Avoiding randomization failure in program evaluation, with application to the medicare health support program

Population Health Management

Volumn 14, Issue SUPPL. 1, 2011, Pages

  King, Gary ^a   Nielsen, Richard ^a   Coberley, Carter ^b   Pope, James E ^b   Wells, Aaron ^b  

^a HARVARD UNIVERSITY   (United States)

^b HEALTHWAYS INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLINICAL EVALUATION; HEALTH CARE COST; HEALTH CARE MANAGEMENT; HEALTH PROGRAM; HUMAN; MEDICARE; MEDICARE HEALTH SUPPORT PROGRAM; METHODOLOGY; RANDOM ERROR; RANDOMIZATION; STUDY DESIGN;

GOVERNMENT PROGRAMS; HUMANS; MEDICARE; PILOT PROJECTS; PROGRAM EVALUATION; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN; SELECTION BIAS; UNITED STATES;

EID: 79951860117     PISSN: 19427891     EISSN: 19427905     Source Type: Journal    
DOI: 10.1089/pop.2010.0074     Document Type: Article

References (17)

1. King G, Gakidou E, Ravishankar N, et al. A "politically robust" experimental design for public policy evaluation, with application to the Mexican universal health insurance program. J Policy Anal Manag. 2007;26:479-506.
2. Cromwell J, McCall N, Burton J. Evaluation of Medicare health support chronic disease pilot program. Health Care Financ Rev. 2008;30:47-60.
3. Iacus SM, King G, Porro G. CEM: Coarsened exact matching software. Available at: http://gking.harvard.edu/cem. Accessed December 17, 2010.
4. Box GEP, Hunter WG, Hunter JS. Statistics for Experimenters. New York: Wiley-Interscience; 1978.
5. Ho D, Imai K, King G, Stuart E. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15:199-236.
6. Imai K, King G, Nall C. The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation. Statist Sci. 2009;24:29-53.
7. Rubin DB. Bias reduction using mahalanobis-metric matching. Biometrics. 1980;36:293-298.
8. Cornfield J. Randomization by group: A formal analysis. Am J Epidemiol. 1978;108:100-102. (Pubitemid 9148062)
9. Gelman A. Treatment effects in before-after data. In: Gelman A, Meng X-L, eds. Applied Bayesian Modeling and Causal Inference from an Incomplete Data Perspective. London: Wiley; 2004:195-202.
10. McCall N, Cromwell J, Urato C, Rabiner D. Evaluation of phase I of the Medicare health support pilot program under traditional fee-for-service Medicare: 18-month interim analysis. Available at: http://www.cms.gov/reports/ downloads/ MHS-Second-Report-to-Congress-October-2008.pdf. Accessed December 19, 2010.
11. King G, Zeng L. The dangers of extreme counterfactuals. Polit Anal. 2006;14:131-159. (Pubitemid 43444515)
12. King G, Honaker J, Joseph A, Scheve K. Analyzing incomplete political science data: An alternative algorithm for multiple imputation. Am Polit Sci Rev. 2001;95:49-69. (Pubitemid 33390251)
13. Iacus SM, King G, Porro G. Causal inference without balance checking: Coarsened exact matching. Available at: http:// gking.harvard.edu/files/abs/cem- plus-abs.shtml.
14. King G, Gakidou E, Imai K, et al. Public policy for the poor? A randomised assessment of the Mexican universal health insurance programme. Lancet. 2009;373:1447-1454.
15. King G. Replication, replication. Polit Sci Pol. 1995;28:443-499.
16. Foote SM. Next steps: How can Medicare accelerate the pace of improving chronic care? Health Affairs. 2009;28:99-102.
17. Thorpe KE, Ogden LL, Galactionova K. Chronic conditions account for rise in Medicare spending from 1987 to 2006. Health Affairs. 2010;29:718-724.