Évaluation de l’effet d’un traitement dans les études observationnelles par le score de propension

Pratique Neurologique - FMC - Tập 12 - Trang 252-260 - 2021
C. Ternynck1, H. Béhal1, B. Lapergue2, J. Labreuche1, A. Duhamel1
1Université Lille, CHU Lille, ULR 2694, METRICS : évaluation des technologies de santé et des pratiques médicales, 59000 Lille, France
2Service de neurologie, unité de neurovasculaire, hôpital Foch, université de Versailles et Saint-Quentin-en-Yvelines, Suresnes, France

Tài liệu tham khảo

Rosenbaum, 1983, The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41, 10.1093/biomet/70.1.41 Cepeda, 2003, Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders, Am J Epidemiol, 158, 280, 10.1093/aje/kwg115 Lonjon, 2017, Potential pitfalls of reporting and bias in observational studies with propensity score analysis assessing a surgical procedure: a methodological systematic review, Ann Surg, 265, 901, 10.1097/SLA.0000000000001797 Gronnier, 2014, Impact of neoadjuvant chemoradiotherapy on postoperative outcomes after esophageal cancer resection: results of a European multicenter study, Ann Surg, 260, 764, 10.1097/SLA.0000000000000955 Di Maria, 2018, Intravenous thrombolysis prior to mechanical thrombectomy in acute ischemic stroke: silver bullet or useless bystander?, J Stroke, 20, 385, 10.5853/jos.2018.01543 Vincent, 2020, Ultrasound guidance to reduce vascular and bleeding complications of percutaneous transfemoral transcatheter aortic valve replacement: a propensity score–matched comparison, J Am Heart Assoc, 9, e014916, 10.1161/JAHA.119.014916 Austin, 2011, An introduction to propensity score methods for reducing the effects of confounding in observational studies, Multivariate Behav Res, 46, 399, 10.1080/00273171.2011.568786 Austin, 2011, Optimal caliper widths for propensity score matching when estimating differences in means and differences in proportions in observational studies, Pharm Stat, 10, 150, 10.1002/pst.433 Brookhart, 2006, Variable selection for propensity score models, Am J Epidemiol, 163, 1149, 10.1093/aje/kwj149 Glynn, 2006, Indications for propensity scores and review of their use in pharmacoepidemiology, Basic Clin Pharmacol Toxicol, 98, 253, 10.1111/j.1742-7843.2006.pto_293.x Shah, 2005, Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review, J Clin Epidemiol, 58, 550, 10.1016/j.jclinepi.2004.10.016 Austin, 2010, Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using manty-to-one matching on the propensity score, Am J Epidemiol, 172, 1092, 10.1093/aje/kwq224 Austin, 2014, A comparison of 12 algorithms for matching on the propensity score, Stat Med, 33, 1057, 10.1002/sim.6004 Austin, 2008, Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score, Pharmacoepidemiol Drug Saf, 17, 1202, 10.1002/pds.1673 Hill, 2008, Discussion of research using propensity score matching: comments on ‘A critical appraisal of propensity score matching in the medical literature between 1996 and 2003’ by Peter Austin, Statistics in Medicine, Stat Med, 27, 2055, 10.1002/sim.3245 Rosenbaun, 1984, Reducing bias in observational studies using subclassification on the propensity score, J Am Stat Assoc, 79, 516, 10.1080/01621459.1984.10478078 Austin, 2015, Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies, Stat Med, 34, 3661, 10.1002/sim.6607 Li, 2019, Addressing extreme propensity scores via the overlap weights, Am J Epidemiol, 188, 250 Rosenbaum, 1987, Model-based direct adjustment, J Am Stat Assoc, 82, 387, 10.1080/01621459.1987.10478441 Rubin, 2001, Using propensity scores to help design observational studies: application to the tobacco litigation, Health Serv Outcomes Res Methodol, 2, 169, 10.1023/A:1020363010465 Stuart, 2010, Matching methods for causal inference: a review and a look forward, Stat Sci, 25, 1, 10.1214/09-STS313 Austin, 2009, The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies, Med Decis Making, 29, 661, 10.1177/0272989X09341755 Normand, 2001, Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores, J Clin Epidemiol, 54, 387, 10.1016/S0895-4356(00)00321-8 Peduzzi, 1996, A simulation study of the number of events per variable in logistic regression analysis, J Clin Epidemiol, 49, 1373, 10.1016/S0895-4356(96)00236-3 Peduzzi, 1995, Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates, J Clin Epidemiol, 48, 15, 10.1016/0895-4356(95)00048-8 Austin, 2009, Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity score matched samples, Stat Med, 28, 3083, 10.1002/sim.3697 Rubin, 1978, Multiple imputation in sample survey: a phenomenological Bayesian approach to nonresponse, Proc Surv Res Meth Sect Am Stat Assoc, 1, 20 Mattei, 2009, Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on well-being, Stat Meth Appl, 18, 257, 10.1007/s10260-007-0086-0