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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07532 |
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| _version_ | 1866917205474541568 |
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| author | Green, Nathan |
| author_facet | Green, Nathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07532 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Population-Adjusted Indirect Treatment Comparison with the outstandR Package in R Green, Nathan Computation 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. |
| title | Population-Adjusted Indirect Treatment Comparison with the outstandR Package in R |
| topic | Computation |
| url | https://arxiv.org/abs/2601.07532 |