Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data

Journal of the American Statistical Association

Volumn 110, Issue 511, 2015, Pages 910-922

  Zubizarreta, José R ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Causal inference; Observational study; Propensity score; Quadratic programming; Unit nonresponse; Weight adjustment

Indexed keywords

EID: 84946945195     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2015.1023805     Document Type: Article

References (46)

1. M.Baiocchi,, D.S.Small,, S.Lorch,, P.R.Rosenbaum, (2010), Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants, Journal of the American Statistical Association, 105, 1285–1296.
2. S.Boyd,, and L.Vandenberghe, (2004), Convex Optimization, New York: Cambridge University Press.
3. S.R.Cole,, M.A.Hernán, (2008), Constructing Inverse Probability Weights for Marginal Structural Models, American Journal of Epidemiology, 168, 656–664.
4. R.K.Crump,, V.J.Hotz,, G.W.Imbens,, O.A.Mitnik, (2009), Dealing with Limited Overlap in Estimation of Average Treatment Effects, Biometrika, 96, 187–199.
5. A.P.Dawid, (1979), Conditional Independence in Statistical Theory, Journal of the Royal Statistical Society, Series B, 41, 1–31.
6. B.Efron, (1971), Forcing a Sequential Experiment to be Balanced, Biometrika, 58, 403–407.
7. M.R.Elliott, (2008), Model Averaging Methods for Weight Trimming, Journal of Official Statistics, 24, 517–540.
8. W.A.Fuller, (2009), Sampling Statistics (Vol. 560), New York: Wiley.
9. A.Gelman,, and X.L.(.Meng, X.-L. (eds.) (2004), Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, New York: Wiley.
10. T.Hastie,, R.Tibshirani,, and J.Friedman, (2009), The Elements of Statistical Learning, New York: Springer.
11. K.Hirano,, G.W.Imbens, (2001), Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization, Health Services and Outcomes Research Methodology, 2, 259–278.
12. J.W.Hogan,, T.Lancaster, (2004), Instrumental Variables and Inverse Probability Weighting for Causal Inference from Longitudinal Observational Studies, Statistical Methods in Medical Research, 13, 17–48.
13. D.G.Horvitz,, D.J.Thompson, (1952), A Generalization of Sampling Without Replacement From a Finite Universe, Journal of the American Statistical Association, 47, 663–685.
14. K.Imai,, M.Ratkovic, (2014), Covariate Balancing Propensity Score, Journal of the Royal Statistical Society, Series B, 76, 243–263.
15. M.M.Joffe,, T.R.T.Have,, H.I.Feldman,, S.E.Kimmel, (2004), Model Selection, Confounder Control, and Marginal Structural Models: Review and New Applications, The American Statistician, 58, 272–279.
16. J.D.Y.Kang,, J.L.Schafer, (2007), Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data” (with discussion), Statistical Science, 22, 523–539.
17. B.K.Lee,, J.Lessler,, E.A.Stuart, (2010), Improving Propensity Score Weighting Using Machine Learning, Statistics in Medicine, 29, 337–346.
18. R.J.A.Little,, and D.B.Rubin, (2002), Statistical Analysis with Missing Data, (2nd ed.), New York: Wiley.
19. MIDEPLAN (2011), Ficha Técnica Encuesta Post Terromoto. Available at http://www.ministeriodesarrollosocial.gob.cl/encuesta-post-terremoto/index.html.
20. L.W.Miratrix,, J.S.Sekhon,, B.Yu, (2013), Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments, Journal of the Royal Statistical Society, Series B, 75, 369–396.
21. S.L.T.Normand,, M.B.Landrum,, E.Guadagnoli,, J.Z.Ayanian,, T.J.Ryan,, P.D.Cleary,, B.J.McNeil, (2001), Validating Recommendations for Coronary Angiography Following Acute Myocardial Infarction in the Elderly: A Matched Analysis Using Propensity Scores, Journal of Clinical Epidemiology, 54, 387–398.
22. C.Papadimitriou, (1994), Computational Complexity, Reading, MA: Addison-Wesley.
23. J.N.K.Rao,, A.C.Singh, (2009), Range Restricted Weight Calibration for Survey Data Using Ridge Regression, Pakistan Journal of Statistics, 25, 371–384.
24. J.Robins,, N.Wang, (2000), Inference for Imputation Estimators, Biometrika, 87, 113–124.
25. J.M.Robins,, A.Rotnitzky,, L.P.Zhao, (1994), Estimation of Regression Coefficients When Some Regressors Are Not Always Observed, Journal of the American Statistical Association, 89, 846–866.
26. J.M.Robins,, M.Sued,, L.G.Quanhong,, A.Rotnitzky, (2007), Comment: Performance of Double-Robust Estimators When ‘Inverse Probability’ Weights Are Highly Variable, Statistical Science, 22, 544–559.
27. P.R.Rosenbaum, (1987), Model-Based Direct Adjustment, Journal of the American Statistical Association, 82, 387–394.
28. P.R.Rosenbaum, (2010), Design of Observational Studies, New York: Springer.
29. P.R.Rosenbaum,, D.B.Rubin, (1983), The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika, 70, 41–55.
30. A.Rotnitzky,, Q.Lei,, M.Sued,, J.M.Robins, (2012), Improved Double-robust Estimation in Missing Data and Causal Inference Models, Biometrika, 99, 439–456.
31. D.B.Rubin, (1978), Bayesian Inference for Causal Effects: The Role of Randomization, The Annals of Statistics, 6, 34–58.
32. D.B.Rubin, (2005), Causal Inference Using Potential Outcomes: Design, Modeling, Decisions, Journal of the American Statistical Association, 100, 322–331.
33. D.B.Rubin, (2008), For Objective Causal Inference, Design Trumps Analysis, The Annals of Applied Statistics, 2, 808–840.
34. D.O.Scharfstein,, A.Rotnitzky,, J.M.Robins, (1999), Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models: Rejoinder, Journal of the American Statistical Association, 94, 1135–1146.
35. W.R.Shadish,, T.D.Cook,, and D.T.Campbell, (2002), Experimental and Quasi-Experimental Designs for Generalized Causal Inference, Boston, MA: Houghton Mifflin.
36. E.A.Stuart, (2010), Matching Methods for Causal Inference: A Review and a Look Forward, Statistical Science, 25, 1–21.
37. E.A.Stuart,, S.R.Cole,, C.P.Bradshaw,, P.J.Leaf, (2011), The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials, Journal of the Royal Statistical Society, Series A, 174, 369–386.
38. Z.Tan, (2010), Bounded, Efficient and Doubly Robust Estimation with Inverse Weighting, Biometrika, 97, 661–682.
39. A.Tsybakov, (2008), Introduction to Nonparametric Estimation, New York: Springer.
40. USGS (2014), Largest Earthquakes in the World Since 1900. Available at http://earthquake.usgs.gov/earthquakes/world/10_largest_world.php.
41. S.Wright, (1997), Primal-Dual Interior-Point Methods, Philadelphia, PA: Society for Industrial and Applied Mathematics.
42. D.Yang,, D.Small,, J.H.Silber,, P.R.Rosenbaum, (2012), Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes, Biometrics, 68, 628–636.
43. J.R.Zubizarreta, (2012), Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery, Journal of the American Statistical Association, 107, 1360–1371.
44. J.R.Zubizarreta,, M.Cerdá,, P.R.Rosenbaum, (2013a), Effect of the 2010 Chilean Earthquake on Posttraumatic Stress: Reducing Sensitivity to Unmeasured Bias Through Study Design, Epidemiology, 24, 79–87.
45. J.R.Zubizarreta,, C.E.Reinke,, R.R.Kelz,, J.H.Silber,, P.R.Rosenbaum, (2011), Matching for Several Sparse Nominal Variables in a Case-Control Study of Readmission Following Surgery, The American Statistician, 65, 229–238.
46. J.R.Zubizarreta,, D.S.Small,, N.K.Goyal,, S.A.Lorch,, P.R.Rosenbaum, (2013b), Stronger Instruments Via Integer Programming in an Observational Study of Late Preterm Birth Outcomes, Annals of Applied Statistics, 7, 25–50.