Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research

Psychological Methods

Volumn 15, Issue 3, 2010, Pages 234-249

  Harder, Valerie S ^a   Stuart, Elizabeth A ^b   Anthony, James C ^c  

^a UNIVERSITY OF VERMONT COLLEGE OF MEDICINE   (United States)

^b JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^c MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Cannabis; Causal inference; Observational studies; Standardized bias

Indexed keywords

ADOLESCENT; ARTICLE; BEHAVIORAL RESEARCH; CANNABIS ADDICTION; CHILD; COMPUTER PROGRAM; EPIDEMIOLOGY; HUMAN; METHODOLOGY; OUTCOME ASSESSMENT; PROPENSITY SCORE; PSYCHOLOGICAL ASPECT; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICS;

ADOLESCENT; BEHAVIORAL RESEARCH; BIAS (EPIDEMIOLOGY); CAUSALITY; CHILD; HUMANS; MARIJUANA ABUSE; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); PROPENSITY SCORE; REGRESSION ANALYSIS; SOFTWARE;

EID: 77956807205     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0019623     Document Type: Article

References (75)

1. Allison P.D. Missing data (Sage University Papers Series on Quantitative Applications in the Social Sciences, 07-136) 2001, Sage, Thousand Oaks, CA.
2. Diagnostic and statistical manual of mental disorders 1994, American Psychiatric Association, Author, Washington, DC. 4th ed.
3. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees 1984, Wadsworth & Brooks/Cole, Pacific Grove, CA.
4. Casella G., Berger R.L. Statistical inference 2002, Duxbury Press, Pacific Grove, CA. 2nd ed.
5. Cochran W.G. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 1968, 24:295-313. doi: 10.2307/2528036.
6. Cochran W.G., Rubin D.B. Controlling bias in observational studies: A review. Sankhya, Series A 1973, 35:417-446.
7. D'Agostino R.B., Lang W., Walkup M., Morgan T. Examining the impact of missing data on propensity score estimation in determining the effectiveness of self-monitoring of blood glucose (SMBG). Health Services and Outcomes Research Methodology 2001, 2:291-315.
8. D'Agostino R.B., Rubin D.B. Estimating and using propensity scores with partially missing data. Journal of the American Statistical Association 2000, 95:749-759. doi: 10.2307/2669455.
9. Dehejia R.H., Wahba S. Propensity score matching methods for non-experimental causal studies (NBER Working Paper 6829) 1998, Retrieved from http://www.nber.org/papers/w6829.
10. Dehejia R.H., Wahba S. Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association 1999, 94:1053-1062. doi: 10.2307/2669919.
11. Diamond A., Sekhon J.S. Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies (Institute of Governmental Studies Paper WP2006-35) 2006, Retrieved from http://repositories.cdlib.org/igs/WP2006-35.
12. Friedman J.H. Greedy function approximation: A gradient boosting machine. Annals of Statistics 2001, 29:1189-1232. Retrieved from http://www.jstor.org/stable/2699986.
13. Graham J.W. Missing data analysis: Making it work in the real world. Annual Review of Psychology 2009, 60:549-576. doi: 10.1146/annurev.psych.58.110405.085530.
14. Gu X., Rosenbaum P.R. Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics 1993, 2:405-420. doi: 10.2307/1390693.
15. Hansen B.B. Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association 2004, 99:609-618. doi: 10.1198/016214504000000647.
16. Hansen B.B. Optmatch: Functions for optimal matching (R package Version 0.3-4) [Computer software] 2009, R Foundation for Statistical Computing, Vienna, Austria, Retrieved from http://cran.r-project.org/web/packages/optmatch/index.html.
17. Hansen B.B., Bowers J. Covariate balance in simple, stratified and clustered comparative studies. Statistical Science 2008, 23:219-235.
18. Hansen, B. B., & Kleyman, Y. (2008, January). Deconfounding small quasi-experiments using propensity scores and other dimension reduction techniques. Presentation at the International Conference on Health Policy Statistics, Philadelphia, PA. doi: 10.1214/08-STS254.
19. Harder V.S., Stuart E.A., Anthony J.C. Adolescent cannabis problem use and young adult depression: A gender stratified propensity score analysis. American Journal of Epidemiology 2008, 168:592-601. doi: 10.1093/aje/kwn184.
20. Harrell F.E. Regression modeling strategies 2001, Springer-Verlag, New York, NY.
21. Haviland A., Nagin D.S., Rosenbaum P.R. Combining propensity score matching and group-based trajectory analysis in an observational study. Psychological Methods 2007, 12:247-267. doi: 10.1037/1082-989X.12.3.247.
22. Heckman J.J., Ichimura H., Todd P.E. Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies 1997, 64:605-654. doi: 10.2307/2971733.
23. Heckman J.J., Ichimura H., Todd P.E. Matching as an econometric evaluation estimator. Review of Economic Studies 1998, 65:261-294. doi: 10.1111/1467-937X.00044.
24. Hirano K., Imbens G., Ridder G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 2003, 71:1161-1189. doi: 10.1111/1468-0262.00442.
25. Ho D., Imai K., King G., Stuart E.A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 2007, 15:199-236. doi: 10.1093/pan/mpl013.
26. Ho D., Imai K., King G., Stuart E.A. MatchIt: Nonparametric preprocessing for parametric casual inference (R Package Version 2.2-13) [Computer software] 2007, Retrieved from http://gking.harvard.edu/matchit.
27. Ialongo N.S., Koenig-McNaught A.L., Wagner B.M., Pearson J.L., McCreary B.K., Poduska J., Kellam S. African American children's reports of depressed mood, hopelessness, and suicidal ideation and later suicide attempts. Suicide and Life-Threatening Behaviors 2004, 34:395-407. doi: 10.1521/suli.34.4.395.53743.
28. Imai K., King G., Stuart E.A. Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A 2008, 171:481-502. doi: 10.1111/j.1467-985X.2007.00527.x.
29. Imai K., van Dyk D.A. Causal inference with general treatment regimes: Generalizing the propensity score. Journal of the American Statistical Association 2004, 99:854-866. doi: 10.1198/016214504000001187.
30. Imbens G. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics 2004, 86:4-29.
31. Kellam S.G., Anthony J.C. Targeting early antecedents to prevent tobacco smoking: Findings from an epidemiologically based randomized field trial. American Journal of Public Health 1998, 88:1490-1495. doi: 10.2105/AJPH.88.10.1490.
32. Kellam S.G., Rebok G.W. Building developmental and etiological theory through epidemiologically based preventive intervention trials. Preventing antisocial behavior: Interventions from birth through adolescence 1992, 162-195. Guilford Press, New York, NY. J. McCord, R.E. Tremblay (Eds.).
33. Kellam S.G., Werthamer-Larsson L., Dolan L.J., Brown C.H., Mayer L.S., Rebok G.W., Edelsohn G. Developmental epidemiologically based preventive trials: Baseline modeling of early target behaviors and depressive symptoms. American Journal of Community Psychology 1991, 19:563-584. doi: 10.1007/BF00937992.
34. Kurth T., Walker A.M., Glynn R.J., Chan K.A., Gaziano J.M., Berger K., Robins J.M. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. American Journal of Epidemiology 2006, 163:262-270. doi: 10.1093/aje/kwj047.
35. Lipsey M.W., Cordray D.S. Evaluation methods for social intervention. Annual Review of Psychology 2000, 51:345-375. doi: 10.1146/annurev.psych.51.1.345.
36. Lunceford J.K., Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine 2004, 23:2937-2960. doi: 10.1002/sim.1903.
37. Madigan D., Ridgeway G. Discussion of "least angle regression" by Efron et al. Annals of Statistics 2004, 32:465-469.
38. McCaffrey D.F., Ridgeway G., Morral A.R. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods 2004, 9:403-425. doi: 10.1037/1082-989X.9.4.403.
39. Morgan S.L., Winship C. Counterfactuals and causal inference: Methods and principles for social research 2007, Cambridge University Press, New York, NY.
40. Potter F.J. A study of procedures to identify and trim extreme sampling weights. Proceedings of the Section on Survey Research Methods of the American Statistical Association 1990, 225-230. American Statistical Association, Alexandria, VA.
41. Potter F.J. The effect of weight trimming on nonlinear survey estimates. Proceedings of the Section on Survey Research Methods of the American Statistical Association 1993, 758-763. American Statistical Association, Alexandria, VA.
42. Qu Y., Lipkovich H. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Statistics in Medicine 2009, 28:1402-1414. doi: 10.1002/sim.3549.
43. R: A language and environment for statistical computing [Computer software] 2007, R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria, Retrieved from http://www.cran.r-project.org.
44. Reed P.L., Anthony J.C., Breslau N. Incidence of drug problems in young adults exposed to trauma and posttraumatic stress disorder: Do early life experiences and predispositions matter?. Archives of General Psychiatry 2007, 64:1435-1442. doi: 10.1001/archpsyc.64.12.1435.
45. Reed P.L., Storr C.L., Anthony J.C. Drug dependence enviromics: Job strain in the work environment and risk of becoming drug-dependent. American Journal of Epidemiology 2002, 163:404-411. doi: 10.1093/aje/kwj064.
46. Ridgeway G. The state of boosting. Computing Science and Statistics 1999, 31:172-181.
47. Ridgeway G. GBM 1.6-3 package manual 2007, R Foundation for Statistical Computing, Vienna, Austria, Retrieved from http://cran.r-project.org.
48. Ridgeway G., McCaffrey D., Morral A. Twang: Toolkit for weighting and analysis of nonequivalent groups (R Package Version 1.0-1) [Computer software] 2006, R Foundation for Statistical Computing, Vienna, Austria, Retrieved from http://cran.r-project.org/web/packages/twang/index.html.
49. Robins J.M. Marginal structural models. 1997 Proceedings of the Section on Bayesian Statistical Science 1998, 1-10. American Statistical Association, Alexandria, VA.
50. Robins J.M. Marginal structural models versus structural nested models as tools for causal inference. Statistical models in epidemiology: The environment and clinical trials 1999, 95-134. Springer-Verlag, New York, NY. E. Halloran, D. Berry (Eds.).
51. Robins J.M., Hernan M.A., Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000, 11:550-560. doi: 10.1097/00001648-200009000-00011.
52. Robins J.M., Rotnitzky A. Comment on "Inference for Semiparametric Models: Some Questions and an Answer," by P. J. Bickel and J. Kwon. Statistica Sinica 2001, 11:920-936.
53. Rosenbaum P.R. Sensitivity analysis for matched case-control studies. Biometrics 1991, 47:87-100. doi: 10.2307/2532498.
54. Rosenbaum P.R. Observational studies 2002, Springer-Verlag, New York, NY. 2nd ed.
55. Rosenbaum P.R. Design of observational studies 2009, Springer, New York, NY.
56. Rosenbaum P.R., Rubin D.B. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society Series B (Methodological) 1983, 45:212-218.
57. Rosenbaum P.R., Rubin D.B. The central role of the propensity score in observational studies for causal effects. Biometrika 1983, 70:41-55. doi: 10.1093/biomet/70.1.41.
58. Rosenbaum P.R., Rubin D.B. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984, 79:516-524. doi: 10.2307/2288398.
59. Rosenbaum P.R., Rubin D.B. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician 1985, 39:33-38. doi: 10.2307/2683903.
60. Rubin D.B. Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology 2001, 2:169-188.
61. Rubin D.B. On principles for modeling propensity scores in medical research. Pharmacoepidemiology and Drug Safety 2004, 13:855-857. doi: 10.1002/pds.968.
62. Rubin D.B. For objective causal inference, design trumps analysis. Annals of Applied Statistics 2008, 2:808-840. doi: 10.1214/08-AOAS187.
63. Rubin D.B., Thomas N. Matching using estimated propensity scores: Relating theory to practice. Biometrics 1996, 52:249-264. doi: 10.2307/2533160.
64. Rubin D.B., Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association 2000, 95:573-585. doi: 10.2307/2669400.
65. SAS (Release 9). [Computer software] 2004, SAS Institute, Cary, NC.
66. Schafer J.L., Kang J. Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods 2008, 13:279-313. doi: 10.1037/a0014268.
67. Shadish W.R., Clark M.H., Steiner P.M. Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. Journal of the American Statistical Association 2008, 103:1334-1343. doi: 10.1198/016214508000000733.
68. Smith H. Matching with multiple controls to estimate treatment effects in observational studies. Sociological Methodology 1997, 27:325-353. doi: 10.1111/1467-9531.271030.
69. Snedecor G.W., Cochran W.G. Statistical methods 1980, Iowa State University Press, Ames, IA. 7th ed.
70. Song J., Belin T.R., Lee M.B., Gao X., Rotheram-Borus M.J. Handling baseline differences and missing items in a longitudinal study of HIV risk among runaway youths. Health Services and Outcomes Research Methodology 2001, 2:317-329.
71. Stata Statistical Software (Release 9). [Computer software] 2005, StataCorp, Author, College Station, TX.
72. Stuart, E. A. (in press). Matching methods for causal inference: A review and a look forward. Statistical Science.
73. Stuart E.A., Green K.M. Using full matching to estimate causal effects in non-experimental studies: Examining the relationship between adolescent marijuana use and adult outcomes. Developmental Psychology 2008, 44:395-406. doi: 10.1037/0012-1649.44.2.395.
74. Stuart E.A., Rubin D.B. Best practices in quasi-experimental designs: Matching methods for causal inference. Best practices in quantitative social science 2007, 155-176. Sage, Thousand Oaks, CA. J. Osborne (Ed.).
75. Composite International Diagnostic Interview 1990, World Health Organization, Author, Geneva, Switzerland.