Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration - PubMed (original) (raw)
. 2005 Aug 1;162(3):279-89.
doi: 10.1093/aje/kwi192. Epub 2005 Jun 29.
Affiliations
- PMID: 15987725
- PMCID: PMC1444885
- DOI: 10.1093/aje/kwi192
Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration
Til Stürmer et al. Am J Epidemiol. 2005.
Abstract
Often, data on important confounders are not available in cohort studies. Sensitivity analyses based on the relation of single, but not multiple, unmeasured confounders with an exposure of interest in a separate validation study have been proposed. In this paper, the authors controlled for measured confounding in the main cohort using propensity scores (PS's) and addressed unmeasured confounding by estimating two additional PS's in a validation study. The "error-prone" PS exclusively used information available in the main cohort. The "gold standard" PS additionally included data on covariates available only in the validation study. Based on these two PS's in the validation study, regression calibration was applied to adjust regression coefficients. This propensity score calibration (PSC) adjusts for unmeasured confounding in cohort studies with validation data under certain, usually untestable, assumptions. The authors used PSC to assess the relation between nonsteroidal antiinflammatory drugs (NSAIDs) and 1-year mortality in a large cohort of elderly persons. "Traditional" adjustment resulted in a hazard ratio for NSAID users of 0.80 (95% confidence interval (CI): 0.77, 0.83) as compared with an unadjusted hazard ratio of 0.68 (95% CI: 0.66, 0.71). Application of PSC resulted in a more plausible hazard ratio of 1.06 (95% CI: 1.00, 1.12). Until the validity and limitations of PSC have been assessed in different settings, the method should be seen as a sensitivity analysis.
Similar articles
- Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information.
Stürmer T, Glynn RJ, Rothman KJ, Avorn J, Schneeweiss S. Stürmer T, et al. Med Care. 2007 Oct;45(10 Supl 2):S158-65. doi: 10.1097/MLR.0b013e318070c045. Med Care. 2007. PMID: 17909375 Free PMC article. Review. - Performance of propensity score calibration--a simulation study.
Stürmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Stürmer T, et al. Am J Epidemiol. 2007 May 15;165(10):1110-8. doi: 10.1093/aje/kwm074. Epub 2007 Mar 28. Am J Epidemiol. 2007. PMID: 17395595 Free PMC article. - Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution--a simulation study.
Stürmer T, Rothman KJ, Avorn J, Glynn RJ. Stürmer T, et al. Am J Epidemiol. 2010 Oct 1;172(7):843-54. doi: 10.1093/aje/kwq198. Epub 2010 Aug 17. Am J Epidemiol. 2010. PMID: 20716704 Free PMC article. - Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly.
Stürmer T, Schneeweiss S, Brookhart MA, Rothman KJ, Avorn J, Glynn RJ. Stürmer T, et al. Am J Epidemiol. 2005 May 1;161(9):891-8. doi: 10.1093/aje/kwi106. Am J Epidemiol. 2005. PMID: 15840622 Free PMC article. - A tutorial on the use of instrumental variables in pharmacoepidemiology.
Ertefaie A, Small DS, Flory JH, Hennessy S. Ertefaie A, et al. Pharmacoepidemiol Drug Saf. 2017 Apr;26(4):357-367. doi: 10.1002/pds.4158. Epub 2017 Feb 27. Pharmacoepidemiol Drug Saf. 2017. PMID: 28239929 Review.
Cited by
- Assessing the Benefits and Harms of Pharmacotherapy in Older Adults with Frailty: Insights from Pharmacoepidemiologic Studies of Routine Health Care Data.
Kim DH, Park CM, Ko D, Lin KJ, Glynn RJ. Kim DH, et al. Drugs Aging. 2024 Jul;41(7):583-600. doi: 10.1007/s40266-024-01121-0. Epub 2024 Jul 2. Drugs Aging. 2024. PMID: 38954400 Review. - Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation.
Kim MK, Rouphael C, McMichael J, Welch N, Dasarathy S. Kim MK, et al. Gut Liver. 2024 Mar 15;18(2):201-208. doi: 10.5009/gnl230272. Epub 2023 Oct 31. Gut Liver. 2024. PMID: 37905424 Free PMC article. Review. - A Tutorial for Propensity Score Weighting for Moderation Analysis With Categorical Variables: An Application Examining Smoking Disparities Among Sexual Minority Adults.
Griffin BA, Schuler MS, Cefalu M, Ayer L, Godley M, Greifer N, Coffman DL, McCaffrey DF. Griffin BA, et al. Med Care. 2023 Dec 1;61(12):836-845. doi: 10.1097/MLR.0000000000001922. Epub 2023 Oct 2. Med Care. 2023. PMID: 37782463 - Strategies to Address Current Challenges in Real-World Evidence Generation in Japan.
Laurent T, Lambrelli D, Wakabayashi R, Hirano T, Kuwatsuru R. Laurent T, et al. Drugs Real World Outcomes. 2023 Jun;10(2):167-176. doi: 10.1007/s40801-023-00371-5. Epub 2023 May 13. Drugs Real World Outcomes. 2023. PMID: 37178273 Free PMC article. Review.
References
- Cornfield J, Haenszel W, Hammond EC, et al. Smoking and lung cancer: Recent evidence and a discussion of some questions. J Natl Cancer Inst. 1959;22:173–203. - PubMed
- Rosenbaum PR, Rubin DB. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J R Statist Soc B. 1983;45:212–8.
- Rosenbaum PR. Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika. 1987;74:13–26.
- Rosenbaum PR. Sensitivity analysis for matched case-control studies. Biometrics. 1991;47:87–100. - PubMed
- Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics. 1998;54:948–63. - PubMed