Improved double-robust estimation in missing data and causal inference models
Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-ro...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
2012
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 07112caa a22006977a 4500 | ||
|---|---|---|---|
| 001 | PAPER-9598 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203932.0 | ||
| 008 | 190411s2012 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-84861641356 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 030 | |a BIOKA | ||
| 100 | 1 | |a Rotnitzky, A. | |
| 245 | 1 | 0 | |a Improved double-robust estimation in missing data and causal inference models |
| 260 | |c 2012 | ||
| 270 | 1 | 0 | |m Rotnitzky, A.; Di Tella University, Saenz Valiente 1010, Buenos Aires 14281, Argentina; email: arotnitzky@utdt.edu |
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Bang, H., Robins, J.M., Doubly robust estimation in missing data and causal inference models (2005) Biometrics, 61, pp. 692-972 | ||
| 504 | |a Cao, W., Tsiatis, A., Davidian, M., Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data (2009) Biometrika, 96, pp. 723-734 | ||
| 504 | |a Gill, R.D., Non- and semi-parametric maximum likelihood estimators and the von mises method (1989) Scand. J. Statist., 16, pp. 97-128 | ||
| 504 | |a Kang, D.Y.L., Schafer, J.L., Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data (with discussion and rejoinder (2007) Statist. Sci., 22, pp. 523-580 | ||
| 504 | |a Robins, J.M., Marginal structural models versus structural nested models as tools for causal inference (1999) Statistical Models in Epidemiology: The Environment and Clinical Trials, pp. 95-134. , Ed. M. E. Halloran and D. Berry, Institute for Mathematics and its Applications 116. New York: Springer | ||
| 504 | |a Robins, J.M., Robust estimation in sequentially ignorable missing data and causal inference models (2000) Proc. Am. Statist. Assoc. Sect. Bayesian Statist. Sci., pp. 6-10 | ||
| 504 | |a Robins, J.M., Rotnitzky, A., Semiparametric efficiency in multivariate regression models with missing data (1995) J. Am. Statist. Assoc., 90, pp. 122-129 | ||
| 504 | |a Robins, J.M., Wang, N., Inference for imputation estimators (2000) Biometrika, 87, pp. 113-124 | ||
| 504 | |a Robins, J.M., Rotnitzky, A., Zhao, L.P., Estimation of regression-coefficients when some regressors are not always observed (1994) J. Am. Statist. Assoc., 89, pp. 846-866 | ||
| 504 | |a Robins, J.M., Gomez, Q., Sued, M., Rotnitzky, A., Performance of double-robust estimators when inverse probability weights are highly variable (2007) Statist. Sci., 22, pp. 544-559 | ||
| 504 | |a Rubin, D.B., Inference and missing data (1976) Biometrika, 63, pp. 581-592 | ||
| 504 | |a Rubin, D., Van Der Laan, M.J., Empirical efficiency maximization: Improved locally efficient covariate adjustment in randomized experiments and survival analysis (2008) Int. J. Biostatist., 4. , article 5 | ||
| 504 | |a Scharfstein, D.O., Rotnitzky, A., Robins, J.M., Adjusting for non-ignorable drop-out using semiparametric non-response models (1999) J. Am. Statist. Assoc., 94, pp. 1096-1020 | ||
| 504 | |a Tan, Z., A distributional approach for causal inference using propensity scores (2006) J. Am. Statist. Assoc., 101, pp. 1619-1637 | ||
| 504 | |a Tan, Z., Understanding or, ps and dr (2007) Statist. Sci., 22, pp. 560-568 | ||
| 504 | |a Tan, Z., Comment: Improved local efficiency and double robustness (2008) Int. J. Biostatist., 4. , article 10 | ||
| 504 | |a Tan, Z., Bounded, efficient and doubly robust estimation with inverse weighting (2010) Biometrika, 97, pp. 661-682 | ||
| 504 | |a Tan, Z., Nonparametric likelihood and doubly robust estimating equations for marginal and nested structural models (2010) Can. J. Statist., 38, pp. 609-632 | ||
| 504 | |a Van Der Laan, M.J., Targetedmaximum likelihood based causal inference: Part i (2010) Int. J. Biostatist., 6. , article 2 | ||
| 504 | |a Van Der Laan, M.J., Robins, J., (2003) Unified Methods for Censored Longitudinal Data and Causality, , New York: Springer | ||
| 504 | |a Van Der Laan, M.J., Rubin, D., Targeted maximum likelihood learning (2006) Int. J. Biostatist., 2. , article 11 | ||
| 504 | |a Van Der Vaart, A.W., (2000) Asymptotic Statistics, , Cambridge: Cambridge University Press | ||
| 520 | 3 | |a Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory. © 2012 Biometrika Trust. |l eng | |
| 536 | |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas | ||
| 536 | |a Detalles de la financiación: National Institutes of Health | ||
| 536 | |a Detalles de la financiación: Harvard School of Public Health | ||
| 536 | |a Detalles de la financiación: Andrea Rotnitzky, Lei Gomez and James Robins were funded by grants from the National Institutes of Health, U.S.A. Andrea Rotnitzky is also affiliated with the Harvard School of Public Health. Mariela Sued was funded by grants from the Agencia de Promocion Cientifica y Tecnica de Argentina and the Consejo Nacional de Investigaciones Cientificas y Tecnicas de Argentina. The authors wish to thank the reviewers for helpful comments. | ||
| 593 | |a Di Tella University, Saenz Valiente 1010, Buenos Aires 14281, Argentina | ||
| 593 | |a Adheris, Inc., One Van de Graaff Drive, Burlington, MA 01803, United States | ||
| 593 | |a Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Guiraldes 2160, Buenos Aires 1428, Argentina | ||
| 593 | |a Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, United States | ||
| 690 | 1 | 0 | |a DROP-OUT |
| 690 | 1 | 0 | |a MARGINAL STRUCTURAL MODEL |
| 690 | 1 | 0 | |a MISSING AT RANDOM |
| 700 | 1 | |a Lei, Q. | |
| 700 | 1 | |a Sued, M. | |
| 700 | 1 | |a Robins, J.M. | |
| 773 | 0 | |d 2012 |g v. 99 |h pp. 439-456 |k n. 2 |p Biometrika |x 00063444 |w (AR-BaUEN)CENRE-139 |t Biometrika | |
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| 856 | 4 | 0 | |u https://doi.org/10.1093/biomet/ass013 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_00063444_v99_n2_p439_Rotnitzky |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00063444_v99_n2_p439_Rotnitzky |y Registro en la Biblioteca Digital |
| 961 | |a paper_00063444_v99_n2_p439_Rotnitzky |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/article |a info:ar-repo/semantics/artículo |b info:eu-repo/semantics/publishedVersion | ||