Population-Adjusted Indirect Treatment Comparison with the outstandR Package in R

TL;DR

This paper introduces outstandR, an R package that offers a comprehensive, unified framework for population-adjusted indirect treatment comparisons, integrating advanced methods to improve evidence synthesis in health technology assessments.

Contribution

The paper presents outstandR, an R package that consolidates various population-adjusted indirect comparison methods, including G-computation and MIM, into a flexible, user-friendly tool for HTA applications.

Findings

outstandR enables robust covariate adjustment in indirect comparisons

The package integrates Bayesian and maximum likelihood G-computation methods

It streamlines workflows for complex evidence synthesis

Abstract

Indirect treatment comparisons (ITCs) are essential in Health Technology Assessment (HTA) when head-to-head clinical trials are absent. A common challenge arises when attempting to compare a treatment with available individual patient data (IPD) against a competitor with only reported aggregate-level data (ALD), particularly when trial populations differ in effect modifiers. While methods such as Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC) exist to adjust for these cross-trial differences, software implementations have often been fragmented or limited in scope. This article introduces outstandR, an R package designed to provide a comprehensive and unified framework for population-adjusted indirect comparison (PAIC). Beyond standard weighting and regression approaches, outstandR implements advanced G-computation methods within both maximum likelihood and Bayesian frameworks, and Multiple Imputation Marginalization (MIM) to address non-collapsibility and missing data. By streamlining the workflow of covariate simulation, model standardization, and contrast estimation, outstandR enables robust and compatible evidence synthesis in complex decision-making scenarios.