{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/individualized-treatment-effect-was-predicted","title":"Individualized treatment effect was predicted best by modeling baseline risk in interaction with treatment assignment","arxiv_id":"2205.01717","date":"2022-05-03","proceeding":null,"authors":["Alexandros Rekkas","Peter R. Rijnbeek","David M. Kent","Ewout W. Steyerberg","David van Klaveren"],"abstract":"Objective: To compare different risk-based methods for optimal prediction of treatment effects. Methods: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk (PI), the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the PI). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the PI; models including a linear interaction of treatment with the PI; models including an interaction of treatment with a restricted cubic spline (RCS) transformation of the PI; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. Results: The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N=4,250 patients; ~ 785 events). The RCS-model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N=17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. Conclusion: An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.","url_abs":"https://arxiv.org/abs/2205.01717v4","url_pdf":"https://arxiv.org/pdf/2205.01717v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"individualized-treatment-effect-was-predicted","repo_url":"https://github.com/mi-erasmusmc/HteSimulationRCT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}