메뉴 건너뛰기




Volumn 25, Issue 1, 2016, Pages 188-204

A comparison of two methods of estimating propensity scores after multiple imputation

Author keywords

Missing data; multiple imputation; observational studies; propensity score

Indexed keywords

CONTROLLED STUDY; HUMAN; INTERMETHOD COMPARISON; OBSERVATIONAL STUDY; PROPENSITY SCORE; BIOSTATISTICS; BREAST FEEDING; CHILD; CHILD DEVELOPMENT; COMPARATIVE STUDY; COMPUTER SIMULATION; INFANT; NEWBORN; PRESCHOOL CHILD; STATISTICAL BIAS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TREATMENT OUTCOME;

EID: 84958699481     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212445945     Document Type: Article
Times cited : (214)

References (32)
  • 1
    • 77951622706 scopus 로고
    • The central role of the propensity score in observational studies for causal effects
    • Rosenbaum PR, Rubin DB,. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41-55.
    • (1983) Biometrika , vol.70 , pp. 41-55
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 2
    • 84945581878 scopus 로고
    • Constructing a control group using multivariate matched sampling methods that incorporate the propensity score
    • Rosenbaum PR, Rubin DB,. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 1985; 39 (1): 33-38.
    • (1985) Am Stat , vol.39 , Issue.1 , pp. 33-38
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 3
    • 3543135271 scopus 로고    scopus 로고
    • Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group
    • D'Agostino RB Jr,. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998; 17: 2265-2281.
    • (1998) Stat Med , vol.17 , pp. 2265-2281
    • D'Agostino, R.B.J.1
  • 4
    • 33847026356 scopus 로고    scopus 로고
    • Classifying radiographic progression status in early rheumatoid arthritis patients using propensity scores to adjust for baseline differences
    • Park GS, Wong WK, Oh M, et al. Classifying radiographic progression status in early rheumatoid arthritis patients using propensity scores to adjust for baseline differences. Stat Meth Med Res 2007; 16 (1): 13-29.
    • (2007) Stat Meth Med Res , vol.16 , Issue.1 , pp. 13-29
    • Park, G.S.1    Wong, W.K.2    Oh, M.3
  • 5
    • 84859298193 scopus 로고    scopus 로고
    • Propensity scores: From naive enthusiasm to intuitive understanding
    • (online early
    • Williamson E, Morley R, Lucas A, et al. Propensity scores: from naive enthusiasm to intuitive understanding. Stat Meth Med Res 2011. (online early.
    • (2011) Stat Meth Med Res
    • Williamson, E.1    Morley, R.2    Lucas, A.3
  • 6
    • 84949193513 scopus 로고
    • Reducing bias in observational studies using subclassification on the propensity score
    • Rosenbaum PR, Rubin DB,. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984 79: 516-524.
    • (1984) J Am Stat Assoc , vol.79 , pp. 516-524
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 7
    • 1542334708 scopus 로고    scopus 로고
    • Propensity score modeling strategies for the causal analysis of observational data
    • Hullsiek KH, Louis TA,. Propensity score modeling strategies for the causal analysis of observational data. Biostatistics (Oxford) 2002; 3 (2): 179-193.
    • (2002) Biostatistics (Oxford) , vol.3 , Issue.2 , pp. 179-193
    • Hullsiek, K.H.1    Louis, T.A.2
  • 8
    • 0000180788 scopus 로고
    • A characterization of optimal designs for observational studies
    • Rosenbaum PR,. A characterization of optimal designs for observational studies. J Roy Stat Soc Ser B 1991; 53: 597-610.
    • (1991) J Roy Stat Soc ser B , vol.53 , pp. 597-610
    • Rosenbaum, P.R.1
  • 9
    • 42049093965 scopus 로고    scopus 로고
    • Using full matching to estimate causal effects in nonexperimental studies: Examining the relationship between adolescent marijuana use and adult outcomes
    • Stuart EA, Green KM,. Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes. Develop Psychol 2008; 44 (2): 395-406.
    • (2008) Develop Psychol , vol.44 , Issue.2 , pp. 395-406
    • Stuart, E.A.1    Green, K.M.2
  • 10
    • 4444230264 scopus 로고    scopus 로고
    • Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study
    • Lunceford JK, Davidian M,. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med 2004; 23 (19): 2937-2960.
    • (2004) Stat Med , vol.23 , Issue.19 , pp. 2937-2960
    • Lunceford, J.K.1    Davidian, M.2
  • 11
    • 58849124121 scopus 로고    scopus 로고
    • Effcient sampling approaches to address confounding in database studies
    • Hanley JA, Dendukuri N,. Effcient sampling approaches to address confounding in database studies. Stat Meth Med Res 2009; 18 (1): 81-105.
    • (2009) Stat Meth Med Res , vol.18 , Issue.1 , pp. 81-105
    • Hanley, J.A.1    Dendukuri, N.2
  • 12
    • 50449104524 scopus 로고    scopus 로고
    • Estimation of propensity scores using generalized additive models
    • Woo MJ, Reiter JP, Karr AF,. Estimation of propensity scores using generalized additive models. Stat Med 2008; 27 (19): 3805-3816.
    • (2008) Stat Med , vol.27 , Issue.19 , pp. 3805-3816
    • Woo, M.J.1    Reiter, J.P.2    Karr, A.F.3
  • 13
    • 77953607621 scopus 로고    scopus 로고
    • Propensity score estimation: Neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression
    • Westreich D, Lessler J, Funk MJ,. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol 2010; 63: 826-833.
    • (2010) J Clin Epidemiol , vol.63 , pp. 826-833
    • Westreich, D.1    Lessler, J.2    Funk, M.J.3
  • 14
    • 46349084991 scopus 로고    scopus 로고
    • Evaluating uses of data mining techniques in propensity score estimation: A simulation study
    • Setoguchi S, Schneeweiss S, Brookhart MA, et al. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiol Drug Safety 2008; 17: 546-555.
    • (2008) Pharmacoepidemiol Drug Safety , vol.17 , pp. 546-555
    • Setoguchi, S.1    Schneeweiss, S.2    Brookhart, M.A.3
  • 15
    • 2242479444 scopus 로고    scopus 로고
    • Estimating and using propensity scores with partially missing data
    • D'Agostino RB Jr, Rubin DB,. Estimating and using propensity scores with partially missing data. J Am Stat Assoc 2000; 95 (451): 749-759.
    • (2000) J Am Stat Assoc , vol.95 , Issue.451 , pp. 749-759
    • D'Agostino, R.B.J.1    Rubin, D.B.2
  • 16
    • 34548857107 scopus 로고    scopus 로고
    • Combining propensity score matching and group-based trajectory analysis in an observational study
    • Haviland A, Nagin DS, Rosenbaum PR,. Combining propensity score matching and group-based trajectory analysis in an observational study. Psychol Meth 2007; 12 (3): 247-267.
    • (2007) Psychol Meth , vol.12 , Issue.3 , pp. 247-267
    • Haviland, A.1    Nagin, D.S.2    Rosenbaum, P.R.3
  • 17
    • 67549090540 scopus 로고    scopus 로고
    • Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach
    • Qu Y, Lipkovich I,. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Stat Med 2009; 28 (9): 1402-1414.
    • (2009) Stat Med , vol.28 , Issue.9 , pp. 1402-1414
    • Qu, Y.1    Lipkovich, I.2
  • 19
    • 38349075009 scopus 로고    scopus 로고
    • The multiple adaptations of multiple imputation
    • Reiter JP, Raghunathan TE,. The multiple adaptations of multiple imputation. J Am Stat Assoc 2007; 102: 1462-1471.
    • (2007) J Am Stat Assoc , vol.102 , pp. 1462-1471
    • Reiter, J.P.1    Raghunathan, T.E.2
  • 21
    • 84950455641 scopus 로고
    • Regression with Missing X's: A Review
    • Little RJA,. Regression with Missing X's: A Review. J Am Stat Assoc 1992; 87: 1227-1237.
    • (1992) J Am Stat Assoc , vol.87 , pp. 1227-1237
    • Little, R.J.A.1
  • 22
    • 33748709502 scopus 로고    scopus 로고
    • Using the outcome for imputation of missing predictor values was preferred
    • Moons KGM, Donders RART, Stijnen T, et al. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 2006; 59 (10): 1092-1101.
    • (2006) J Clin Epidemiol , vol.59 , Issue.10 , pp. 1092-1101
    • Moons, K.G.M.1    Donders, R.A.R.T.2    Stijnen, T.3
  • 23
    • 0033476953 scopus 로고    scopus 로고
    • Earnings and employment effects of continuous off-the-job training in east Germany after unification
    • Lechner M,. Earnings and employment effects of continuous off-the-job training in east germany after unification. J Business Econ Stat 1999; 17 (1): 74-90.
    • (1999) J Business Econ Stat , vol.17 , Issue.1 , pp. 74-90
    • Lechner, M.1
  • 24
    • 67249097811 scopus 로고    scopus 로고
    • Type i Error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses
    • (1
    • Austin PC,. Type I Error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. Int J Biostat 2009; 5. (1.
    • (2009) Int J Biostat , vol.5
    • Austin, P.C.1
  • 25
    • 79951705391 scopus 로고    scopus 로고
    • Estimating propensity scores with missing covariate data using general location mixture models
    • Mitra R, Reiter JP,. Estimating propensity scores with missing covariate data using general location mixture models. Stat Med 2011; 30 (6): 627-641.
    • (2011) Stat Med , vol.30 , Issue.6 , pp. 627-641
    • Mitra, R.1    Reiter, J.P.2
  • 26
    • 33745588280 scopus 로고    scopus 로고
    • Interval estimation for treatment effects using propensity score matching
    • Hill J, Reiter JP,. Interval estimation for treatment effects using propensity score matching. Stat Med 2006; 25: 2230-2256.
    • (2006) Stat Med , vol.25 , pp. 2230-2256
    • Hill, J.1    Reiter, J.P.2
  • 27
    • 1542742319 scopus 로고    scopus 로고
    • Combining propensity score matching with additional adjustments for prognostic covariates
    • Rubin DB, Thomas N,. Combining propensity score matching with additional adjustments for prognostic covariates. J Am Stat Assoc 2000; 95 (450): 573-585.
    • (2000) J Am Stat Assoc , vol.95 , Issue.450 , pp. 573-585
    • Rubin, D.B.1    Thomas, N.2
  • 29
    • 84969424547 scopus 로고    scopus 로고
    • Does firm size make a difference? Analysing the effectiveness of R&D subsidies in East Germany
    • Alecke B, Mitze T, Reinkowski J, et al. Does firm size make a difference? Analysing the effectiveness of R&D subsidies in East Germany. German Economic Review 2011.
    • (2011) German Economic Review
    • Alecke, B.1    Mitze, T.2    Reinkowski, J.3
  • 30
    • 33644866343 scopus 로고    scopus 로고
    • Full breastfeeding duration and associated decrease in respiratory tract infection in US children
    • 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.
    • (2006) Pediatrics , vol.117 , Issue.2 , pp. 425-432
    • Chantry, C.J.1    Howard, C.R.2    Auinger, P.3
  • 31
    • 0000133998 scopus 로고
    • An analysis of transformations
    • Box GEP, Cox DR,. An analysis of transformations. J Roy Stat Soc Ser B 1964; 26 (2): 211-252.
    • (1964) J Roy Stat Soc ser B , vol.26 , Issue.2 , pp. 211-252
    • Box, G.E.P.1    Cox, D.R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.