the high-dimensional likelihood of the correlated vector outcomes, we propose to analyze each data source using Qu, Lindsay and Li (2000)'s quadratic inference functions, and then to jointly reestimate parameters from each data source by accounting for correlation between data sources using a combined meta-estimator in a similar spirit to Hansen (1982)'s generalised method of moments. We show both theoretically and numerically that the proposed method yields efficiency improvements and is computationally fast. We illustrate the proposed methodology with the joint integrative analysis of the association between smoking and metabolites in a large multi-cohort study and provide an R package for ease of implementation.