Two-stage matching-adjusted indirect comparison

TL;DR

This paper introduces 2SMAIC, a two-stage extension of MAIC, which improves precision and efficiency in covariate-adjusted indirect comparisons by modeling treatment and trial assignment mechanisms, especially effective with small sample sizes.

Contribution

The paper proposes a novel two-stage modeling approach, 2SMAIC, that enhances covariate balancing and efficiency over traditional MAIC in indirect treatment comparisons.

Findings

2SMAIC improves precision and efficiency over MAIC.

The method is effective with small sample sizes in IPD trials.

Truncation can reduce bias but may decrease efficiency.

Abstract

Anchored covariate-adjusted indirect comparisons inform reimbursement decisions where there are no head-to-head trials between the treatments of interest, there is a common comparator arm shared by the studies, and there are patient-level data limitations. Matching-adjusted indirect comparison (MAIC) is the most widely used covariate-adjusted indirect comparison method. MAIC has poor precision and is inefficient when the effective sample size after weighting is small. A modular extension to MAIC, termed two-stage matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two parametric models. One estimates the treatment assignment mechanism in the study with individual patient data (IPD), the other estimates the trial assignment mechanism. The resulting weights seek to balance covariates between treatment arms and across studies. A simulation study provides proof-of-principle in an indirect comparison performed across two randomized trials and explores the use of weight truncation in combination with MAIC for the first time. Despite enforcing randomization and knowing the true treatment assignment mechanism in the IPD trial, 2SMAIC yields improved precision and efficiency with respect to MAIC in all scenarios, while maintaining similarly low levels of bias. The two-stage approach is effective when sample sizes in the IPD trial are low, as it controls for chance imbalances in prognostic baseline covariates between study arms. It is not as effective when overlap between the trials' target populations is poor and the extremity of the weights is high. In these scenarios, truncation leads to substantial precision and efficiency gains but induces considerable bias. The combination of a two-stage approach with truncation produces the highest precision and efficiency improvements.