Alternative weighting schemes when performing matching-adjusted indirect comparisons.

Abstract

Methods for indirect comparisons and network meta-analysis use aggregate level data from multiple studies. A very common, and closely related, scenario is where a company has individual patient data (IPD) from its own trial, but only has published aggregate data from a competitor's trial, and an indirect comparison of the treatments evaluated in these two trials is required. Matching-Adjusted Indirect Comparison (MAIC) has been developed for this situation, where we use the available IPD to adjust for between-trial imbalances in the distributions of observed baseline covariates between the two trials. We extend the current MAIC methodology, where we compute the weights that satisfy the conventional method of moments and result in the largest possible effective sample size (ESS). We show that the approach proposed by Zubizarreta in a previous study can be used for this purpose. We derive a new analytical result that shows why this alternative approach provides a larger ESS than a conventional MAIC. We also derive a new formula for the maximum ESS that can be achieved, even when permitting negative weights, when adjusting for one covariate. This can be used as an easily computed new metric that quantifies the difficulty in adjusting for covariates. What is already known: MAIC is an established way to perform population adjustment in the situation where IPD is available from one trial but only aggregate level data is available from another trial, and an indirect comparison is required. However the effective sample size (ESS) can be small after making the adjustment. What is new: We show that an alternative method can result in a larger ESS. We provide new analytical results showing why this is the case. We derive a new descriptive statistic that is based on maximising the ESS that quantifies the difficulties in adjusting for particular covariates. Potential impact for RSM readers outside the authors' field: Reweighting methods for population adjustment are becoming more commonly used and their implications for research synthesis methodology is now considerable. This paper provides important new links between the theoretical literature, and the more applied research synthesis methodology literature, relating to this topic.