A comparison of alternative strategies for choosing control populations in observational studies

Health Services and Outcomes Research Methodology

Volumn 15, Issue 3-4, 2015, Pages 157-181

  Steventon, Adam ^a,b   Grieve, Richard ^a   Sekhon, Jasjeet S ^c  

^a LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^b Health Foundation   (United Kingdom)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Program evaluation; Propensity score matching; Quasi experiments

Indexed keywords

ALGORITHM; ARTICLE; CALCULATION; CALIBRATION; CASE STUDY; COHORT ANALYSIS; HEALTH SERVICE; HOSPITAL ADMISSION; MATHEMATICAL ANALYSIS; OBSERVATIONAL STUDY; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; SCORING SYSTEM; SIMULATION; SOCIOECONOMICS;

EID: 84941732038     PISSN: 13873741     EISSN: 15729400     Source Type: Journal    
DOI: 10.1007/s10742-014-0135-8     Document Type: Article

References (42)

1. Abadie, A., Diamond, A., Hainmueller, J.: Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J. Am. Stat. Assoc. 105, 493–505 (2010)
2. Abadie, A., Imbens, G.W.: A Martingale representation for matching estimators. J. Am. Stat. Assoc. 107, 833–843 (2012)
3. Agresti, A., Min, Y.: Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data. Stat. Med. 23, 65–75 (2004)
4. Austin, P.C.: A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat. Med. 27, 2037–2049 (2008)
5. Austin, P.C.: Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28, 3083–3107 (2009)
6. Austin, P.C., Grootendorst, P., Anderson, G.M.: 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. 26, 734–753 (2007)
7. Bang, H., Robins, J.M.: Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962–973 (2005)
8. Billings, J., Dixon, J., Mijanovich, T., Wennberg, D.: Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 333, 327 (2006)
9. Campbell, D.T.: Perspective: artifact and control. In: Rosenthal, R., Rosnow, R.L. (eds.) Artifacts in behavioral research, pp. 264–288. Oxford University Press, Oxford (1969)
10. Cook, T.D., Shadish, W.R., Wong, V.C.: Three conditions under which experiments and observational studies produce comparable causal estimates: new findings from within-study comparisons. J. Policy Anal. Manage. 27, 724–750 (2008)
11. Deeks, J.J., Dinnes, J., D’Amico, R., Sowden, A.J., Sakarovitch, C., Song, F., Petticrew, M., Altman, D.G.: Evaluating non-randomised intervention studies. Health Technol. Assess. 7, 1–188 (2003)
12. Drake, C.: Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics 49, 1231–1236 (1993)
13. Glazerman, S., Levy, D.M., Myers, D.: Nonexperimental versus experimental estimates of earnings impacts. Ann. Am. Acad. Pol. Soc. Sci. 589, 63–93 (2003)
14. Griswold, M.E., Localio, A.R.: Propensity score adjustment with multilevel data: setting your sites on decreasing selection bias. Ann. Intern. Med. 152, 393–396 (2010)
15. Hansen, B.B.: The prognostic analogue of the propensity score. Biometrika 95, 481–488 (2008)
16. Harrison, D.A., Gao, H., Welch, C.A., Rowan, K.M.: The effects of critical care outreach services before and after critical care: a matched-cohort analysis. J. Crit. Care 25, 196–204 (2010)
17. Hartman, E., Grieve, R., Ramsahai, R., Sekhon, J.S.: From SATE to PATT: combining experimental with observational studies to estimate population treatment effects. J. R. Stat. Soc. Ser. A Stat. Soc. (forthcoming). doi:10.1111/rssa.12094
18. Ho, D.E., Imai, K., King, G., Stuart, E.A.: Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit. Anal. 15, 199–236 (2007)
19. Imai, K., King, G., Stuart, E.A.: Misunderstandings between experimentalists and observationalists about causal inference. J. R. Stat. Soc. Ser. A Stat. Soc. 171, 481–502 (2008)
20. Joynt, K.E., Jha, A.K.: Thirty-day readmissions—truth and consequences. N. Engl. J. Med. 366, 1366–1369 (2012)
21. Larsen, K., Merlo, J.: Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am. J. Epidemiol. 161, 81–88 (2005)
22. Lu, B., Rosenbaum, P.R.: Optimal pair matching with two control groups. J. Comput. Graph. Stat. 13, 422–434 (2004)
23. McConnell, K.J., Wallace, N.T., Gallia, C.A., Smith, J.A.: Effect of eliminating behavioral health benefits for selected medicaid enrollees. Health Serv. Res. 43, 1348–1365 (2008)
24. Miettinen, O.S.: Variance and dissent: the “case–control” study: valid selection of subjects. J. Chronic Dis. 38, 543–548 (1985)
25. Nelson, L.: Lessons from Medicare’s demonstration projects on disease management and care coordination. Congressional Budget Office, Washington (2012)
26. Normand, S.-L.T., Landrum, M.B., Guadagnoli, E., Ayanian, J.Z., Ryan, T.J., Cleary, P.D., McNeil, B.J.: Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J. Clin. Epidemiol. 54, 387–398 (2001)
27. Office for National Statistics: 2001 area classification of health areas—corresponding areas. Office for National Statistics, London (2010)
28. Roland, M., Lewis, R., Steventon, A., Abel, G., Adams, J., Bardsley, M., Brereton, L., Chitnis, X., Conklin, A., Staetsky, L., Tunkel, S., Ling, T.: Case management for at-risk elderly patients in the English integrated care pilots: observational study of staff and patient experience and secondary care utilisation. Int. J. Integr. Care. 12, e130 (2012)
29. Rosenbaum, P.R.: The role of a second control group in an observational study. Stat. Sci. 2, 292–316 (1987)
30. Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)
31. Rosenbaum, P.R., Rubin, D.B.: Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am. Stat. 39, 33–38 (1985)
32. Rubin, D.B.: Bayesian inference for causal effects: the role of randomization. Ann. Stat. 6, 34–58 (1978)
33. Rubin, D.B.: The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat. Med. 26, 20–36 (2007)
34. Rubin, D.B.: For objective causal inference, design trumps analysis. Ann. Appl. Stat. 2, 808–840 (2008)
35. Rubin, D.B.: On the limitations of comparative effectiveness research. Stat. Med. 29, 1991–1995 (2010)
36. Sekhon, J.S., Grieve, R.D.: A matching method for improving covariate balance in cost-effectiveness analyses. Health Econ. 21, 695–714 (2012)
37. Shadish, W.R., Clark, M.H., Steiner, P.M.: Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. J. Am. Stat. Assoc. 103, 1334–1343 (2008)
38. Steventon, A., Bardsley, M., Billings, J., Georghiou, T., Lewis, G.: An evaluation of the impact of community-based interventions on hospital use: a case study of eight partnership for older people projects. Nuffield Trust, London (2011)
39. Steventon, A., Bardsley, M., Billings, J., Georghiou, T., Lewis, G.H.: The role of matched controls in building an evidence base for hospital-avoidance schemes: a retrospective evaluation. Health Serv. Res. 47, 1679–1698 (2012)
40. Stuart, E.A., Rubin, D.B.: Matching with multiple control groups with adjustment for group differences. J. Educ. Behav. Stat. 33, 279–306 (2008)
41. Stukel, T.A., Fisher, E.S., Wennberg, D.E., Alter, D.A., Gottlieb, D.J., Vermeulen, M.J.: Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA 297, 278–285 (2007)
42. Windle, K., Wagland, R., Forder, J., D’Amico, F., Janssen, D., Wistow, G.: The national evaluation of partnerships for older people projects: executive summary. Personal Social Services Research Unit, University of Kent, Canterbury (2009)