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Population-Adjusted Indirect Treatment Comparison with th...
[Submitted on 12 Jan 2026 (v1), last revised 9 Jun 2026 (this ve · 2026-06-10 · via stat updates on arXiv.org

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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Indirect Comparison (MAIC) exist to adjust for these cross-trial differences, they are increasingly being superseded by regression-based marginalization methods. Historically, software implementations for these methods 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). outstandR implements advanced G-computation methods - within both maximum likelihood and Bayesian frameworks, and Multiple Imputation Marginalization (MIM) to address non-collapsibility. By streamlining the workflow of covariate simulation, model standardization, and contrast estimation, outstandR enables robust and compatible evidence synthesis in complex decision-making scenarios.

Submission history

From: Nathan Green Dr [view email]
[v1] Mon, 12 Jan 2026 13:33:15 UTC (128 KB)
[v2] Thu, 15 Jan 2026 15:36:18 UTC (127 KB)
[v3] Tue, 9 Jun 2026 13:40:11 UTC (124 KB)

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