دورية أكاديمية
A Cautionary Note on Using Propensity Score Calibration to Control for Unmeasured Confounding Bias When the Surrogacy Assumption Is Absent.
| العنوان: | A Cautionary Note on Using Propensity Score Calibration to Control for Unmeasured Confounding Bias When the Surrogacy Assumption Is Absent. |
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| المؤلفون: | Wan F |
| المصدر: | American journal of epidemiology [Am J Epidemiol] 2024 Feb 05; Vol. 193 (2), pp. 360-369. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 7910653 Publication Model: Print Cited Medium: Internet ISSN: 1476-6256 (Electronic) Linking ISSN: 00029262 NLM ISO Abbreviation: Am J Epidemiol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Cary, NC : Oxford University Press Original Publication: Baltimore, School of Hygiene and Public Health of Johns Hopkins Univ. |
| مواضيع طبية MeSH: | Calibration*, Humans ; Propensity Score ; Confounding Factors, Epidemiologic ; Bias ; Proportional Hazards Models |
| مستخلص: | Conventional propensity score methods encounter challenges when unmeasured confounding is present, as it becomes impossible to accurately estimate the gold-standard propensity score when data on certain confounders are unavailable. Propensity score calibration (PSC) addresses this issue by constructing a surrogate for the gold-standard propensity score under the surrogacy assumption. This assumption posits that the error-prone propensity score, based on observed confounders, is independent of the outcome when conditioned on the gold-standard propensity score and the exposure. However, this assumption implies that confounders cannot directly impact the outcome and that their effects on the outcome are solely mediated through the propensity score. This raises concerns regarding the applicability of PSC in practical settings where confounders can directly affect the outcome. While PSC aims to target a conditional treatment effect by conditioning on a subject's unobservable propensity score, the causal interest in the latter case lies in a conditional treatment effect conditioned on a subject's baseline characteristics. Our analysis reveals that PSC is generally biased unless the effects of confounders on the outcome and treatment are proportional to each other. Furthermore, we identify 2 sources of bias: 1) the noncollapsibility of effect measures, such as the odds ratio or hazard ratio and 2) residual confounding, as the calibrated propensity score may not possess the properties of a valid propensity score. (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.) |
| معلومات مُعتمدة: | P30 CA091842 United States CA NCI NIH HHS |
| فهرسة مساهمة: | Keywords: confounding bias; linear models; noncollapsibility; nonlinear models; propensity score calibration |
| تواريخ الأحداث: | Date Created: 20230927 Date Completed: 20240206 Latest Revision: 20240723 |
| رمز التحديث: | 20240725 |
| DOI: | 10.1093/aje/kwad189 |
| PMID: | 37759344 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1476-6256 |
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| DOI: | 10.1093/aje/kwad189 |