Propensity score weighting with survey weighted data when outcomes are binary: a simulation study

Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. US Department of Health and Human Services, Rockville (MD) (2008). https://www.ncbi.nlm.nih.gov/books/NBK63952/

Austin, P.C.: An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar. Behav. Res. 46(3), 399–424 (2011). https://doi.org/10.1080/00273171.2011.568786

Article  Google Scholar 

Austin, P.C.: Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat. Med. 35(30), 5642–5655 (2016). https://doi.org/10.1002/sim.7084

Article  PubMed  PubMed Central  Google Scholar 

Austin, P.C., Mamdani, M.M.: A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use. Stat. Med. 25(12), 2084–2106 (2006). https://doi.org/10.1002/sim.2328

Article  PubMed  Google Scholar 

Austin, P.C., Jembere, N., Chiu, M.: Propensity score matching and complex surveys. Stat. Methods Med. Res. 27(4), 1240–1257 (2018). https://doi.org/10.1177/0962280216658920

Article  PubMed  Google Scholar 

Benson, K., Hartz, A.J.: A comparison of observational studies and randomized, controlled trials. N. Engl. J. Med. 342(25), 1878–1886 (2000). https://doi.org/10.1056/NEJM200006223422506

Article  CAS  PubMed  Google Scholar 

Cook, B.L., McGuire, T.G., Meara, E., Zaslavsky, A.M.: Adjusting for health status in non-linear models of health care disparities. Health Serv. Outcomes Res. Methodol. 9(1), 1–21 (2009). https://doi.org/10.1007/s10742-008-0039-6

Article  PubMed  PubMed Central  Google Scholar 

Dong, N., Stuart, E.A., Lenis, D., Quynh Nguyen, T.: Using propensity score analysis of survey data to estimate population average treatment effects: a case study comparing different methods. Eval. Rev. 44(1), 84–108 (2020). https://doi.org/10.1177/0193841X20938497

Article  PubMed  Google Scholar 

Dugoff, E.H., Schuler, M., Stuart, E.A.: Generalizing observational study results: applying propensity score methods to complex surveys. Health Serv. Res. 49(1), 284–303 (2014). https://doi.org/10.1111/1475-6773.12090

Article  PubMed  Google Scholar 

Feinstein, A.R.: Epidemiologic analyses of causation: the unlearned scientific lessons of randomized trials. J. Clin. Epidemiol. 42(6), 481–489 (1989). https://doi.org/10.1016/0895-4356(89)90142-X

Article  CAS  PubMed  Google Scholar 

Goetghebeur, E., le Cessie, S., De Stavola, B., Moodie, E.E., Waernbaum, I.: Formulating causal questions and principled statistical answers. Stat. Med. 39(30), 4922–4948 (2020). https://doi.org/10.1002/sim.8741

Article  PubMed  PubMed Central  Google Scholar 

Gomila, R.: Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J. Exp. Psychol. Gen. 150, 700–709 (2021). https://doi.org/10.1037/xge0000920

Article  PubMed  Google Scholar 

Harder, V.S., Stuart, E.A., Anthony, J.C.: Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychol. Methods 15(3), 234–249 (2010). https://doi.org/10.1037/a0019623

Article  PubMed  PubMed Central  Google Scholar 

Heckman, J.J., Robb, R.: Alternative methods for evaluating the impact of interventions: an overview. J. Econom. 30(1), 239–267 (1985). https://doi.org/10.1016/0304-4076(85)90139-3

Article  Google Scholar 

Henley, S.J., Asman, K., Momin, B., Gallaway, M.S., Culp, M.B., Ragan, K.R., Richards, T.B., Babb, S.: Smoking cessation behaviors among older U.S. adults. Prev. Med. Rep. 16, 100978 (2019). https://doi.org/10.1016/j.pmedr.2019.100978

Article  PubMed  PubMed Central  Google Scholar 

Hu, L., Ji, J., Li, F.: Estimating heterogeneous survival treatment effect in observational data using machine learning. Stat. Med. 40(21), 4691–4713 (2021). https://doi.org/10.1002/sim.9090

Article  PubMed  PubMed Central  Google Scholar 

Imai, K., King, G., Stuart, E.A.: Misunderstandings between experimentalists and observationalists about causal inference. J. R. Stat. Soc. Ser. A Stat. Soc. 171(2), 481–502 (2008). https://doi.org/10.1111/j.1467-985X.2007.00527.x

Article  Google Scholar 

Imbens, G.: Nonparametric estimation of average treatment effects under exogeneity: a review. Rev. Econ. Stat. 86(1), 4–29 (2004)

Article  Google Scholar 

Kolenikov, S.: Resampling variance estimation for complex survey data. Stata J. 10(2), 165–199 (2010). https://doi.org/10.1177/1536867x1001000201

Article  Google Scholar 

Lee, B.K., Lessler, J., Stuart, E.A.: Improving propensity score weighting using machine learning. Stat. Med. 29(3), 337–346 (2010). https://doi.org/10.1002/sim.3782

Article  PubMed  PubMed Central  Google Scholar 

Lenis, D., Ackerman, B., Stuart, E.A.: Measuring model misspecification: application to propensity score methods with complex survey data. Comput. Stat. Data Anal. 128, 48–57 (2018). https://doi.org/10.1016/j.csda.2018.05.003

Article  PubMed  PubMed Central  Google Scholar 

Lenis, D., Nguyen, T.Q., Dong, N., Stuart, E.A.: It’s all about balance: propensity score matching in the context of complex survey data. Biostatistics 20(1), 147–163 (2019). https://doi.org/10.1093/biostatistics/kxx063

Article  PubMed  Google Scholar 

Li, F., Thomas, L.E., Li, F.: Addressing extreme propensity scores via the overlap weights. Am. J. Epidemiol. 188(1), 250–257 (2019). https://doi.org/10.1093/aje/kwy201

Article  PubMed  Google Scholar 

Lunceford, J.K., Davidian, M.: Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat. Med. 23(19), 2937–2960 (2004). https://doi.org/10.1002/sim.1903

Article  PubMed  Google Scholar 

Pfeffermann, D.: The role of sampling weights when modeling survey data. Int. Stat. Rev. 61(2), 317–337 (1993). https://doi.org/10.2307/1403631

Article  Google Scholar 

Qaqish, B.F.: A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations. Biometrika 90(2), 455–463 (2003). https://doi.org/10.1093/biomet/90.2.455

Article  Google Scholar 

Ridgeway, G., Kovalchik, S.A., Griffin, B.A., Kabeto, M.U.: Propensity score analysis with survey weighted data. J Causal Inference 3(2), 237–249 (2015). https://doi.org/10.1515/jci-2014-0039

Article  PubMed  PubMed Central  Google Scholar 

Rosenbaum, P.R.: Model-based direct adjustment. J. Am. Stat. Assoc. 82(398), 387–394 (1987). https://doi.org/10.2307/2289440

Article  Google Scholar 

Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983). https://doi.org/10.1093/biomet/70.1.41

Article  Google Scholar 

Rosenbaum, P.R., Rubin, D.B.: Reducing bias in observational studies using subclassification on the propensity score. J. Am. Stat. Assoc. 79(387), 516–524 (1984). https://doi.org/10.2307/2288398

Article  Google Scholar 

Rubin, D.B.: Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66(5), 688–701 (1974). https://doi.org/10.1037/h0037350

Article  Google Scholar 

Rubin, D.B.: Assignment to treatment group on the basis of a covariate. J. Educ. Stat. 2(1), 1–26 (1977). https://doi.org/10.2307/1164933

Article  Google Scholar 

Sato, T., Matsuyama, Y.: Marginal structural models as a tool for standardization. Epidemiology 14(6), 680–686 (2003). https://doi.org/10.1097/01.EDE.0000081989.82616.7d

Article  PubMed  Google Scholar 

Shao, J.: Resampling methods in sample surveys. Statistics 27(3–4), 203–254 (1996). https://doi.org/10.1080/02331889708802523

Article  Google Scholar 

Shao, J., Tu, D.: The Jackknife and Bootstrap. Springer, New York (1995)

Book  Google Scholar 

Stuart, E.A.: Matching methods for causal inference: a review and a look forward. Stat. Sci. 25(1), 1–21 (2010). https://doi.org/10.1214/09-STS313

Article  PubMed  PubMed Central  Google Scholar 

Suri, R.S., Li, L., Nesrallah, G.E.: The risk of hospitalization and modality failure with home dialysis. Kidney Int. 88(2), 360–368 (2015). https://doi.org/10.1038/ki.2015.68

Article  PubMed  PubMed Central  Google Scholar 

Tremblay, D., King, A., Li, L., Moshier, E., Coltoff, A., Koshy, A., Kremyanskaya, M., Hoffman, R., Mauro, M.J., Rampal, R.K., Mascarenhas, J.: Risk factors for infections and secondary malignancies in patients with a myeloproliferative neoplasm treated with ruxolitinib: a dual-center, propensity score-matched analysis. Leuk. Lymphoma 61(3), 660–667 (2020). https://doi.org/10.1080/10428194.2019.1688323

Article  CAS  PubMed  Google Scholar 

Zanutto, E.: A comparison of propensity score and linear regression analysis of complex survey data. J. Data Sci. 4, 67–91 (2006)

Article  Google Scholar 

Zanutto, E., Lu, B., Hornik, R.: Using propensity score subclassification for multiple treatment doses to evaluate a national antidrug media campaign. J. Educ. Behav. Stat. 30(1), 59–73 (2005). https://doi.org/10.3102/10769986030001059

Article  Google Scholar 

留言 (0)

沒有登入
gif