Cited by 413Statistical Methods and InferenceStatistical Methods in EpidemiologyStatistical Distribution Estimation and Applications

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Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models

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Uno, Hajime, Cai, Tianxi, Tian, Lu, Wei, L. J·2007·Journal of the American Statistical Association

DOI 10.1198/016214507000000149

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Abstract

Suppose that we are interested in establishing simple but reliable rules for predicting future t-year survivors through censored regression models. In this article we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models, we derive consistent estimators for the above measures through substitution and cross-validation estimation procedures. Furthermore, we provide large-sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All of the proposals are illustrated with real examples, and their finite-sample properties are evaluated through a simulation study.

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Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
The Statistical Analysis of Failure Time Data.
Aitkin, M. A., Kalbfleisch, J. D., & Prentice, R. L. (1981) · Biometrics · doi:10.2307/2530581 · Cited by 9,875
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,500
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,474
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478
Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
Heagerty, P. J., Lumley, T., & PEPE, M. S. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00337.x · Cited by 2,870
The Jackknife: The Bootstrap and Other Resampling Plans.
Low, L. Y., & Efron, B. (1983) · Journal of the American Statistical Association · doi:10.2307/2288218 · Cited by 2,752

Show all 48 references

Research works printed but not linked: 4

Data, 1973, Medicine, 9, 487
Regression, Medicine, 15, 1987
Trials Clinical, Medicine, 5, 412
Survival Analysis Cox Regression, 1998, Medicine, 11, 1871

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Self-citation
A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
Nadiminti, K. V. G., Langer, K. J., Shabbir, E., Hefazi, M., Dozeman, L., Jethava, Y. S., Loeffler, B. T., Alkhateeb, H. B., Litzow, M. R., Patnaik, M. M., et al. (2021) · Blood Cancer Journal · doi:10.1038/s41408-021-00573-6 · Cited by 2

Authors: Uno, H., Cai, T., Tian, L., & Wei, L. J.
Affiliations: Northwestern University (US)
ORCID: Uno, Hajime
Cai, Tianxi
Wei, L. J
Venue: Journal of the American Statistical Association
Publisher: Informa UK Limited
Published: 2007-06
Volume: 102
Issue: 478
Pages: 527-537
ISSN: 0162-1459 (ISSN-L)
1537-274X
DOI: 10.1198/016214507000000149
OpenAlex: W2082174016
Cited by: 413
References: 48
Citations by year: 2026: 32 · 2025: 37 · 2024: 33 · 2023: 35 · 2022: 46 · 2021: 43 · 2020: 50 · 2019: 20 · 2018: 14 · 2017: 20 · 2016: 18 · 2015: 10 · 2014: 10 · 2013: 15 · 2012: 7
FWCI: 4.74
Type: journal article
Language: en
Open access: green
biostats.bepress.com
Locations: Journal of the American Statistical Association (publishedVersion)
Collection of Biostatistics Research Archive (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Keywords: prediction rules, binary classification, misclassification rate, sensitivity and specificity, predictive value, consistent estimators, cross-validation, large sample approximation, confidence intervals, finite sample properties

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Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models

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