dorsal/arxiv
View SchemaPopulation-Adjusted Indirect Treatment Comparison with the outstandR Package in R
| Authors | Nathan Green |
|---|---|
| Categories | |
| ArXiv ID | 2601.07532vv2 |
| URL | https://arxiv.org/abs/2601.07532 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
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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) 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.",
"arxiv_id": "2601.07532",
"authors": [
"Nathan Green"
],
"categories": [
"stat.CO"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Population-Adjusted Indirect Treatment Comparison with the outstandR Package in R",
"url": "https://arxiv.org/abs/2601.07532",
"version": "v2"
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