Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial
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DOI: 10.1080/01621459.2013.842488
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References listed on IDEAS
- Uno, Hajime & Cai, Tianxi & Tian, Lu & Wei, L.J., 2007. "Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 527-537, June.
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- Layla Parast & Su-Chun Cheng & Tianxi Cai, 2012. "Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1492-1501, December.
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- Qi Gong & Douglas E. Schaubel, 2017. "Estimating the average treatment effect on survival based on observational data and using partly conditional modeling," Biometrics, The International Biometric Society, vol. 73(1), pages 134-144, March.
- Layla Parast & Beth Ann Griffin, 2017. "Landmark estimation of survival and treatment effects in observational studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 161-182, April.
- Yu Zheng & Tianxi Cai, 2017. "Augmented estimation for t‐year survival with censored regression models," Biometrics, The International Biometric Society, vol. 73(4), pages 1169-1178, December.
- David Benkeser & Iván Díaz & Alex Luedtke & Jodi Segal & Daniel Scharfstein & Michael Rosenblum, 2021. "Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1467-1481, December.
- 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.
- Yen‐Tsung Huang, 2021. "Rejoinder to “Causal mediation of semicompeting risks”," Biometrics, The International Biometric Society, vol. 77(4), pages 1170-1174, December.
- 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.
- 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.
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