Estimating propensity scores with missing covariate data using general location mixture models

Statistics in Medicine

Volumn 30, Issue 6, 2011, Pages 627-641

  Mitra, Robin ^a   Reiter, Jerome P ^b  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b Duke University   (United States)

Author keywords

Latent class; Matching; Missing data; Multiple imputation; Observational studies; Propensity score

Indexed keywords

ARTICLE; CALCULATION; GENERAL LOCATION MIXTURE MODEL; RELIABILITY; SCORING SYSTEM; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ADOLESCENT; BREAST FEEDING; COGNITION; COHORT STUDIES; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; PROPENSITY SCORE; YOUNG ADULT;

EID: 79951705391     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4124     Document Type: Article

References (41)

1. Cochran WG, Chambers SP. The planning of observational studies of human populations. Journal of the Royal Statistical Society. Series A (General) 1965; 128(2):234-266.
2. Rubin D. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66(5):688-701.
3. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41-55.
4. Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 1985; 39(1):33-38.
5. Rosenbaum PR. A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society, Series B-Methodological 1991; 53:597-610.
6. Stuart EA, Green KM. Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes. Developmental Psychology 2008; 44(2):395-406.
7. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984; 79:516-524.
8. Hullsiek KH, Louis TA. Propensity score modeling strategies for the causal analysis of observational data. Biostatistics (Oxford) 2002; 3(2):179-193.
9. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11(5):550-560.
10. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine 2004; 23(19):2937-2960.
11. D'Agostino Jr RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine 1998; 17:2265-2281.
12. Rosenbaum PR. Sensitivity analysis for certain permutation inferences in matched observational studies (Corr: V75 p396). Biometrika 1987; 74:13-26.
13. Woo MJ, Reiter JP, Karr AF. Estimation of propensity scores using generalized additive models. Statistics in Medicine 2008; 27(19):3805-3816.
14. D'Agostino Jr RB, Rubin DB. Estimating and using propensity scores with partially missing data. Journal of the American Statistical Association 2000; 95(451):749-759.
15. Rubin DB. Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987.
16. Hill J. Reducing bias in treatment effect estimation in observational studies suffering from missing data. Columbia University Institute for Social and Economic Research and Policy (ISERP). Working Paper 04-01, 2004;
17. Hill JL, Reiter JP, Zanutto EL. A comparison of experimental and observational data analyses. In Applied Bayesian Modeling and Causal Inference from an Incomplete-Data Perspective, Gelman A, Meng XL (eds). Wiley: New York, 2004.
18. Haviland A, Nagin D, Rosenbaum P. Combining propensity score matching and group-based trajectory analysis in an observational study. Psychological Methods 2007; 12(3):247-267.
19. Qu Y, Lipkovich I. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Statistics in Medicine 2009; 28(9):1402-1414.
20. Cho YB, Lee K, Suh K, Kim Y, Yoon J, Lee H, Hahn S, Park B. Maternal smoking during pregnancy and birthweight: a propensity score matching approach. Journal of Gastroenterology andHepatology 2007; 22(10):1643-1649.
21. da Veiga PV, Wilder RP. Maternal smoking during pregnancy and birthweight: a propensity score matching approach. Maternal and Child Health Journal 2008; 12(2):194-203.
22. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine 2008; 27(12):2037-2049.
23. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment group in propensity-score matched samples. Statistics in Medicine 2009; published online in Wiley InterScience.
24. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 1994; 89:846-866.
25. Liang H, Wang S, Robins JM, Carroll RJ. Estimation in partially linear models with missing covariates. Journal of the American Statistical Association 2004; 99(466):357-367.
26. Ibrahim JG, Chen MH, Lipsitz SR, Herring AH. Missing-data methods for generalized linear models: a comparative review. Journal of the American Statistical Association 2005; 100(469):332-346.
27. Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association 1987; 82:528-540.
28. Little RJA. Regression with missing X's: a review. Journal of the American Statistical Association 1992; 87:1227-1237.
29. Moons KGM, Donders RART, Stijnen T, Harrell Jr FE. Using the outcome for imputation of missing predictor values was preferred. Journal of Clinical Epidemiology 2006; 59(10):1092-1101.
30. Beunckens C, Molenberghs G, Verbeke G, Mallinckrodt C. A latent-class mixture model for incomplete longitudinal gaussian data. Biometrics 2008; 64:96-105.
31. Hill JL. Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics 2010; DOI: 10.1198/jcgs.2010.08162.
32. Diebolt J, Robert CP. Estimation of finite mixture distributions through Bayesian sampling. Journal of the Royal Statistical Society. Series B (Methodological) 1994; 56(2):363-375.
33. Wasserman L. Asymptotic inference for mixture models by using data-dependent priors. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2000; 62(1):159-180.
34. Box GEP, Cox DR. An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological) 1964; 26(2):211-252.
35. Schafer JL. Analysis of Incomplete Multivariate Data. Chapman & Hall: London, 1997.
36. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. Chapman & Hall: London, 1995.
37. Chantry CJ, Howard CR, Auinger P. Full breastfeeding duration and associated decrease in respiratory tract infection in US children. Pediatrics 2006; 117(2):425-432.
38. Austin PC. Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. The International Journal of Biostatistics 2009; 5(1). DOI: 10.2202/1557-4679.1146.
39. Hill J, Reiter JP. Interval estimation for treatment effects using propensity score matching. Statistics in Medicine 2006; 25:2230-2256.
40. Der G, Batty GD, Deary IJ. Effect of breast feeding on intelligence in children: prospective study, sibling pairs analysis, and meta-analysis. British Medical Journal 2006; 333. DOI: 10.1136/bmj.38978.699583.55.
41. Raghunathan TE, Lepkowski JM, van Hoewyk J, Solenberger P. A multivariate technique for multiply imputing missing values using a series of regression models. Survey Methodology 2001; 27:85-96.