Propensity score weighting with multilevel data

Statistics in Medicine

Volumn 32, Issue 19, 2013, Pages 3373-3387

  Li, Fan ^a   Zaslavsky, Alan M ^b   Landrum, Mary Beth ^b  

^a Duke University   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Balance; Multilevel; Propensity score; Racial disparity; Treatment effect; Unmeasured confounders; Weighting

Indexed keywords

AFRICAN AMERICAN; ARTICLE; BREAST CANCER; CANCER SCREENING; DATA ANALYSIS; EUROPEAN AMERICAN; HEALTH CARE PLANNING; HUMAN; MATHEMATICAL COMPUTING; MEDICARE; MONTE CARLO METHOD; MULTILEVEL ANALYSIS; PROPENSITY SCORE; RACE DIFFERENCE;

BALANCE; MULTILEVEL; PROPENSITY SCORE; RACIAL DISPARITY; TREATMENT EFFECT; UNMEASURED CONFOUNDERS; WEIGHTING;

AFRICAN CONTINENTAL ANCESTRY GROUP; AGED; AGED, 80 AND OVER; BIAS (EPIDEMIOLOGY); BREAST NEOPLASMS; CLUSTER ANALYSIS; COMPUTER SIMULATION; EARLY DETECTION OF CANCER; EUROPEAN CONTINENTAL ANCESTRY GROUP; FEMALE; HEALTHCARE DISPARITIES; HUMANS; MEDICARE; MONTE CARLO METHOD; PROPENSITY SCORE; UNITED STATES;

EID: 84880301538     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5786     Document Type: Article

References (35)

1. Rubin D.Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association 1979; 74:318-324.
2. Rosenbaum P, Rubin D.The central role of the propensity score in observational studies for causal effects. Journal of the Royal Statistical Society: Series B 1983; 70(1):41-55.
3. Rosenbaum P, Rubin D.Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984; 79:516-524.
4. D'Agostino R.Tutorial in biostatistics: propensity score methods for bias reduction in the comparisons of a treatment to a non-randomized control. Statistics in Medicine 1998; 17:2265-2281.
5. Rosenbaum P.Observational Studies. Springer: New York, 2002.
6. Firebaugh G.Rule for inferring individual-level relationships from aggregate data. American Sociological Review 1978; 43:557-572.
7. Gatsonis C, Normand S, Liu C, Morris C.Geographic variation of procedure utilization: a hierarchical model approach. Medical Care 1993; 31:YS54-YS59.
8. Nattinger A, Gottilieb M, Veum J, Yahnke D, Goodwin J.Geographic variation in the use of breast-conserving treatment for breast cancer. New England Journal of Medicine 1992; 326:1102-1127.
9. Farrow D, Samet J, Hunt W.Regional variation in survival following the diagnosis of cancer. Journal of Clinical Epidemiology 1996; 49:843-847.
10. Lingle J.Evaluating the performance of propensity scores to address selection bias in a multilevel context: a Monte Carlo simulation study and application using a national dataset. Educational policy studies dissertations, paper 56, 2009.
11. Su YS, Cortina J.What do we gain? Combining propensity score methods and multilevel modeling. APSA 2009 Annual Meeting Paper, Toronto, 2009. (Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1450058).
12. Arpino B, Mealli F.The specification of the propensity score in multilevel observational studies. Computational Statistics and Data Analysis 2011; 55:1770-1780.
13. Robins J, Rotnitzky A, Zhao L.Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 1995; 90(429):106-121.
14. Robins J, Rotnitzky A.Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 1995; 90(429):122-129.
15. Hirano K, Imbens G.Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Services & Outcomes Research Methodology 2001; 2:259-278.
16. Hirano K, Imbens G, Ridder G.Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 2003; 71:1161-1189.
17. Oakes J.The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. Social Science and Medicine 2004; 58:1929-1952.
18. VanderWeele T.Ignorability and stability assumptions in neighborhood effects research. Statistics in Medicine 2008; 27:1934-1943.
19. Schneider E, Zaslavsky A, Epstein A.Racial disparities in the quality of care for enrollees in medicare managed care. Journal of the American Medical Association 2002; 287(10):1288-1294.
20. Zhao Z.Sensitivity of propensity score methods to the specifications. Economics Letters 2008; 98(3):309-319.
21. Holland P.Statistics and causal inference. Journal of the American Statistical Association 1986; 81:945-960.
22. Rubin D.Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66(1):688-701.
23. Rubin D.Comment on 'Randomization analysis of experimental data: the Fisher randomization test' by D. Basu. Journal of the American Statistical Association 1980; 75:591-593.
24. Hausman J.Specification tests in econometrics. Econometrica 1978; 46(6):1251-1271.
25. Mundlak Y.On the pooling of time series and cross section data. Econometrica 1978; 46(1):69-85.
26. Neyman J, Scott E.Consistent estimation from partially consistent observations. Econometrica 1948; 16:1-32.
27. Bang H, Robins J.Doubly robust estimation in missing data and causal inference models. Biometrics 2005; 61:962-972.
28. McCandless L, Gustafson P, Austin P.Bayesian propensity score analysis for observational data. Statistics in Medicine 2009; 15:94-112.
29. Raudenbush S.Adaptive centering with random effects: an alternative to the fixed effects model for studying time-varying treatments in school settings. Journal of Education Finance and Policy 2009; 4(4):468-491.
30. Bates D, Maechler M, Bolker B.lme4: linear mixed-effects models using s4 classes, 2011. (Available from: http://cran.r-project.org), R Package Version 0.999375-42.
31. McGuire T, Alegria M, Cook B, Wells K, Zaslavsky A.Implementing the Institute of Medicine definition of disparities: an application to mental health care. Health Services Research 2006; 41:1979-2005.
32. Rosenbaum P.Interference between units in randomized experiments. Journal of the American Statistical Association 2007; 102(477):191-200.
33. Hudgens M, Halloran M.Towards causal inference with interference. Journal of the American Statistical Association 2008; 103:832-842.
34. Hong G, Raudenbush S.Evaluating kindergarten retention policy: a case study of causal inference for multilevel observational data. Journal of the American Statistical Association 2006; 101:901-910.
35. Hong G.Marginal mean weighting through stratification: adjustment for selection bias in multilevel data. Journal of Educational and Behavioral Statistics 2010; 35(5):495-531.