Difference-in-Differences Method in Comparative Effectiveness Research: Utility with Unbalanced Groups

Applied Health Economics and Health Policy

Volumn 14, Issue 4, 2016, Pages 419-429

  Zhou, Huanxue ^a   Taber, Christopher ^b   Arcona, Steve ^c   Li, Yunfeng ^c  

^a KMK Consulting   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c NOVARTIS PHARMA AG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

FINGOLIMOD; GLATIRAMER; IMMUNOSUPPRESSIVE AGENT;

ADULT; ARTICLE; COMPARATIVE EFFECTIVENESS; CONTROLLED STUDY; DATA BASE; DRUG SUBSTITUTION; DRUG WITHDRAWAL; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; MULTIPLE SCLEROSIS; RECURRENCE RISK; RELAPSE; RETROSPECTIVE STUDY; TREATMENT OUTCOME; ADOLESCENT; CHI SQUARE DISTRIBUTION; CLASSIFICATION; ECONOMICS; HEALTH INSURANCE; INSURANCE; PROBABILITY; PROCEDURES; RECURRENT DISEASE; REGRESSION ANALYSIS; UNITED STATES; YOUNG ADULT;

ADOLESCENT; ADULT; CHI-SQUARE DISTRIBUTION; COMPARATIVE EFFECTIVENESS RESEARCH; FEMALE; FINGOLIMOD HYDROCHLORIDE; GLATIRAMER ACETATE; HUMANS; IMMUNOSUPPRESSIVE AGENTS; INSURANCE CLAIM REVIEW; INSURANCE, HEALTH; MALE; MIDDLE AGED; MULTIPLE SCLEROSIS; PROBABILITY; RECURRENCE; REGRESSION ANALYSIS; RETROSPECTIVE STUDIES; UNITED STATES; YOUNG ADULT;

EID: 84976484815     PISSN: 11755652     EISSN: 11791896     Source Type: Journal    
DOI: 10.1007/s40258-016-0249-y     Document Type: Article

References (30)

1. Concato J, Lawler EV, Lew RA, et al. Observational methods in comparative effectiveness research. Am J Med. 2010;123(12 Suppl 1):e16–23.
2. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46(3):399–424.
3. Curtis LH, Hammill BG, Eisenstein EL, et al. Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases. Medical Care. 2007;45(10 Supl 2):S103–7.
4. Lanehart RE, Rodriguez de Gil P, Kim ES, Bellara AP, Kromrey JD, Lee SR. Paper 314-2012: propensity score analysis and assessment of proposensity score approaches using SAS procedures. SAS Global Forum 2012; 2012.
5. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127(8 Pt 2):757–63.
6. Hirano K, Imbens GW. Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv Outcomes Res Methodol. 2001;2:259–78.
7. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med. 2008;27(12):2037–49.
8. Austin PC. Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations. Biometrical J. 2009;51(1):171–84.
9. King G, Nielsen R. Why propensity scores should not be used for matching. 2016. Available at: http://gking.harvard.edu/publications/why-propensity-scores-should-not-be-used-formatching. Accessed 7 Sept 2015.
10. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. Jama. 2014;312(22):2401–2.
11. Bertrand M, Duflo E, Mullainathan S. How much should we trust difference-in-differences estimates? Q J Econ. 2004;119:249–75.
12. Crown WH. Propensity-score matching in economic analyses: comparison with regression models, instrumental variables, residual inclusion, differences-in-differences, and decomposition methods. Appl Health Econ Health Policy. 2014;12(1):7–18.
13. Disanto G, Berlanga AJ, Handel AE, et al. Heterogeneity in multiple sclerosis: scratching the surface of a complex disease. Autoimmun Dis. 2010;2011:932351.
14. Abadie A. Semiparametric difference-in-differences estimators. Rev Econ Stud. 2005;72(1):1–19.
15. Meyer B. Natural and quas-experiments in economics. J Bus Econ Stat. 1995;13(2):151–61.
16. Butler Quint J. White paper health research data for the real world: the MarketScan® Databases. Ann Arbor: Truven Health Analytics Inc; 2015.
17. Chastek BJ, Oleen Burkey M, Lopez-Bresnahan MV. Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims. J Med Econ. 2010;13(4):618–25.
18. Ashenfelter O. Estimating the effect of training programs on earnings. Rev Econ Stat. 1978;60:47–57.
19. Heckman JJ, Smith JA. The pre-programme earnings dip and the determinants of participation in a social programme. Implications for simple programme evaluation strategies. Econ J. 1999;109:313–48.
20. Weiss J, Makonnen R, Sula D. Shifting management of a community volunteer system for improved child health outcomes: results from an operations research study in Burundi. BMC Health Serv Res. 2015;15(Suppl 1):S2.
21. Brenner S, Muula AS, Robyn PJ, et al. Design of an impact evaluation using a mixed methods model—an explanatory assessment of the effects of results-based financing mechanisms on maternal healthcare services in Malawi. BMC Health Serv Res. 2014;14:180.
22. Colla CH, Lewis VA, Gottlieb DJ, et al. Cancer spending and accountable care organizations: evidence from the Physician Group Practice Demonstration. Healthcare (Amst). 2013;1(3–4):100–7.
23. Dubay L, Kenney G. Expanding public health insurance to parents: effects on children’s coverage under Medicaid. Health Serv Res. 2003;38(5):1283–301.
24. McAdam-Marx C, Dahal A, Jennings B, et al. The effect of a diabetes collaborative care management program on clinical and economic outcomes in patients with type 2 diabetes. J Manag Care Spec Pharm. 2015;21(6):452–68.
25. Pereira SK, Kumar P, Dutt V, et al. Protocol for the evaluation of a social franchising model to improve maternal health in Uttar Pradesh, India. Implement Sci. 2015;10:77.
26. Salinas-Rodriguez A, Torres-Pereda Mdel P, Manrique-Espinoza B, et al. Impact of the non-contributory social pension program 70 y mas on older adults’ mental well-being. PLoS One. 2014;9(11):e113085.
27. Siddiqui M, Roberts ET, Pollack CE. The effect of emergency department copayments for Medicaid beneficiaries following the Deficit Reduction Act of 2005. JAMA Intern Med. 2015;175(3):393–8.
28. Rajaram R, Chung JW, Jones AT, et al. Association of the 2011 ACGME resident duty hour reform with general surgery patient outcomes and with resident examination performance. Jama. 2014;312(22):2374–84.
29. Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. Jama. 2014;312(22):2364–73.
30. Jain RMC, Lee H, Wong W. Use of rosiglitazone and pioglitazone immediately after the cardiovascular risk warnings. Res Soc Adm Pharm. 2012;8(1):47–59.