Assessing the Impact of Covariate Distribution and Positivity Violation on Weighting-Based Indirect Comparisons: a Simulation Study
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arXiv
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| Format: | Preprint |
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2025
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| author | Serret-Larmande, Arnaud Lambert, Jérôme Gaudry, Stéphane Hajage, David |
| author_facet | Serret-Larmande, Arnaud Lambert, Jérôme Gaudry, Stéphane Hajage, David |
| contents | Population-Adjusted Indirect Comparisons (PAICs) are used to estimate treatment effects when direct comparisons are infeasible and individual patient data (IPD) are only available for one trial. Among PAIC methods, Matching-Adjusted Indirect Comparison (MAIC) is the most widely used. However, little is known about how MAIC performs under challenging conditions such as limited covariate overlap or markedly non-normal covariate distributions.
We conducted a Monte Carlo simulation study comparing three estimators: (i) MAIC matching first moment (MAIC-1), (ii) MAIC matching first and second moments (MAIC-2), and (iii) a benchmark method leveraging full IPD -- Propensity Score Weighting (PSW). We examined eight scenarios ranging from ideal conditions to situations with positivity violations and non-normal (including bimodal) covariate distributions. We assessed both anchored and unanchored estimators and examined the impact of adjustment model misspecification. We also applied these estimators to real-world data from the AKIKI and AKIKI-2 trials, comparing renal replacement therapy strategies in critically ill patients.
MAIC-1 demonstrated robust performance, remaining unbiased in the presence of moderate positivity violations and non-normal covariates, while MAIC-2 and PSW appeared more sensitive to positivity violations. All methods showed substantial bias when key confounders were omitted, emphasizing the importance of correct model specification. In real-world data, a consistent trend was found with MAIC-1 showing narrower confidence intervals with positivity violation.
Our findings support the cautious use of unanchored MAICs and highlight MAIC-1's resilience across moderate violations of assumptions. However, the method's limited flexibility underscores the need for careful use in real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing the Impact of Covariate Distribution and Positivity Violation on Weighting-Based Indirect Comparisons: a Simulation Study Serret-Larmande, Arnaud Lambert, Jérôme Gaudry, Stéphane Hajage, David Methodology Applications Population-Adjusted Indirect Comparisons (PAICs) are used to estimate treatment effects when direct comparisons are infeasible and individual patient data (IPD) are only available for one trial. Among PAIC methods, Matching-Adjusted Indirect Comparison (MAIC) is the most widely used. However, little is known about how MAIC performs under challenging conditions such as limited covariate overlap or markedly non-normal covariate distributions. We conducted a Monte Carlo simulation study comparing three estimators: (i) MAIC matching first moment (MAIC-1), (ii) MAIC matching first and second moments (MAIC-2), and (iii) a benchmark method leveraging full IPD -- Propensity Score Weighting (PSW). We examined eight scenarios ranging from ideal conditions to situations with positivity violations and non-normal (including bimodal) covariate distributions. We assessed both anchored and unanchored estimators and examined the impact of adjustment model misspecification. We also applied these estimators to real-world data from the AKIKI and AKIKI-2 trials, comparing renal replacement therapy strategies in critically ill patients. MAIC-1 demonstrated robust performance, remaining unbiased in the presence of moderate positivity violations and non-normal covariates, while MAIC-2 and PSW appeared more sensitive to positivity violations. All methods showed substantial bias when key confounders were omitted, emphasizing the importance of correct model specification. In real-world data, a consistent trend was found with MAIC-1 showing narrower confidence intervals with positivity violation. Our findings support the cautious use of unanchored MAICs and highlight MAIC-1's resilience across moderate violations of assumptions. However, the method's limited flexibility underscores the need for careful use in real-world settings. |
| title | Assessing the Impact of Covariate Distribution and Positivity Violation on Weighting-Based Indirect Comparisons: a Simulation Study |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2507.12241 |