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Volumn 64, Issue 10, 2016, Pages 2065-2073

Use and Interpretation of Propensity Scores in Aging Research: A Guide for Clinical Researchers

Author keywords

confounding; observational research; propensity score

Indexed keywords

DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR;

EID: 84992061294     PISSN: 00028614     EISSN: 15325415     Source Type: Journal    
DOI: 10.1111/jgs.14253     Document Type: Article
Times cited : (48)

References (57)
  • 1
    • 84876291827 scopus 로고    scopus 로고
    • Angiotensin-converting enzyme inhibitors and outcomes in heart failure and preserved ejection fraction
    • Mujib M, Patel K, Fonarow GC et al. Angiotensin-converting enzyme inhibitors and outcomes in heart failure and preserved ejection fraction. Am J Med 2013;126:401–410.
    • (2013) Am J Med , vol.126 , pp. 401-410
    • Mujib, M.1    Patel, K.2    Fonarow, G.C.3
  • 3
    • 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
  • 4
    • 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
  • 5
    • 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;39:33–38.
    • (1985) Am Stat , vol.39 , pp. 33-38
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 6
    • 79951791644 scopus 로고    scopus 로고
    • A tutorial and case study in propensity score analysis: An application to estimating the effect of in-hospital smoking cessation counseling on mortality
    • Austin PC. A tutorial and case study in propensity score analysis: An application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate Behav Res 2011;46:119–151.
    • (2011) Multivariate Behav Res , vol.46 , pp. 119-151
    • Austin, P.C.1
  • 7
    • 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.1
  • 8
    • 84876294116 scopus 로고    scopus 로고
    • Matching by propensity score in cohort studies with three treatment groups
    • Rassen JA, Shelat AA, Franklin JM et al. Matching by propensity score in cohort studies with three treatment groups. Epidemiology 2013;24:401–409.
    • (2013) Epidemiology , vol.24 , pp. 401-409
    • Rassen, J.A.1    Shelat, A.A.2    Franklin, J.M.3
  • 9
    • 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 Saf 2008;17:546–555.
    • (2008) Pharmacoepidemiol Drug Saf , vol.17 , pp. 546-555
    • Setoguchi, S.1    Schneeweiss, S.2    Brookhart, M.A.3
  • 10
    • 74749097452 scopus 로고    scopus 로고
    • Improving propensity score weighting using machine learning
    • Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med 2010;29:337–346.
    • (2010) Stat Med , vol.29 , pp. 337-346
    • Lee, B.K.1    Lessler, J.2    Stuart, E.A.3
  • 12
    • 79959558713 scopus 로고    scopus 로고
    • The implications of propensity score variable selection strategies in pharmacoepidemiology: An empirical illustration
    • Patrick AR, Schneeweiss S, Brookhart MA et al. The implications of propensity score variable selection strategies in pharmacoepidemiology: An empirical illustration. Pharmacoepidemiol Drug Saf 2011;20:551–559.
    • (2011) Pharmacoepidemiol Drug Saf , vol.20 , pp. 551-559
    • Patrick, A.R.1    Schneeweiss, S.2    Brookhart, M.A.3
  • 13
    • 84880191784 scopus 로고    scopus 로고
    • Matching on provider is risky
    • Walker AM. Matching on provider is risky. J Clin Epidemiol 2013;66:S65–S68.
    • (2013) J Clin Epidemiol , vol.66 , pp. S65-S68
    • Walker, A.M.1
  • 14
    • 0037080447 scopus 로고    scopus 로고
    • Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology
    • Hernan MA, Hernandez-Diaz S, Werler MM et al. Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Am J Epidemiol 2002;155:176–184.
    • (2002) Am J Epidemiol , vol.155 , pp. 176-184
    • Hernan, M.A.1    Hernandez-Diaz, S.2    Werler, M.M.3
  • 15
    • 0030862072 scopus 로고    scopus 로고
    • Estimating causal effects from large data sets using propensity scores
    • Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med 1997;127:757–763.
    • (1997) Ann Intern Med , vol.127 , pp. 757-763
    • Rubin, D.B.1
  • 16
    • 0142157169 scopus 로고    scopus 로고
    • Evaluating medication effects outside of clinical trials: New-user designs
    • Ray WA. Evaluating medication effects outside of clinical trials: New-user designs. Am J Epidemiol 2003;158:915–920.
    • (2003) Am J Epidemiol , vol.158 , pp. 915-920
    • Ray, W.A.1
  • 17
    • 33645210305 scopus 로고    scopus 로고
    • Indications for propensity scores and review of their use in pharmacoepidemiology
    • Glynn RJ, Schneeweiss S, Sturmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 2006;98:253–259.
    • (2006) Basic Clin Pharmacol Toxicol , vol.98 , pp. 253-259
    • Glynn, R.J.1    Schneeweiss, S.2    Sturmer, T.3
  • 18
    • 84908094169 scopus 로고    scopus 로고
    • Methods for constructing and assessing propensity scores
    • Garrido MM, Kelley AS, Paris J et al. Methods for constructing and assessing propensity scores. Health Serv Res 2014;49:1701–1720.
    • (2014) Health Serv Res , vol.49 , pp. 1701-1720
    • Garrido, M.M.1    Kelley, A.S.2    Paris, J.3
  • 19
    • 0035044501 scopus 로고    scopus 로고
    • Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: A matched analysis using propensity scores
    • Normand ST, Landrum MB, Guadagnoli E et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: A matched analysis using propensity scores. J Clin Epidemiol 2001;54:387–398.
    • (2001) J Clin Epidemiol , vol.54 , pp. 387-398
    • Normand, S.T.1    Landrum, M.B.2    Guadagnoli, E.3
  • 20
    • 34249885738 scopus 로고    scopus 로고
    • Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference
    • Ho DE, Imai K, King G et al. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal 2007;15:199–236.
    • (2007) Polit Anal , vol.15 , pp. 199-236
    • Ho, D.E.1    Imai, K.2    King, G.3
  • 21
    • 40549104115 scopus 로고    scopus 로고
    • Misunderstandings between experimentalists and observationalists about causal inference
    • Imai K, King G, Stuart EA. Misunderstandings between experimentalists and observationalists about causal inference. J R Stat Soc Ser A Stat Soc 2008;171:481–502.
    • (2008) J R Stat Soc Ser A Stat Soc , vol.171 , pp. 481-502
    • Imai, K.1    King, G.2    Stuart, E.A.3
  • 22
    • 33846842327 scopus 로고    scopus 로고
    • A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: A Monte Carlo study
    • 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 2007;26:734–753.
    • (2007) Stat Med , vol.26 , pp. 734-753
    • Austin, P.C.1    Grootendorst, P.2    Anderson, G.M.3
  • 23
    • 34249863765 scopus 로고    scopus 로고
    • The performance of different propensity score methods for estimating marginal odds ratios
    • Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Stat Med 2007;26:3078–3094.
    • (2007) Stat Med , vol.26 , pp. 3078-3094
    • Austin, P.C.1
  • 24
    • 72149119483 scopus 로고    scopus 로고
    • The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies
    • Austin PC. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Med Decis Making 2009;29:661–677.
    • (2009) Med Decis Making , vol.29 , pp. 661-677
    • Austin, P.C.1
  • 25
    • 79953851739 scopus 로고    scopus 로고
    • Doubly robust estimation of causal effects
    • Funk MJ, Westreich D, Wiesen C et al. Doubly robust estimation of causal effects. Am J Epidemiol 2011;173:761–767.
    • (2011) Am J Epidemiol , vol.173 , pp. 761-767
    • Funk, M.J.1    Westreich, D.2    Wiesen, C.3
  • 27
    • 77958600686 scopus 로고    scopus 로고
    • Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies
    • Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011;10:150–161.
    • (2011) Pharm Stat , vol.10 , pp. 150-161
    • Austin, P.C.1
  • 28
    • 84893777424 scopus 로고    scopus 로고
    • A comparison of 12 algorithms for matching on the propensity score
    • Austin PC. A comparison of 12 algorithms for matching on the propensity score. Stat Med 2014;33:1057–1069.
    • (2014) Stat Med , vol.33 , pp. 1057-1069
    • Austin, P.C.1
  • 29
    • 77957806232 scopus 로고    scopus 로고
    • Matching methods for causal inference: A review and a look forward
    • Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci 2010;25:1–21.
    • (2010) Stat Sci , vol.25 , pp. 1-21
    • Stuart, E.A.1
  • 30
    • 35448972461 scopus 로고    scopus 로고
    • Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: A systematic review and suggestions for improvement
    • Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: A systematic review and suggestions for improvement. J Thorac Cardiovasc Surg 2007;134:1128–1135.
    • (2007) J Thorac Cardiovasc Surg , vol.134 , pp. 1128-1135
    • Austin, P.C.1
  • 31
    • 44649173785 scopus 로고    scopus 로고
    • A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003
    • Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med 2008;27:2037–2049.
    • (2008) Stat Med , vol.27 , pp. 2037-2049
    • Austin, P.C.1
  • 32
    • 56349115008 scopus 로고    scopus 로고
    • On the failure of the bootstrap for matching estimators
    • Abadie A, Imbens GW. On the failure of the bootstrap for matching estimators. Econometrica 2008;76:1537–1557.
    • (2008) Econometrica , vol.76 , pp. 1537-1557
    • Abadie, A.1    Imbens, G.W.2
  • 33
    • 0033839024 scopus 로고    scopus 로고
    • Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men
    • Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000;11:561–570.
    • (2000) Epidemiology , vol.11 , pp. 561-570
    • Hernan, M.A.1    Brumback, B.2    Robins, J.M.3
  • 34
    • 0033847784 scopus 로고    scopus 로고
    • Marginal structural models and causal inference in epidemiology
    • Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11:550–560.
    • (2000) Epidemiology , vol.11 , pp. 550-560
    • Robins, J.M.1    Hernan, M.A.2    Brumback, B.3
  • 35
    • 79953304087 scopus 로고    scopus 로고
    • Weight trimming and propensity score weighting
    • Lee BK, Lessler J, Stuart EA. Weight trimming and propensity score weighting. PLoS ONE 2011;6:e18174.
    • (2011) PLoS ONE , vol.6
    • Lee, B.K.1    Lessler, J.2    Stuart, E.A.3
  • 36
    • 10844272276 scopus 로고    scopus 로고
    • Propensity score estimation with boosted regression for evaluating causal effects in observational studies
    • McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods 2004;9:403–425.
    • (2004) Psychol Methods , vol.9 , pp. 403-425
    • McCaffrey, D.F.1    Ridgeway, G.2    Morral, A.R.3
  • 37
    • 51749124303 scopus 로고    scopus 로고
    • Constructing inverse probability weights for marginal structural models
    • Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol 2008;168:656–664.
    • (2008) Am J Epidemiol , vol.168 , pp. 656-664
    • Cole, S.R.1    Hernan, M.A.2
  • 38
    • 31344433620 scopus 로고    scopus 로고
    • Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect
    • Kurth T, Walker AM, Glynn RJ et al. 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.
    • (2006) Am J Epidemiol , vol.163 , pp. 262-270
    • Kurth, T.1    Walker, A.M.2    Glynn, R.J.3
  • 39
    • 0024547831 scopus 로고
    • Performance of tests of significance based on stratification by a multivariate confounder score or by a propensity score
    • Cook EF, Goldman L. Performance of tests of significance based on stratification by a multivariate confounder score or by a propensity score. J Clin Epidemiol 1989;42:317–324.
    • (1989) J Clin Epidemiol , vol.42 , pp. 317-324
    • Cook, E.F.1    Goldman, L.2
  • 40
    • 18844452973 scopus 로고    scopus 로고
    • Propensity score methods gave similar results to traditional regression modeling in observational studies: A systematic review
    • Shah BR, Laupacis A, Hux JE et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: A systematic review. J Clin Epidemiol 2005;58:550–559.
    • (2005) J Clin Epidemiol , vol.58 , pp. 550-559
    • Shah, B.R.1    Laupacis, A.2    Hux, J.E.3
  • 41
    • 33645226210 scopus 로고    scopus 로고
    • 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
    • Sturmer T, Joshi M, Glynn RJ et al. 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.
    • (2006) J Clin Epidemiol , vol.59 , pp. 437-447
    • Sturmer, T.1    Joshi, M.2    Glynn, R.J.3
  • 42
    • 79958704133 scopus 로고    scopus 로고
    • An introduction to propensity score methods for reducing the effects of confounding in observational studies
    • Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011;46:399–424.
    • (2011) Multivariate Behav Res , vol.46 , pp. 399-424
    • Austin, P.C.1
  • 43
    • 0027717184 scopus 로고
    • Effects of misspecification of the propensity score on estimators of treatment effect
    • Drake C. Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics 1993;49:1231–1236.
    • (1993) Biometrics , vol.49 , pp. 1231-1236
    • Drake, C.1
  • 44
    • 0041626110 scopus 로고    scopus 로고
    • Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders
    • Cepeda MS, Boston R, Farrar JT et al. 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.
    • (2003) Am J Epidemiol , vol.158 , pp. 280-287
    • Cepeda, M.S.1    Boston, R.2    Farrar, J.T.3
  • 45
    • 84992124768 scopus 로고    scopus 로고
    • Regression adjustment
    • Confounding Fitzmaurice G. Regression adjustment. Nutrition 2006;22:581–583.
    • (2006) Nutrition , vol.22 , pp. 581-583
    • Confounding, F.G.1
  • 46
    • 0035761763 scopus 로고    scopus 로고
    • Using propensity scores to help design observational studies: Application to the tobacco litigation
    • Rubin D. Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Serv Outcomes Res Methodol 2001;2:169–188.
    • (2001) Health Serv Outcomes Res Methodol , vol.2 , pp. 169-188
    • Rubin, D.1
  • 47
    • 57849113940 scopus 로고    scopus 로고
    • Cardiovascular disease care in the nursing home: The need for better evidence for outcomes of care and better quality for processes of care
    • Ahmed A, Ekundayo OJ. Cardiovascular disease care in the nursing home: The need for better evidence for outcomes of care and better quality for processes of care. J Am Med Dir Assoc 2009;10:1–3.
    • (2009) J Am Med Dir Assoc , vol.10 , pp. 1-3
    • Ahmed, A.1    Ekundayo, O.J.2
  • 48
    • 33744495314 scopus 로고    scopus 로고
    • Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics
    • Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf 2006;15:291–303.
    • (2006) Pharmacoepidemiol Drug Saf , vol.15 , pp. 291-303
    • Schneeweiss, S.1
  • 49
    • 84886631572 scopus 로고    scopus 로고
    • An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research
    • Liu W, Kuramoto SJ, Stuart EA. An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research. Prev Sci 2013;14:570–580.
    • (2013) Prev Sci , vol.14 , pp. 570-580
    • Liu, W.1    Kuramoto, S.J.2    Stuart, E.A.3
  • 50
    • 65449149106 scopus 로고    scopus 로고
    • Statin adherence and risk of accidents: A cautionary tale
    • Dormuth CR, Patrick AR, Shrank WH et al. Statin adherence and risk of accidents: A cautionary tale. Circulation 2009;119:2051–2057.
    • (2009) Circulation , vol.119 , pp. 2051-2057
    • Dormuth, C.R.1    Patrick, A.R.2    Shrank, W.H.3
  • 51
    • 34848820064 scopus 로고    scopus 로고
    • Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results
    • Schneeweiss S, Patrick AR, Sturmer T et al. Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care 2007;45:S131–142.
    • (2007) Med Care , vol.45 , pp. S131-142
    • Schneeweiss, S.1    Patrick, A.R.2    Sturmer, T.3
  • 52
    • 84936761499 scopus 로고    scopus 로고
    • Controlling time-dependent confounding by health status and frailty: Restriction versus statistical adjustment
    • McGrath LJ, Ellis AR, Brookhart MA. Controlling time-dependent confounding by health status and frailty: Restriction versus statistical adjustment. Am J Epidemiol 2015;182:17–25.
    • (2015) Am J Epidemiol , vol.182 , pp. 17-25
    • McGrath, L.J.1    Ellis, A.R.2    Brookhart, M.A.3
  • 53
    • 0035658203 scopus 로고    scopus 로고
    • Zolpidem use and hip fractures in older people
    • Wang PS, Bohn RL, Glynn RJ et al. Zolpidem use and hip fractures in older people. J Am Geriatr Soc 2001;49:1685–1690.
    • (2001) J Am Geriatr Soc , vol.49 , pp. 1685-1690
    • Wang, P.S.1    Bohn, R.L.2    Glynn, R.J.3
  • 54
    • 59149087852 scopus 로고    scopus 로고
    • Assessing residual confounding of the association between antipsychotic medications and risk of death using survey data
    • Schneeweiss S, Setoguchi S, Brookhart MA et al. Assessing residual confounding of the association between antipsychotic medications and risk of death using survey data. CNS Drugs 2009;23:171–180.
    • (2009) CNS Drugs , vol.23 , pp. 171-180
    • Schneeweiss, S.1    Setoguchi, S.2    Brookhart, M.A.3
  • 55
    • 8744265037 scopus 로고    scopus 로고
    • Association between SSRI use and hip fractures and the effect of residual confounding bias in claims database studies
    • Schneeweiss S, Wang PS. Association between SSRI use and hip fractures and the effect of residual confounding bias in claims database studies. J Clin Psychopharmacol 2004;24:632–638.
    • (2004) J Clin Psychopharmacol , vol.24 , pp. 632-638
    • Schneeweiss, S.1    Wang, P.S.2
  • 56
    • 23444460132 scopus 로고    scopus 로고
    • Claims data studies of sedative-hypnotics and hip fractures in older people: Exploring residual confounding using survey information
    • Schneeweiss S, Wang PS. Claims data studies of sedative-hypnotics and hip fractures in older people: Exploring residual confounding using survey information. J Am Geriatr Soc 2005;53:948–954.
    • (2005) J Am Geriatr Soc , vol.53 , pp. 948-954
    • Schneeweiss, S.1    Wang, P.S.2
  • 57
    • 78650778119 scopus 로고    scopus 로고
    • Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders
    • Vanderweele TJ, Arah OA. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology 2011;22:42–52.
    • (2011) Epidemiology , vol.22 , pp. 42-52
    • Vanderweele, T.J.1    Arah, O.A.2


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