Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models
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| Format: | Artículo Open Access |
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Wiley
2026
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| _version_ | 1867019295918129152 |
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| author | Yao Chen Sophie Sun Konstantinos Sechidis Cong Zhang Torsten Hothorn Björn Bornkamp |
| author_facet | Yao Chen Sophie Sun Konstantinos Sechidis Cong Zhang Torsten Hothorn Björn Bornkamp Yao Chen Sophie Sun Konstantinos Sechidis Cong Zhang Torsten Hothorn Björn Bornkamp |
| collection | Wiley Open Access |
| contents | Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models Yao Chen Sophie Sun Konstantinos Sechidis Cong Zhang Torsten Hothorn Björn Bornkamp Statistics in Medicine ABSTRACT This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test, motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on the score‐residual of the treatment effect and explore different variants of tests in this class. All approaches are compared in a simulation study, and the approach based on residual scores is illustrated in a clinical trial with a time‐to‐event outcome comparing treatment vs. placebo. Our findings demonstrate that score‐residual‐based methods provide practical, flexible, and reliable tools for exploring treatment effect heterogeneity and treatment effect modifiers, and can provide useful guidance for decision‐making around treatment effect heterogeneity. 10.1002/sim.70381 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.70381 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70381 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models Yao Chen Sophie Sun Konstantinos Sechidis Cong Zhang Torsten Hothorn Björn Bornkamp Statistics in Medicine Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models Yao Chen Sophie Sun Konstantinos Sechidis Cong Zhang Torsten Hothorn Björn Bornkamp Statistics in Medicine ABSTRACT This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test, motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on the score‐residual of the treatment effect and explore different variants of tests in this class. All approaches are compared in a simulation study, and the approach based on residual scores is illustrated in a clinical trial with a time‐to‐event outcome comparing treatment vs. placebo. Our findings demonstrate that score‐residual‐based methods provide practical, flexible, and reliable tools for exploring treatment effect heterogeneity and treatment effect modifiers, and can provide useful guidance for decision‐making around treatment effect heterogeneity. 10.1002/sim.70381 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70381 |