A Statistical Framework for Understanding Causal Effects that Vary by Treatment Initiation Time in EHR-based Studies
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Benz, Luke Mukherjee, Rajarshi Wang, Rui Arterburn, David Fischer, Heidi Lee, Catherine Shortreed, Susan M. Levis, Alexander W. Haneuse, Sebastien |
| author_facet | Benz, Luke Mukherjee, Rajarshi Wang, Rui Arterburn, David Fischer, Heidi Lee, Catherine Shortreed, Susan M. Levis, Alexander W. Haneuse, Sebastien |
| contents | Standard practice in electronic health record (EHR)-based studies evaluating the comparative effectiveness of bariatric surgery relative to no surgery is to estimate and report a constant treatment effect across calendar time. However, real-world treatment strategies can evolve, particularly when comparators include standard of care or surgical procedures where techniques may improve, making it clinically important to ascertain whether efficacy of bariatric surgery has changed over time. Efforts to determine whether treatment efficacy itself is evolving are complicated by changing patient populations, with potential covariate shift in key effect modifiers. Through a comprehensive analysis of EHR data from Kaiser Permanente following two bariatric surgical procedures compared to standard of care, we develop a statistical framework to estimate calendar time-specific average treatment effects and describe both how and why effects vary across treatment initiation time in EHR-based studies. Our approach projects doubly robust, time-specific treatment effect estimates onto candidate marginal structural models and uses a model selection procedure to best describe how effects vary by treatment initiation time. We further introduce a novel summary metric, based on standardization analysis, to quantify the role of covariate shift in explaining observed effect changes and disentangle changes in treatment effects from changes in the patient population receiving treatment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19553 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Statistical Framework for Understanding Causal Effects that Vary by Treatment Initiation Time in EHR-based Studies Benz, Luke Mukherjee, Rajarshi Wang, Rui Arterburn, David Fischer, Heidi Lee, Catherine Shortreed, Susan M. Levis, Alexander W. Haneuse, Sebastien Methodology Standard practice in electronic health record (EHR)-based studies evaluating the comparative effectiveness of bariatric surgery relative to no surgery is to estimate and report a constant treatment effect across calendar time. However, real-world treatment strategies can evolve, particularly when comparators include standard of care or surgical procedures where techniques may improve, making it clinically important to ascertain whether efficacy of bariatric surgery has changed over time. Efforts to determine whether treatment efficacy itself is evolving are complicated by changing patient populations, with potential covariate shift in key effect modifiers. Through a comprehensive analysis of EHR data from Kaiser Permanente following two bariatric surgical procedures compared to standard of care, we develop a statistical framework to estimate calendar time-specific average treatment effects and describe both how and why effects vary across treatment initiation time in EHR-based studies. Our approach projects doubly robust, time-specific treatment effect estimates onto candidate marginal structural models and uses a model selection procedure to best describe how effects vary by treatment initiation time. We further introduce a novel summary metric, based on standardization analysis, to quantify the role of covariate shift in explaining observed effect changes and disentangle changes in treatment effects from changes in the patient population receiving treatment. |
| title | A Statistical Framework for Understanding Causal Effects that Vary by Treatment Initiation Time in EHR-based Studies |
| topic | Methodology |
| url | https://arxiv.org/abs/2512.19553 |