Propensity scores in the presence of effect modification: A case study using the comparison of mortality on hemodialysis versus peritoneal dialysis
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Sturmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S: A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol. 2006, 59: 437-447. 10.1016/j.jclinepi.2005.07.004
Miettinen OS: Stratification by a multivariate confounder score. Am J Epidemiol. 1976, 104: 609-620.
Rosenbaum PR, Rubin DB: The central role of the propensity score in observational studies for causal effects. Biometrika. 1983, 70: 41-55. 10.1093/biomet/70.1.41
Shah BR, Laupacis A, Hux JE, Austin PC: Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol. 2005, 58: 550-559. 10.1016/j.jclinepi.2004.10.016
Korevaar JC, Feith GW, Dekker FW, van Manen JG, Boeschoten EW, Bossuyt PM, Krediet RT: Effect of starting with hemodialysis compared with peritoneal dialysis in patients new on dialysis treatment: a randomized controlled trial. Kidney Int. 2003, 64: 2222-2228. 10.1046/j.1523-1755.2003.00321.x
de Charro FT, Nieuwenhuizen MG, Ramsteijn PG, van Hamersvelt HW, Struijk DG, Ter Wee PM, Tjandra YI: Statistisch Verslag 2001. Rotterdam: Stichting RENINE; 2001.
Huisman RM, Nieuwenhuizen MG, Th de Charro F: Patient-related and centre-related factors influencing technique survival of peritoneal dialysis in The Netherlands. Nephrol Dial Transplant. 2002, 17: 1655-1660. 10.1093/ndt/17.9.1655
Rubin DB: Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997, 127: 757-763.
D'Agostino RB Jr, D'Agostino RB Sr: Estimating treatment effects using observational data. Jama. 2007, 297: 314-316. 10.1001/jama.297.3.314
Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V: Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf. 2004, 13: 841-853. 10.1002/pds.969
Liem YS, Wong JB, Hunink MG, de Charro FT, Winkelmayer WC: Comparison of hemodialysis and peritoneal dialysis survival in The Netherlands. Kidney Int. 2007, 71: 153-158. 10.1038/sj.ki.5002014
Held PJ, Port FK, Turenne MN, Gaylin DS, Hamburger RJ, Wolfe RA: Continuous ambulatory peritoneal dialysis and hemodialysis: comparison of patient mortality with adjustment for comorbid conditions. Kidney Int. 1994, 45: 1163-1169. 10.1038/ki.1994.154
Winkelmayer WC, Glynn RJ, Mittleman MA, Levin R, Pliskin JS, Avorn J: Comparing mortality of elderly patients on hemodialysis versus peritoneal dialysis: a propensity score approach. J Am Soc Nephrol. 2002, 13: 2353-2362. 10.1097/01.ASN.0000025785.41314.76
Collins AJ, Weinhandl E, Snyder JJ, Chen SC, Gilbertson D: Comparison and survival of hemodialysis and peritoneal dialysis in the elderly. Semin Dial. 2002, 15: 98-102. 10.1046/j.1525-139X.2002.00032.x
Vonesh EF, Snyder JJ, Foley RN, Collins AJ: The differential impact of risk factors on mortality in hemodialysis and peritoneal dialysis. Kidney Int. 2004, 66: 2389-2401. 10.1111/j.1523-1755.2004.66028.x
Sturmer T, Rothman KJ, Glynn RJ: Insights into different results from different causal contrasts in the presence of effect-measure modification. Pharmacoepidemiol Drug Saf. 2006, 15: 698-709. 10.1002/pds.1231
Cepeda MS, Boston R, Farrar JT, Strom BL: Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol. 2003, 158: 280-287. 10.1093/aje/kwg115
Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, Robins JM: Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2006, 163: 262-270. 10.1093/aje/kwj047
Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V: Weaknesses of goodness-of-fit tests for evaluating propensity score models: the case of the omitted confounder. Pharmacoepidemiol Drug Saf. 2005, 14: 227-238. 10.1002/pds.986
Austin PC, Grootendorst P, Anderson GM: A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med. 2006.
