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Andrea Rotnitzky

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. A Rotnitzky & E Smucler & J M Robins, 2021. "Characterization of parameters with a mixed bias property," Biometrika, Biometrika Trust, vol. 108(1), pages 231-238.

    Cited by:

    1. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
    2. Xingyu Chen & Lin Liu & Rajarshi Mukherjee, 2024. "Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics," Papers 2408.06103, arXiv.org.
    3. Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
    4. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    5. Lin Liu & Chang Li, 2023. "New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators," Papers 2302.08097, arXiv.org.
    6. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2022. "Double Robust Bayesian Inference on Average Treatment Effects," Papers 2211.16298, arXiv.org, revised Oct 2024.
    7. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

  2. Andrea Rotnitzky & Quanhong Lei & Mariela Sued & James M. Robins, 2012. "Improved double-robust estimation in missing data and causal inference models," Biometrika, Biometrika Trust, vol. 99(2), pages 439-456.

    Cited by:

    1. Gruber Susan & van der Laan Mark J., 2012. "Targeted Minimum Loss Based Estimator that Outperforms a given Estimator," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-22, May.
    2. Sloczynski, Tymon & Wooldridge, Jeffrey M., 2014. "A General Double Robustness Result for Estimating Average Treatment Effects," IZA Discussion Papers 8084, Institute of Labor Economics (IZA).
    3. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    4. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    5. Weibin Mo & Yufeng Liu, 2022. "Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 440-472, April.
    6. Han, Peisong & Song, Peter X.-K. & Wang, Lu, 2015. "Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 59-70.
    7. José R. Zubizarreta, 2015. "Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 910-922, September.
    8. Chen, Xiaohong & Liu, Ying & Ma, Shujie & Zhang, Zheng, 2024. "Causal inference of general treatment effects using neural networks with a diverging number of confounders," Journal of Econometrics, Elsevier, vol. 238(1).
    9. Vermeulen Karel & Vansteelandt Stijn, 2016. "Data-Adaptive Bias-Reduced Doubly Robust Estimation," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 253-282, May.
    10. Lan Wen & Miguel A. Hernán & James M. Robins, 2022. "Multiply robust estimators of causal effects for survival outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1304-1328, September.
    11. Peisong Han, 2014. "Multiply Robust Estimation in Regression Analysis With Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1159-1173, September.
    12. James M. Robins & Miguel A. Hernán & Larry Wasserman, 2015. "Discussion of “On Bayesian estimation of marginal structural models”," Biometrics, The International Biometric Society, vol. 71(2), pages 296-299, June.
    13. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    14. David Cheng & Ashwin N. Ananthakrishnan & Tianxi Cai, 2021. "Robust and efficient semi‐supervised estimation of average treatment effects with application to electronic health records data," Biometrics, The International Biometric Society, vol. 77(2), pages 413-423, June.
    15. Layla Parast & Tianxi Cai & Lu Tian, 2021. "Evaluating multiple surrogate markers with censored data," Biometrics, The International Biometric Society, vol. 77(4), pages 1315-1327, December.
    16. Nicholas Williams & Michael Rosenblum & Iván Díaz, 2022. "Optimising precision and power by machine learning in randomised trials with ordinal and time‐to‐event outcomes with an application to COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2156-2178, October.
    17. Su, Miaomiao & Wang, Ruoyu & Wang, Qihua, 2022. "A two-stage optimal subsampling estimation for missing data problems with large-scale data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    18. van der Laan Mark J., 2014. "Targeted Estimation of Nuisance Parameters to Obtain Valid Statistical Inference," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 29-57, May.
    19. Lee, Myoung-jae & Lee, Sanghyeok, 2019. "Double robustness without weighting," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 175-180.
    20. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    21. Sun Hao & Ertefaie Ashkan & Lu Xin & Johnson Brent A., 2020. "Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 300-314, January.
    22. Ao Yuan & Anqi Yin & Ming T. Tan, 2021. "Enhanced Doubly Robust Procedure for Causal Inference," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 454-478, December.
    23. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
    24. Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
    25. Han, Peisong, 2012. "A note on improving the efficiency of inverse probability weighted estimator using the augmentation term," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2221-2228.
    26. Peisong Han, 2016. "Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 246-260, March.
    27. M. Hristache & V. Patilea, 2017. "Conditional moment models with data missing at random," Biometrika, Biometrika Trust, vol. 104(3), pages 735-742.
    28. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
    29. Huiming Lin & Bo Fu & Guoyou Qin & Zhongyi Zhu, 2017. "Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts," Biometrics, The International Biometric Society, vol. 73(4), pages 1132-1139, December.
    30. Su, Miaomiao & Wang, Qihua, 2022. "A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    31. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
    32. Ying Jin & Dominik Rothenhäusler, 2024. "Tailored inference for finite populations: conditional validity and transfer across distributions," Biometrika, Biometrika Trust, vol. 111(1), pages 215-233.

  3. Lu Wang & Andrea Rotnitzky & Xihong Lin & Randall E. Millikan & Peter F. Thall, 2012. "Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 493-508, June.

    Cited by:

    1. Yehan Ma & Arthur B. Yeh & John T. Chen, 2023. "Simultaneous Confidence Regions and Weighted Hypotheses on Parameter Arrays," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-18, June.
    2. Early Kirstin & Mankoff Jennifer & Fienberg Stephen E., 2017. "Dynamic Question Ordering in Online Surveys," Journal of Official Statistics, Sciendo, vol. 33(3), pages 625-657, September.
    3. Xinru WANG & Nina DELIU & NARITA Yusuke & Bibhas CHAKRABORTY, 2023. "SMART-EXAM: Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials," Discussion papers 23081, Research Institute of Economy, Trade and Industry (RIETI).
    4. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    5. Lan Wen & Miguel A. Hernán & James M. Robins, 2022. "Multiply robust estimators of causal effects for survival outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1304-1328, September.
    6. Jincheng Shen & Lu Wang & Jeremy M. G. Taylor, 2017. "Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models," Biometrics, The International Biometric Society, vol. 73(2), pages 635-645, June.
    7. Thomas A. Murray & Peter F. Thall & Ying Yuan & Sarah McAvoy & Daniel R. Gomez, 2017. "Robust Treatment Comparison Based on Utilities of Semi-Competing Risks in Non-Small-Cell Lung Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 11-23, January.
    8. Armando Turchetta & Erica E. M. Moodie & David A. Stephens & Sylvie D. Lambert, 2023. "Bayesian sample size calculations for comparing two strategies in SMART studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2489-2502, September.
    9. Chaffee Paul H. & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation for Dynamic Treatment Regimes in Sequentially Randomized Controlled Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-32, June.
    10. Nina Zhou & Lu Wang & Daniel Almirall, 2023. "Estimating tree‐based dynamic treatment regimes using observational data with restricted treatment sequences," Biometrics, The International Biometric Society, vol. 79(3), pages 2260-2271, September.
    11. Michael P. Wallace & Erica E. M. Moodie, 2015. "Doubly‐robust dynamic treatment regimen estimation via weighted least squares," Biometrics, The International Biometric Society, vol. 71(3), pages 636-644, September.
    12. Yebin Tao & Lu Wang, 2017. "Adaptive contrast weighted learning for multi-stage multi-treatment decision-making," Biometrics, The International Biometric Society, vol. 73(1), pages 145-155, March.
    13. Cole Manschot & Eric Laber & Marie Davidian, 2023. "Interim monitoring of sequential multiple assignment randomized trials using partial information," Biometrics, The International Biometric Society, vol. 79(4), pages 2881-2894, December.

  4. E. J. Tchetgen Tchetgen & A. Rotnitzky, 2011. "On protected estimation of an odds ratio model with missing binary exposure and confounders," Biometrika, Biometrika Trust, vol. 98(3), pages 749-754.

    Cited by:

    1. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.

  5. Orellana Liliana & Rotnitzky Andrea & Robins James M., 2010. "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part II: Proofs of Results," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-19, March.

    Cited by:

    1. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    2. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    3. Luedtke Alexander R. & van der Laan Mark J., 2016. "Super-Learning of an Optimal Dynamic Treatment Rule," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 305-332, May.
    4. Ying Huang & Youyi Fong, 2014. "Identifying optimal biomarker combinations for treatment selection via a robust kernel method," Biometrics, The International Biometric Society, vol. 70(4), pages 891-901, December.
    5. Shu Yang & Anastasios A. Tsiatis & Michael Blazing, 2018. "Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach," Biometrics, The International Biometric Society, vol. 74(3), pages 900-909, September.
    6. Eric B. Laber & Daniel J. Lizotte & Bradley Ferguson, 2014. "Set-valued dynamic treatment regimes for competing outcomes," Biometrics, The International Biometric Society, vol. 70(1), pages 53-61, March.
    7. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    8. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    9. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    10. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    11. Emily L. Butler & Eric B. Laber & Sonia M. Davis & Michael R. Kosorok, 2018. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules," Biometrics, The International Biometric Society, vol. 74(1), pages 18-26, March.
    12. Mélanie Prague & Daniel Commenges & Jon Michael Gran & Bruno Ledergerber & Jim Young & Hansjakob Furrer & Rodolphe Thiébaut, 2017. "Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study," Biometrics, The International Biometric Society, vol. 73(1), pages 294-304, March.
    13. Lucia Babino & Andrea Rotnitzky & James Robins, 2019. "Multiple robust estimation of marginal structural mean models for unconstrained outcomes," Biometrics, The International Biometric Society, vol. 75(1), pages 90-99, March.
    14. Eric B. Laber & Anastasios A. Tsiatis & Marie Davidian & Shannon T. Holloway, 2014. "Discussion of “Combining biomarkers to optimize patient treatment recommendation”," Biometrics, The International Biometric Society, vol. 70(3), pages 707-710, September.
    15. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    16. Cain Lauren E. & Robins James M. & Lanoy Emilie & Logan Roger & Costagliola Dominique & Hernán Miguel A., 2010. "When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-26, April.
    17. Yasuhiro Hagiwara & Tomohiro Shinozaki & Hirofumi Mukai & Yutaka Matsuyama, 2021. "Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two‐stage stochastic dynamic treatment regime approach," Biometrics, The International Biometric Society, vol. 77(2), pages 702-714, June.
    18. Yunan Wu & Lan Wang, 2021. "Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes," Biometrics, The International Biometric Society, vol. 77(2), pages 465-476, June.
    19. Yuqian Zhang & Weijie Ji & Jelena Bradic, 2021. "Dynamic treatment effects: high-dimensional inference under model misspecification," Papers 2111.06818, arXiv.org, revised Jun 2023.
    20. Orellana Liliana & Rotnitzky Andrea & Robins James M., 2010. "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-49, March.
    21. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

  6. Andrea Rotnitzky & Lingling Li & Xiaochun Li, 2010. "A note on overadjustment in inverse probability weighted estimation," Biometrika, Biometrika Trust, vol. 97(4), pages 997-1001.

    Cited by:

    1. Schnitzer Mireille E. & Lok Judith J. & Gruber Susan, 2016. "Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 97-115, May.
    2. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    3. Samuel Kwesi Dunyo & Samuel Amponsah Odei, 2023. "Firm-Level Innovations in an Emerging Economy: Do Perceived Policy Instability and Legal Institutional Conditions Matter?," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    4. Porter Kristin E. & Gruber Susan & van der Laan Mark J. & Sekhon Jasjeet S., 2011. "The Relative Performance of Targeted Maximum Likelihood Estimators," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, August.
    5. Zeyu Bian & Erica E. M. Moodie & Susan M. Shortreed & Sahir Bhatnagar, 2023. "Variable selection in regression‐based estimation of dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 79(2), pages 988-999, June.
    6. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    7. Leonard Henckel & Emilija Perković & Marloes H. Maathuis, 2022. "Graphical criteria for efficient total effect estimation via adjustment in causal linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 579-599, April.
    8. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
    9. Bartolucci, Francesco & Grilli, Leonardo & Pieroni, Luca, 2012. "Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator," MPRA Paper 43430, University Library of Munich, Germany.
    10. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).

  7. Wang, Lu & Rotnitzky, Andrea & Lin, Xihong, 2010. "Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1135-1146.

    Cited by:

    1. Takahiro Hoshino & Yuya Shimizu, 2019. "Doubly Robust-type Estimation of Population Moments and Parameters in Biased Sampling," Keio-IES Discussion Paper Series 2019-006, Institute for Economics Studies, Keio University.
    2. Peisong Han, 2014. "Multiply Robust Estimation in Regression Analysis With Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1159-1173, September.
    3. Majid Mojirsheibani & Timothy Reese, 2017. "Kernel regression estimation for incomplete data with applications," Statistical Papers, Springer, vol. 58(1), pages 185-209, March.
    4. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    5. Lei Wang, 2019. "Dimension reduction for kernel-assisted M-estimators with missing response at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 889-910, August.
    6. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
    7. Li, Wei & Luo, Shanshan & Xu, Wangli, 2024. "Calibrated regression estimation using empirical likelihood under data fusion," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    8. Timothy Reese & Majid Mojirsheibani, 2017. "On the $$L_p$$ L p norms of kernel regression estimators for incomplete data with applications to classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 81-112, March.
    9. Han, Peisong, 2012. "A note on improving the efficiency of inverse probability weighted estimator using the augmentation term," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2221-2228.
    10. Takayuki Toda & Ayako Wakano & Takahiro Hoshino, 2019. "Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect Estimation," Papers 1905.04443, arXiv.org.
    11. Edward H. Kennedy & Zongming Ma & Matthew D. McHugh & Dylan S. Small, 2017. "Non-parametric methods for doubly robust estimation of continuous treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1229-1245, September.
    12. Chen, Qixuan & Paik, Myunghee Cho & Kim, Minjin & Wang, Cuiling, 2016. "Using link-preserving imputation for logistic partially linear models with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 174-185.

  8. Orellana Liliana & Rotnitzky Andrea & Robins James M., 2010. "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-49, March.

    Cited by:

    1. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    2. Rich Benjamin & Moodie Erica E. M. & A. Stephens David, 2016. "Influence Re-weighted G-Estimation," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 157-177, May.
    3. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    4. Ying Huang & Youyi Fong, 2014. "Identifying optimal biomarker combinations for treatment selection via a robust kernel method," Biometrics, The International Biometric Society, vol. 70(4), pages 891-901, December.
    5. Shu Yang & Anastasios A. Tsiatis & Michael Blazing, 2018. "Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach," Biometrics, The International Biometric Society, vol. 74(3), pages 900-909, September.
    6. Eric B. Laber & Daniel J. Lizotte & Bradley Ferguson, 2014. "Set-valued dynamic treatment regimes for competing outcomes," Biometrics, The International Biometric Society, vol. 70(1), pages 53-61, March.
    7. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    8. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    9. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    10. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    11. Emily L. Butler & Eric B. Laber & Sonia M. Davis & Michael R. Kosorok, 2018. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules," Biometrics, The International Biometric Society, vol. 74(1), pages 18-26, March.
    12. Mélanie Prague & Daniel Commenges & Jon Michael Gran & Bruno Ledergerber & Jim Young & Hansjakob Furrer & Rodolphe Thiébaut, 2017. "Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study," Biometrics, The International Biometric Society, vol. 73(1), pages 294-304, March.
    13. Lucia Babino & Andrea Rotnitzky & James Robins, 2019. "Multiple robust estimation of marginal structural mean models for unconstrained outcomes," Biometrics, The International Biometric Society, vol. 75(1), pages 90-99, March.
    14. Eric B. Laber & Anastasios A. Tsiatis & Marie Davidian & Shannon T. Holloway, 2014. "Discussion of “Combining biomarkers to optimize patient treatment recommendation”," Biometrics, The International Biometric Society, vol. 70(3), pages 707-710, September.
    15. Chaffee Paul H. & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation for Dynamic Treatment Regimes in Sequentially Randomized Controlled Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-32, June.
    16. Jeffrey A. Boatman & David M. Vock, 2018. "Estimating the causal effect of treatment regimes for organ transplantation," Biometrics, The International Biometric Society, vol. 74(4), pages 1407-1416, December.
    17. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    18. Cain Lauren E. & Robins James M. & Lanoy Emilie & Logan Roger & Costagliola Dominique & Hernán Miguel A., 2010. "When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-26, April.
    19. Yasuhiro Hagiwara & Tomohiro Shinozaki & Hirofumi Mukai & Yutaka Matsuyama, 2021. "Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two‐stage stochastic dynamic treatment regime approach," Biometrics, The International Biometric Society, vol. 77(2), pages 702-714, June.
    20. Yunan Wu & Lan Wang, 2021. "Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes," Biometrics, The International Biometric Society, vol. 77(2), pages 465-476, June.
    21. Yuqian Zhang & Weijie Ji & Jelena Bradic, 2021. "Dynamic treatment effects: high-dimensional inference under model misspecification," Papers 2111.06818, arXiv.org, revised Jun 2023.
    22. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

  9. Eric J. Tchetgen Tchetgen & James M. Robins & Andrea Rotnitzky, 2010. "On doubly robust estimation in a semiparametric odds ratio model," Biometrika, Biometrika Trust, vol. 97(1), pages 171-180.

    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    2. Amanda Coston & Edward H. Kennedy, 2022. "The role of the geometric mean in case-control studies," Papers 2207.09016, arXiv.org.
    3. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
    5. Oliver Dukes & Torben Martinussen & Eric J. Tchetgen Tchetgen & Stijn Vansteelandt, 2019. "On doubly robust estimation of the hazard difference," Biometrics, The International Biometric Society, vol. 75(1), pages 100-109, March.
    6. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.
    7. Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
    8. Tan, Zhiqiang, 2019. "On doubly robust estimation for logistic partially linear models," Statistics & Probability Letters, Elsevier, vol. 155(C), pages 1-1.
    9. Dridi, Ichrak & Boughrara, Adel, 2023. "Flexible inflation targeting and stock market volatility: Evidence from emerging market economies," Economic Modelling, Elsevier, vol. 126(C).
    10. van Amsterdam Wouter A. C. & Ranganath Rajesh, 2023. "Conditional average treatment effect estimation with marginally constrained models," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-26, January.
    11. Nicola Orsini & Rino Bellocco & Arvid Sjolander, 2013. "Doubly robust estimation in generalized linear models," Stata Journal, StataCorp LP, vol. 13(1), pages 185-205, March.

  10. Page, John H. & Rotnitzky, Andrea, 2009. "Estimation of the disease-specific diagnostic marker distribution under verification bias," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 707-717, January.

    Cited by:

    1. Danping Liu & Xiao-Hua Zhou, 2013. "Covariate Adjustment in Estimating the Area Under ROC Curve with Partially Missing Gold Standard," Biometrics, The International Biometric Society, vol. 69(1), pages 91-100, March.
    2. Danping Liu & Xiao-Hua Zhou, 2011. "Semiparametric Estimation of the Covariate-Specific ROC Curve in Presence of Ignorable Verification Bias," Biometrics, The International Biometric Society, vol. 67(3), pages 906-916, September.
    3. Chinyereugo M Umemneku Chikere & Kevin Wilson & Sara Graziadio & Luke Vale & A Joy Allen, 2019. "Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.

  11. Yannis Jemiai & Andrea Rotnitzky & Bryan E. Shepherd & Peter B. Gilbert, 2007. "Semiparametric estimation of treatment effects given base‐line covariates on an outcome measured after a post‐randomization event occurs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 879-901, November.

    Cited by:

    1. Halloran M. Elizabeth & Hudgens Michael G., 2012. "Causal Inference for Vaccine Effects on Infectiousness," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-40, January.
    2. Gilbert Peter B. & Blette Bryan S. & Hudgens Michael G. & Shepherd Bryan E., 2020. "Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 54-69, January.
    3. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    4. Gilbert Peter B. & Hudgens Michael G. & Wolfson Julian, 2011. "Commentary on "Principal Stratification -- a Goal or a Tool?" by Judea Pearl," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-15, September.
    5. Sandra García & Jennifer Hill, 2009. "The Impact of Conditional Cash Transfers on Children´s School Achievement: Evidence from Colombia," Documentos CEDE 5403, Universidad de los Andes, Facultad de Economía, CEDE.
    6. Arvid Sjölander & Keith Humphreys & Stijn Vansteelandt & Rino Bellocco & Juni Palmgren, 2009. "Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease," Biometrics, The International Biometric Society, vol. 65(2), pages 514-520, June.
    7. Sjolander Arvid & Vansteelandt Stijn & Humphreys Keith, 2010. "A Principal Stratification Approach to Assess the Differences in Prognosis between Cancers Caused by Hormone Replacement Therapy and by Other Factors," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-37, June.
    8. Gilbert Peter B. & Blette Bryan S. & Shepherd Bryan E. & Hudgens Michael G., 2020. "Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 54-69, January.

  12. Andrea Rotnitzky & Andres Farall & Andrea Bergesio & Daniel Scharfstein, 2007. "Analysis of failure time data under competing censoring mechanisms," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 307-327, June.

    Cited by:

    1. Shu Yang & Yilong Zhang & Guanghan Frank Liu & Qian Guan, 2023. "SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale," Biometrics, The International Biometric Society, vol. 79(1), pages 230-240, March.
    2. Judith J. Lok & Shu Yang & Brian Sharkey & Michael D. Hughes, 2018. "Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 201-223, April.
    3. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    4. Sujatro Chakladar & Samuel Rosin & Michael G. Hudgens & M. Elizabeth Halloran & John D. Clemens & Mohammad Ali & Michael E. Emch, 2022. "Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring," Biometrics, The International Biometric Society, vol. 78(2), pages 777-788, June.
    5. Shu Yang, 2022. "Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations," Biometrics, The International Biometric Society, vol. 78(3), pages 937-949, September.
    6. Ungolo, Francesco & van den Heuvel, Edwin R., 2024. "A Dirichlet process mixture regression model for the analysis of competing risk events," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 95-113.

  13. James Robins & Andrea Rotnitzky & Stijn Vansteelandt, 2007. "Discussions," Biometrics, The International Biometric Society, vol. 63(3), pages 650-653, September.

    Cited by:

    1. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    2. Pierre-Yves Geoffard & Karine Lamiraud, 2007. "Therapeutic non adherence: A rational behavior revealing patient preferences?," Post-Print halshs-00754193, HAL.
    3. Gustafson Paul, 2012. "Double-Robust Estimators: Slightly More Bayesian than Meets the Eye?," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-15, January.
    4. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    5. Sjolander Arvid, 2011. "Reaction to Pearl's Critique of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-5, April.
    6. Petersen Maya & Schwab Joshua & Gruber Susan & Blaser Nello & Schomaker Michael & van der Laan Mark, 2014. "Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models," Journal of Causal Inference, De Gruyter, vol. 2(2), pages 147-185, September.
    7. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
    8. Porter Kristin E. & Gruber Susan & van der Laan Mark J. & Sekhon Jasjeet S., 2011. "The Relative Performance of Targeted Maximum Likelihood Estimators," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, August.
    9. Samaneh Mahabadi & Mojtaba Ganjali, 2015. "A Bayesian approach for sensitivity analysis of incomplete multivariate longitudinal data with potential nonrandom dropout," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 397-417, December.
    10. Rosenblum Michael & van der Laan Mark J., 2010. "Targeted Maximum Likelihood Estimation of the Parameter of a Marginal Structural Model," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-30, April.
    11. Díaz Iván & van der Laan Mark J., 2013. "Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 149-160, November.
    12. van der Laan Mark J., 2014. "Targeted Estimation of Nuisance Parameters to Obtain Valid Statistical Inference," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 29-57, May.
    13. Frederico Poleto & Geert Molenberghs & Carlos Paulino & Julio Singer, 2011. "Sensitivity analysis for incomplete continuous data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 589-606, November.
    14. van der Laan Mark J., 2014. "Causal Inference for a Population of Causally Connected Units," Journal of Causal Inference, De Gruyter, vol. 2(1), pages 13-74, March.
    15. Gertheiss, Jan & Goldsmith, Jeff & Staicu, Ana-Maria, 2017. "A note on modeling sparse exponential-family functional response curves," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 46-52.
    16. Qihua Wang & Gregg Dinse & Chunling Liu, 2012. "Hazard function estimation with cause-of-death data missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 415-438, April.
    17. Minjeong Jeon & Sophia Rabe-Hesketh, 2016. "An autoregressive growth model for longitudinal item analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 830-850, September.
    18. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "Rejoinder on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 751-758, December.
    19. Anton Flossmann, 2010. "Accounting for missing data in M-estimation: a general matching approach," Empirical Economics, Springer, vol. 38(1), pages 85-117, February.
    20. Ib Thomsen & Li-Chun Zhang & Joseph Sexton, 2000. "Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response," Discussion Papers 274, Statistics Norway, Research Department.
    21. Hua Chen, 2011. "Representations of efficient score for coarse data problems based on Neumann series expansion," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(3), pages 497-509, June.

  14. Stijn Vansteelandt & Andrea Rotnitzky & James Robins, 2007. "Estimation of Regression Models for the Mean of Repeated Outcomes Under Nonignorable Nonmonotone Nonresponse," Biometrika, Biometrika Trust, vol. 94(4), pages 841-860.

    Cited by:

    1. Ojo, Temitope O. & Baiyegunhi, Lloyd J.S & Adetoro, Adetoso A. & Ogundeji, Abiodun A., 2021. "Adoption of Soil and Water Conservation Technology and Its Impact on the Productivity of Smallholder Rice Farmers in Southwest, Nigeria," 2021 Conference, August 17-31, 2021, Virtual 314981, International Association of Agricultural Economists.
    2. Tony Vangeneugden & Geert Molenberghs & Geert Verbeke & Clarice G.B. Dem�trio, 2011. "Marginal correlation from an extended random-effects model for repeated and overdispersed counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 215-232, September.
    3. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    4. Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.
    5. Xie, Hui, 2012. "Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1287-1300.
    6. Lucia Babino & Andrea Rotnitzky & James Robins, 2019. "Multiple robust estimation of marginal structural mean models for unconstrained outcomes," Biometrics, The International Biometric Society, vol. 75(1), pages 90-99, March.
    7. A. R. Linero, 2017. "Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness," Biometrika, Biometrika Trust, vol. 104(2), pages 327-341.
    8. Daniel O. Scharfstein & Jon Steingrimsson & Aidan McDermott & Chenguang Wang & Souvik Ray & Aimee Campbell & Edward Nunes & Abigail Matthews, 2022. "Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders," Biometrics, The International Biometric Society, vol. 78(2), pages 649-659, June.
    9. Andrew Copas & Shaun Seaman, 2010. "Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 911-922.
    10. Yu Cao & Nitai D. Mukhopadhyay, 2021. "Statistical Modeling of Longitudinal Data with Non-Ignorable Non-Monotone Missingness with Semiparametric Bayesian and Machine Learning Components," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 152-169, May.
    11. Weiping Zhang & Feiyue Xie & Jiaxin Tan, 2020. "A robust joint modeling approach for longitudinal data with informative dropouts," Computational Statistics, Springer, vol. 35(4), pages 1759-1783, December.

  15. Bryan E. Shepherd & Peter B. Gilbert & Yannis Jemiai & Andrea Rotnitzky, 2006. "Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(2), pages 332-342, June.

    Cited by:

    1. Halloran M. Elizabeth & Hudgens Michael G., 2012. "Causal Inference for Vaccine Effects on Infectiousness," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-40, January.
    2. Siyu Heng & Dylan S. Small & Paul R. Rosenbaum, 2020. "Finding the strength in a weak instrument in a study of cognitive outcomes produced by Catholic high schools," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 935-958, June.
    3. Shanshan Luo & Wei Li & Yangbo He, 2023. "Causal inference with outcomes truncated by death in multiarm studies," Biometrics, The International Biometric Society, vol. 79(1), pages 502-513, March.
    4. Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty, 2021. "Increasing power for observational studies of aberrant response: An adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 482-504, July.
    5. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    6. VanderWeele, Tyler J., 2008. "Simple relations between principal stratification and direct and indirect effects," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2957-2962, December.
    7. D. Todem & J. Fine & L. Peng, 2010. "A Global Sensitivity Test for Evaluating Statistical Hypotheses with Nonidentifiable Models," Biometrics, The International Biometric Society, vol. 66(2), pages 558-566, June.
    8. Arvid Sjölander & Keith Humphreys & Stijn Vansteelandt & Rino Bellocco & Juni Palmgren, 2009. "Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease," Biometrics, The International Biometric Society, vol. 65(2), pages 514-520, June.
    9. Sjolander Arvid & Vansteelandt Stijn & Humphreys Keith, 2010. "A Principal Stratification Approach to Assess the Differences in Prognosis between Cancers Caused by Hormone Replacement Therapy and by Other Factors," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-37, June.
    10. Chiba Yasutaka, 2012. "The Large Sample Bounds on the Principal Strata Effect with Application to a Prostate Cancer Prevention Trial," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, May.
    11. Linbo Wang & Thomas S. Richardson & Xiao-Hua Zhou, 2017. "Causal analysis of ordinal treatments and binary outcomes under truncation by death," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 719-735, June.
    12. James Robins & Andrea Rotnitzky & Stijn Vansteelandt, 2007. "Discussions," Biometrics, The International Biometric Society, vol. 63(3), pages 650-653, September.
    13. Dean Follmann & Michael P. Fay & Michael Proschan, 2009. "Chop-Lump Tests for Vaccine Trials," Biometrics, The International Biometric Society, vol. 65(3), pages 885-893, September.
    14. Brian L. Egleston & Robert G. Uzzo & Yu-Ning Wong, 2017. "Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 534-546, April.
    15. Yanqing Sun & Qiong Shou & Peter B. Gilbert & Fei Heng & Xiyuan Qian, 2023. "Semiparametric additive time‐varying coefficients model for longitudinal data with censored time origin," Biometrics, The International Biometric Society, vol. 79(2), pages 695-710, June.
    16. Paul R. Rosenbaum & Dylan S. Small, 2017. "An adaptive Mantel–Haenszel test for sensitivity analysis in observational studies," Biometrics, The International Biometric Society, vol. 73(2), pages 422-430, June.

  16. Rotnitzky, Andrea & Faraggi, David & Schisterman, Enrique, 2006. "Doubly Robust Estimation of the Area Under the Receiver-Operating Characteristic Curve in the Presence of Verification Bias," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1276-1288, September.

    Cited by:

    1. Page, John H. & Rotnitzky, Andrea, 2009. "Estimation of the disease-specific diagnostic marker distribution under verification bias," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 707-717, January.
    2. Danping Liu & Xiao-Hua Zhou, 2013. "Covariate Adjustment in Estimating the Area Under ROC Curve with Partially Missing Gold Standard," Biometrics, The International Biometric Society, vol. 69(1), pages 91-100, March.
    3. Danping Liu & Xiao-Hua Zhou, 2011. "Semiparametric Estimation of the Covariate-Specific ROC Curve in Presence of Ignorable Verification Bias," Biometrics, The International Biometric Society, vol. 67(3), pages 906-916, September.
    4. Khanh To Duc & Monica Chiogna & Gianfranco Adimari, 2019. "Estimation of the volume under the ROC surface in presence of nonignorable verification bias," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 695-722, December.
    5. Chinyereugo M Umemneku Chikere & Kevin Wilson & Sara Graziadio & Luke Vale & A Joy Allen, 2019. "Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    6. Qi Long & Xiaoxi Zhang & Brent A. Johnson, 2011. "Robust Estimation of Area Under ROC Curve Using Auxiliary Variables in the Presence of Missing Biomarker Values," Biometrics, The International Biometric Society, vol. 67(2), pages 559-567, June.
    7. Danping Liu & Xiao-Hua Zhou, 2010. "A Model for Adjusting for Nonignorable Verification Bias in Estimation of the ROC Curve and Its Area with Likelihood-Based Approach," Biometrics, The International Biometric Society, vol. 66(4), pages 1119-1128, December.

  17. James Robins & Andrea Rotnitzky, 2004. "Estimation of treatment effects in randomised trials with non-compliance and a dichotomous outcome using structural mean models," Biometrika, Biometrika Trust, vol. 91(4), pages 763-783, December.

    Cited by:

    1. He Jiwei & Stephens-Shields Alisa & Joffe Marshall, 2015. "Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 203-222, November.
    2. Paul Clarke & Frank Windmeijer, 2010. "Instrumental Variable Estimators for Binary Outcomes," The Centre for Market and Public Organisation 10/239, The Centre for Market and Public Organisation, University of Bristol, UK.
    3. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.
    4. Faerber Jennifer A. & Joffe Marshall M. & Small Dylan S. & Zhang Rongmei & Brown Gregory K. & Ten Have Thomas R., 2017. "A Simple Model Allowing Modification of the Effect of a Randomized Intervention by Post-Randomization Variables," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-16, September.
    5. Ali Reza Soltanian & Soghrat Faghihzadeh, 2012. "A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1037-1048, October.
    6. Mark van der Laan & Alan Hubbard & Nicholas Jewell, 2004. "Estimation of Treatment Effects in Randomized Trials with Noncompliance and a Dichotomous Outcome," U.C. Berkeley Division of Biostatistics Working Paper Series 1157, Berkeley Electronic Press.
    7. Guido Imbens, 2014. "Instrumental Variables: An Econometrician's Perspective," NBER Working Papers 19983, National Bureau of Economic Research, Inc.
    8. Kern, Holger & Hainmueller, Jens, 2007. "Opium for the Masses: How Foreign Free Media Can Stabilize Authoritarian Regimes," MPRA Paper 2702, University Library of Munich, Germany.
    9. Paul S. Clarke & Tom M. Palmer & Frank Windmeijer, 2011. "Estimating Structural Mean Models with Multiple Instrumental Variables using the Generalised Method of Moments," The Centre for Market and Public Organisation 11/266, The Centre for Market and Public Organisation, University of Bristol, UK.
    10. Paul S. Clarke & Frank Windmeijer, 2010. "Identification of causal effects on binary outcomes using structural mean models," CeMMAP working papers CWP02/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Matthew Blackwell & Anton Strezhnev, 2022. "Telescope matching for reducing model dependence in the estimation of the effects of time‐varying treatments: An application to negative advertising," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 377-399, January.
    12. Joffe Marshall M & Small Dylan & Ten Have Thomas & Brunelli Steve & Feldman Harold I, 2008. "Extended Instrumental Variables Estimation for Overall Effects," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-22, April.
    13. Hua Chen & Zhi Geng & Xiao-Hua Zhou, 2009. "Identifiability and Estimation of Causal Effects in Randomized Trials with Noncompliance and Completely Nonignorable Missing Data," Biometrics, The International Biometric Society, vol. 65(3), pages 675-682, September.
    14. Murielle Bochud & Valentin Rousson, 2010. "Usefulness of Mendelian Randomization in Observational Epidemiology," IJERPH, MDPI, vol. 7(3), pages 1-18, February.
    15. Linbo Wang & Xiang Meng & Thomas S. Richardson & James M. Robins, 2023. "Coherent modeling of longitudinal causal effects on binary outcomes," Biometrics, The International Biometric Society, vol. 79(2), pages 775-787, June.
    16. Ditte Nørbo Sørensen & Torben Martinussen & Eric Tchetgen Tchetgen, 2019. "A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 639-659, October.

  18. Jolene Birmingham & Andrea Rotnitzky & Garrett M. Fitzmaurice, 2003. "Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 275-297, February.

    Cited by:

    1. Anastasios A. Tsiatis & Marie Davidian & Weihua Cao, 2011. "Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout," Biometrics, The International Biometric Society, vol. 67(2), pages 536-545, June.
    2. Antonio R. Linero & Michael J. Daniels, 2015. "A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 45-55, March.
    3. Xuerong Chen & Guoqing Diao & Jing Qin, 2020. "Pseudo likelihood‐based estimation and testing of missingness mechanism function in nonignorable missing data problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1377-1400, December.
    4. A. R. Linero, 2017. "Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness," Biometrika, Biometrika Trust, vol. 104(2), pages 327-341.
    5. Daniel Scharfstein & Aidan McDermott & Iván Díaz & Marco Carone & Nicola Lunardon & Ibrahim Turkoz, 2018. "Global sensitivity analysis for repeated measures studies with informative drop†out: A semi†parametric approach," Biometrics, The International Biometric Society, vol. 74(1), pages 207-219, March.
    6. Daniel O. Scharfstein & Jon Steingrimsson & Aidan McDermott & Chenguang Wang & Souvik Ray & Aimee Campbell & Edward Nunes & Abigail Matthews, 2022. "Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders," Biometrics, The International Biometric Society, vol. 78(2), pages 649-659, June.
    7. Jackie, Yenerall & Wen, You & George, Davis & Paul, Estabrooks, 2015. "Examining Ways to Handle Non-Random Missingness in CEA through Econometric and Statistics Lenses," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205690, Agricultural and Applied Economics Association.
    8. Miran A. Jaffa & Ayad A. Jaffa, 2019. "A Likelihood-Based Approach with Shared Latent Random Parameters for the Longitudinal Binary and Informative Censoring Processes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 597-613, December.
    9. Morikawa, Kosuke & Kano, Yutaka, 2018. "Identification problem of transition models for repeated measurement data with nonignorable missing values," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 216-230.

  19. James Robins & Andrea Rotnitzky & Marco Bonetti, 2001. "Discussion of the Frangakis and Rubin Article," Biometrics, The International Biometric Society, vol. 57(2), pages 343-347, June.

    Cited by:

    1. Tianchen Qian & Constantine Frangakis & Constantin Yiannoutsos, 2020. "Deductive Semiparametric Estimation in Double-Sampling Designs with Application to PEPFAR," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 417-445, December.

  20. Daniel Scharfstein & James M. Robins & Wesley Eddings & Andrea Rotnitzky, 2001. "Inference in Randomized Studies with Informative Censoring and Discrete Time-to-Event Endpoints," Biometrics, The International Biometric Society, vol. 57(2), pages 404-413, June.

    Cited by:

    1. Miguel A. Hernán & James M. Robins & Luis A. García Rodríguez, 2005. "Discussion on "Statistical Issues Arising in the Women's Health Initiative"," Biometrics, The International Biometric Society, vol. 61(4), pages 922-930, December.
    2. Marzieh K Golmakani & Rebecca A Hubbard & Diana L Miglioretti, 2022. "Nonhomogeneous Markov chain for estimating the cumulative risk of multiple false positive screening tests," Biometrics, The International Biometric Society, vol. 78(3), pages 1244-1256, September.
    3. Xuelin Huang & Nan Zhang, 2008. "Regression Survival Analysis with an Assumed Copula for Dependent Censoring: A Sensitivity Analysis Approach," Biometrics, The International Biometric Society, vol. 64(4), pages 1090-1099, December.
    4. Ming-Wen An & Constantine E. Frangakis & Beverly S. Musick & Constantin T. Yiannoutsos, 2009. "The Need for Double-Sampling Designs in Survival Studies: An Application to Monitor PEPFAR," Biometrics, The International Biometric Society, vol. 65(1), pages 301-306, March.
    5. Rebecca A. Hubbard & Diana L. Miglioretti, 2013. "A Semiparametric Censoring Bias Model for Estimating the Cumulative Risk of a False-Positive Screening Test Under Dependent Censoring," Biometrics, The International Biometric Society, vol. 69(1), pages 245-253, March.
    6. Sedigheh Mirzaei Salehabadi & Debasis Sengupta & Rituparna Das, 2015. "Parametric Estimation of Menarcheal Age Distribution Based on Recall Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 290-305, March.
    7. Miran A. Jaffa & Ayad A. Jaffa, 2019. "A Likelihood-Based Approach with Shared Latent Random Parameters for the Longitudinal Binary and Informative Censoring Processes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 597-613, December.
    8. Jie Zhu & Blanca Gallego, 2021. "Continuous Treatment Recommendation with Deep Survival Dose Response Function," Papers 2108.10453, arXiv.org, revised Sep 2023.

  21. Korrick, S.A. & Hunter, D.J. & Rotnitzky, A. & Hu, H. & Speizer, F.E., 1999. "Lead and hypertension in a sample of middle-aged women," American Journal of Public Health, American Public Health Association, vol. 89(3), pages 330-335.

    Cited by:

    1. Vladislav Kondrashov & Joseph L. McQuirter & Melba Miller & Stephen J. Rothenberg, 2005. "Assessment of Lead Exposure Risk in Locksmiths," IJERPH, MDPI, vol. 2(1), pages 1-6, April.

  22. Rotnitzky, Andrea & Holcroft, Christina A. & Robins, James M., 1997. "Efficiency Comparisons in Multivariate Multiple Regression with Missing Outcomes," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 102-128, April.

    Cited by:

    1. Lu Wang & Zhongzhe Ouyang & Xihong Lin, 2024. "Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes," Stats, MDPI, vol. 7(3), pages 1-20, August.

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