Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models
Open accessAbstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Uno and colleagues' inverse probability of censoring weights method can account for censoring.
“Uno and colleagues' inverse probability of censoring weights method can account for censoring.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [82]
Cites
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 · see citing works
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 23,834 · see citing works
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 21,028 · see citing works
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 · see citing works
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 · see citing works
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works
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 6,411 · see citing works
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 · see citing works
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 · see citing works
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 2,728 · see citing works
Discriminant Analysis and Statistical Pattern Recognition.
Hawkins, D. M., & McLachlan, G. J. (1993) · Journal of the American Statistical Association · doi:10.2307/2290355 · Cited by 2,662 · see citing works
Estimating the error rate of a prediction rule: improvement on cross-validation
Efron, B. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477973 · Cited by 2,300 · see citing works
Counting Processes and Survival Analysis.
Dabrowska, D. M., Fleming, T. R., & Harrington, D. P. (1992) · Journal of the American Statistical Association · doi:10.2307/2290673 · Cited by 2,300 · see citing works
The Statistical Analysis of Failure Time Data
Klein, J. P., Kalbfleisch, J. D., & Prentice, R. L. (1982) · Technometrics · doi:10.1080/00401706.1982.10487769 · Cited by 2,248 · see citing works
Large sample estimation and hypothesis testingPreprint
Newey, W. K., & McFadden, D. L. (1986) · RePEc: Research Papers in Economics · Cited by 2,192 · see citing works
Mallows, C. L. (1995) · Technometrics · doi:10.1080/00401706.1995.10484370 · Cited by 2,083 · see citing works
The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2005) · Technometrics · doi:10.1198/tech.2005.s278 · Cited by 1,917 · see citing works
Statistical Methods in Diagnostic Medicine
Zhou, X., Sun, J., Pennello, G. A., Obuchowski, N. A., & McClish, D. K. (2011) · Wiley series in probability and statistics · doi:10.1002/9780470906514 · Cited by 1,785 · see citing works
Improvements on Cross-Validation: The .632+ Bootstrap Method
Efron, B., & Tibshirani, R. J. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474007 · Cited by 1,638 · see citing works
Survival Model Predictive Accuracy and ROC Curves
Heagerty, P. J., & Zheng, Y. (2005) · Biometrics · doi:10.1111/j.0006-341x.2005.030814.x · Cited by 1,471 · see citing works
Pepe, M. S. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh101 · Cited by 1,276 · see citing works
Mallows, C. L. (1995) · Technometrics · doi:10.2307/1269729 · Cited by 1,089 · see citing works
Bishop, C. (2004) · Institute of Physics eBooks · doi:10.1887/0750303123/b365c43 · Cited by 846 · see citing works
Wei, L. J. (1992) · Statistics in Medicine · doi:10.1002/sim.4780111409 · Cited by 827 · see citing works
Self-citationPrognosis in primary biliary cirrhosis: Model for decision making
Dickson, R. E., Grambsch, P. M., Fleming, T. R., Fisher, L. D., & Langworthy, A. (1989) · Hepatology · doi:10.1002/hep.1840100102 · Cited by 816 · see citing works
Empirical Processes: Theory and Applications
Pollard, D. (1990) · Project Euclid (Cornell University) · Cited by 710 · see citing works
How Biased is the Apparent Error Rate of a Prediction Rule?
Efron, B. (1986) · Journal of the American Statistical Association · doi:10.2307/2289236 · Cited by 587 · see citing works
On Measuring and Correcting the Effects of Data Mining and Model Selection
Ye, J. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10474094 · Cited by 519 · see citing works
The Estimation of Prediction Error
Efron, B. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000692 · Cited by 477 · see citing works
Analysis of Transformation Models with Censored Data
CHENG, S. C., Wei, L. J., & Ying, Z. (1995) · Biometrika · doi:10.1093/biomet/82.4.835 · Cited by 456 · see citing works
Self-citationProbabilistic prediction in patient management and clinical trials
Spiegelhalter, D. J. (1986) · Statistics in Medicine · doi:10.1002/sim.4780050506 · Cited by 362 · see citing works
On Measuring and Correcting the Effects of Data Mining and Model Selection
Ye, J. (1998) · Journal of the American Statistical Association · doi:10.2307/2669609 · Cited by 358 · see citing works
EXPLAINED VARIATION FOR LOGISTIC REGRESSION
Mittlböck, M., & SCHEMPER, M. (1996) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19961015)15:19 · Cited by 334 · see citing works
Kwek, L. C., & Oh, C. H. (1998) · The European Physical Journal C · doi:10.1007/s100520050261 · Cited by 327 · see citing works
Combining Several Screening Tests: Optimality of the Risk Score
McIntosh, M. W., & PEPE, M. S. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00657.x · Cited by 254 · see citing works
Measures of explained variation for survival data
Korn, E. L., & Simon, R. M. (1990) · Statistics in Medicine · doi:10.1002/sim.4780090503 · Cited by 194 · see citing works
Zoubir, A. M., & Iskander, D. R. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511536717.006 · Cited by 161 · see citing works
Generalized bootstrap for estimating equations
Chatterjee, S., & Bose, A. (2005) · The Annals of Statistics · doi:10.1214/009053604000000904 · Cited by 138 · see citing works
Towards a general asymptotic theory for Cox model with staggered entry
Bilias, Y., Gu, M., & Ying, Z. (1997) · The Annals of Statistics · doi:10.1214/aos/1031833668 · Cited by 137 · see citing works
Model evaluation based on the sampling distribution of estimated absolute prediction error
Tian, L., Cai, T., Goetghebeur, E. J. T., & Wei, L. J. (2007) · Biometrika · doi:10.1093/biomet/asm036 · Cited by 87 · see citing works
Self-citationApplication of the Time‐Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers
Zheng, Y., Cai, T., & Feng, Z. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00441.x · Cited by 82 · see citing works
Self-citationSemiparametric Box–Cox power transformation models for censored survival observations
Cai, T., Tian, L., & Wei, L. J. (2005) · Biometrika · doi:10.1093/biomet/92.3.619 · Cited by 59 · see citing works
Self-citationModel Search by Bootstrap “Bumping”
Tibshirani, R. J., & Knight, K. (1999) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1999.10474842 · Cited by 53 · see citing works
Estimating subject-specific survival functions under the accelerated failure time model
Park, Y., & Wei, L.-J. (2003) · Biometrika · doi:10.1093/biomet/90.3.717 · Cited by 52 · see citing works
Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome
Moskowitz, C. S., & PEPE, M. S. (2004) · Statistics in Medicine · doi:10.1002/sim.1747 · Cited by 34 · see citing works
Explained Variation in Proportional Hazards Regression
Xu, J. O. A. R. (2005) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420027761-27 · Cited by 22 · see citing works
Cross-Validated Bagged Prediction of Survival
Sinisi, S. E., Neugebauer, R. S., & van der Laan, M. J. (2006) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1180 · Cited by 7 · see citing works
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2
Flagged first, then by citations
Cited by
Sorted by citations
Blanche, P., Dartigues, J., & Jacqmin‐Gadda, H. (2013) · Statistics in Medicine · doi:10.1002/sim.5958 · Cited by 1,877 · see citing works
Loi, S., Drubay, D., Adams, S., Pruneri, G., Francis, P. A., Lacroix-Triki, M., Joensuu, H., Dieci, M. V., Badve, S., Demaria, S., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.01010 · Cited by 893 · see citing works
Time-dependent ROC curve analysis in medical research: current methods and applications
Kamarudin, A. N., Cox, T. F., & Kolamunnage‐Dona, R. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0332-6 · Cited by 839 · see citing works
MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era
Kim, W. R., Mannalithara, A., Heimbach, J. K., Kamath, P. S., Asrani, S. K., Biggins, S. W., Wood, N. L., Gentry, S. E., & Kwong, A. J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.08.050 · Cited by 654 · see citing works
Developing clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Halabi, S., Lin, C., Kelly, W. K., Fizazi, K. S., Moul, J. W., Kaplan, E. B., Morris, M. J., & Small, E. J. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.52.3696 · Cited by 479 · see citing works
Sanyal, A. J., Harrison, S. A., Ratziu, V., Abdelmalek, M. F., Diehl, A. M., Caldwell, S., Shiffman, M. L., Aguilar Schall, R., Jia, C., McColgan, B., et al. (2019) · Hepatology · doi:10.1002/hep.30664 · Cited by 390 · see citing works
A Hepatocellular Carcinoma 5-Gene Score Associated With Survival of Patients After Liver Resection
Nault, J., de Reyniès, A., Villanueva, A., Caldéraro, J., Rebouissou, S., Couchy, G., Decaens, T., Franco, D., Imbeaud, S., Rousseau, F., et al. (2013) · Gastroenterology · doi:10.1053/j.gastro.2013.03.051 · Cited by 336 · see citing works
Maximum Likelihood Estimation in Semiparametric Regression Models with Censored Data
Zeng, D., & Lin, D. Y. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1369-7412.2007.00606.x · Cited by 315 · see citing works
Hung, C.-L., Verma, A., Uno, H., Shin, S.-H., Bourgoun, M., Hassanein, A. H., McMurray, J. J., Velazquez, E. J., Kober, L., Pfeffer, M. A., et al. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.06.044 · Cited by 305 · see citing works
Self-citationThe Performance of Risk Prediction Models
Gerds, T. A., Cai, T., & Schumacher, M. (2008) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.200810443 · Cited by 316 · see citing works
Self-citationKickingereder, P., GOETZ, M. A., Muschelli, J., Wick, A., Neuberger, U., Shinohara, R. T., Sill, M., Nowosielski, M., Schlemmer, H.-P., Radbruch, A., et al. (2016) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-0702 · Cited by 268 · see citing works
Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer
Leon-Ferre, R. A., Jonas, S. F., Salgado, R., Loi, S., de Jong, V., Carter, J. M., Nielsen, T. O., Leung, S. C. Y., Riaz, N., Chia, S., et al. (2024) · JAMA · doi:10.1001/jama.2024.3056 · Cited by 247 · see citing works
Analysis of randomized comparative clinical trial data for personalized treatment selections
Cai, T., Tian, L., Wong, P. H., & Wei, L. J. (2010) · Biostatistics · doi:10.1093/biostatistics/kxq060 · Cited by 239 · see citing works
Self-citationSimon, R. M., Subramanian, J., Li, M., & Menezes, S. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr001 · Cited by 228 · see citing works
Senev, A., Coemans, M., Lerut, E., Sandt, V. S. T. V., Kerkhofs, J., Daniëls, L., den Driessche, M. V., Compernolle, V., Sprangers, B., Loon, E. V., et al. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020010019 · Cited by 199 · see citing works
Younes, R., Caviglia, G. P., Govaere, O., Rosso, C., Armandi, A., Sanavia, T., Pennisi, G., Liguori, A., Francione, P., Gallego-Durán, R., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.05.008 · Cited by 196 · see citing works
Deep learning for survival analysis: a review
Wiegrebe, S., Kopper, P., Sonabend, R., Bischl, B., & Bender, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10681-3 · Cited by 194 · see citing works
Rizopoulos, D., Molenberghs, G., & Lesaffre, E. M. E. H. (2017) · Biometrical Journal · doi:10.1002/bimj.201600238 · Cited by 168 · see citing works
McLernon, D. J., Giardiello, D., CALSTER, B. V., Wynants, L., van Geloven, N., van Smeden, M., Therneau, T. M., Steyerberg, E. W., Initiative, T. G. 6. A. 8. O. T. S., McLernon, D. J., et al. (2022) · Annals of Internal Medicine · doi:10.7326/m22-0844 · Cited by 167 · see citing works
Halabi, S., Lin, C., Small, E. J., Armstrong, A. J., Kaplan, E. B., Petrylak, D. P., Sternberg, C. N., Shen, L., Oudard, S. M., Bono, J. S. D., et al. (2013) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djt280 · Cited by 166 · see citing works
The c-index is not proper for the evaluation of $t$-year predicted risks
Blanche, P., Kattan, M. W., & Gerds, T. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy006 · Cited by 158 · see citing works
Cheng, R. K.-H., Levy, W. C., Vasbinder, A. L., Teruya, S. L., Santos, J. D. L., Leedy, D. J., & Maurer, M. S. (2020) · JACC CardioOncology · doi:10.1016/j.jaccao.2020.06.007 · Cited by 150 · see citing works
Direct Oral Anticoagulants vs Low-Molecular-Weight Heparin and Recurrent VTE in Patients With Cancer
Schrag, D., Uno, H., Rosovsky, R. P. G., Rutherford, C., Sanfilippo, K. M., Villano, J. L., Drescher, M. R., Jayaram, N. H., Holmes, C. E., Feldman, L. E., et al. (2023) · JAMA · doi:10.1001/jama.2023.7843 · Cited by 152 · see citing works
Self-citationGood, Z., Sarno, J., Jager, A., Samusik, N., Aghaeepour, N., Simonds, E. F., White, L., Lacayo, N. J., Fantl, W. J., Fazio, G., et al. (2018) · Nature Medicine · doi:10.1038/nm.4505 · Cited by 133 · see citing works
Chen, S.-Y., Yang, Y., Qi, S., Wang, Y., Hu, C., He, X., Zhang, L., Wu, G., Qu, B., Qian, L.-T., et al. (2020) · Leukemia · doi:10.1038/s41375-020-0791-3 · Cited by 127 · see citing works
Admission D-dimer levels, D-dimer trends, and outcomes in COVID-19
Naymagon, L., Zubizarreta, N. J., Feld, J., van Gerwen, M. A. G., Alsen, M., Thibaud, S., Kessler, A. J., Venugopal, S., Makki, I., Qin, Q., et al. (2020) · Thrombosis Research · doi:10.1016/j.thromres.2020.08.032 · Cited by 127 · see citing works
Wahl, S., Boulesteix, A., Zierer, A., Thorand, B., & van de Wiel, M. A. (2016) · BMC Medical Research Methodology · doi:10.1186/s12874-016-0239-7 · Cited by 90 · see citing works
Lambert, J., & Chevret, S. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280213515571 · Cited by 103 · see citing works
Sia, C., Chan, M. H.-H., Zheng, H., Ko, J., Ho, A. F. W., Chong, J. H., Foo, D. C. G., Foo, L.-L., Lim, P. Z., Liew, B. W., et al. (2021) · Cardiovascular Diabetology · doi:10.1186/s12933-021-01395-3 · Cited by 103 · see citing works
Kazmirczak, F., Chen, K.-H. A., Adabağ, S., von Wald, L. N., Roukoz, H., Benditt, D. G., Okasha, O., Farzaneh‐Far, A., Markowitz, J., Nijjar, P. S., et al. (2019) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007488 · Cited by 99 · see citing works
Blanche, P., Dartigues, J., & Jacqmin‐Gadda, H. (2013) · Biometrical Journal · doi:10.1002/bimj.201200045 · Cited by 94 · see citing works
de Vries, E. M. G., Wang, J., Leeflang, M., Boonstra, K., Weersma, R. K., Beuers, U. H., Geskus, R. B., & Ponsioen, C. Y. (2016) · Liver International · doi:10.1111/liv.13110 · Cited by 93 · see citing works
Gedebjerg, A., Szépligeti, S. K., Wackerhausen, L.-M. H., Horváth–Puhó, E., Dahl, R., Hansen, J. G., Sørensen, H. T., Nørgaard, M., Lange, P., & Thomsen, R. W. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30002-x · Cited by 84 · see citing works
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
Cao, S., Wang, J. R., Ji, S., Peng, Y., Dai, Y., Guo, S., Montierth, M. D., Shen, J. P., Zhao, X. H., Chen, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01342-x · Cited by 80 · see citing works
MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma
Spraker, M., Wootton, L., Hippe, D., Ball, K. C., Peeken, J., Macomber, M., Chapman, T., Hoff, M., Kim, E. Y., Pollack, S., et al. (2019) · Advances in Radiation Oncology · doi:10.1016/j.adro.2019.02.003 · Cited by 69 · see citing works
Single and multiple time-point prediction models in kidney transplant outcomes
Lin, R., Horn, S. D., Hurdle, J. F., & Goldfarb‐Rumyantzev, A. S. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.03.005 · Cited by 71 · see citing works
Li, L., Greene, T., & Hu, B. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216680239 · Cited by 70 · see citing works
Sudden Cardiac Death in Hypertensive Patients
Verdecchia, P., Angeli, F., Cavallini, C., Aita, A., Turturiello, D., Fano, M. D., & Reboldi, G. (2019) · Hypertension · doi:10.1161/hypertensionaha.119.12684 · Cited by 69 · see citing works
Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals
Khurshid, S. S., Kartoun, U., Ashburner, J. M., Trinquart, L., Philippakis, A., Khera, A. V., Ellinor, P. T., Ng, K., & Lubitz, S. A. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.008997 · Cited by 65 · see citing works
Zhou, Q. M., Zhe, L., Brooke, R. J., Hudson, M. M., & Yuan, Y. (2021) · Diagnostic and Prognostic Research · doi:10.1186/s41512-021-00102-w · Cited by 64 · see citing works
Haider, S. P., Zeevi, T., Baumeister, P., Reichel, C. A., Sharaf, K., Forghani, R., Kann, B. H., Judson, B. L., Prasad, M. L., Burtness, B. A., et al. (2020) · Cancers · doi:10.3390/cancers12071778 · Cited by 63 · see citing works
Stockmann, J., Verberk, I. M. W., Timmesfeld, N., Denz, R., Budde, B., Lange-Leifhelm, J., Scheltens, P., van der Flier, W. M., Nabers, A., Teunissen, C. E., et al. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00738-8 · Cited by 62 · see citing works
Heilbroner, S. P., Few, R., Neilan, T. G., Mueller, J., Chalwa, J., Charest, F., Suryadevara, S., Kratt, C., Gomez‐Caminero, A., & Dreyfus, B. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002545 · Cited by 61 · see citing works
On Longitudinal Prediction with Time-to-Event Outcome: Comparison of Modeling Options
Maziarz, M., Heagerty, P. J., Cai, T., & Zheng, Y. (2016) · Biometrics · doi:10.1111/biom.12562 · Cited by 61 · see citing works
Self-citationDuMontier, C., Uno, H., Hshieh, T. T., Zhou, G., Chen, R., Magnavita, E. S., Mozessohn, L., Javedan, H., Stone, R. M., Soiffer, R. J., et al. (2021) · Haematologica · doi:10.3324/haematol.2021.278802 · Cited by 57 · see citing works
Self-citationBrunet‐Ratnasingham, E., Anand, S. P., Gantner, P., Dyachenko, A., Moquin‐Beaudry, G., Brassard, N., Beaudoin-Bussières, G., Pagliuzza, A., Gasser, R., Benlarbi, M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abj5629 · Cited by 53 · see citing works
Orcutt, X., Chen, K., Mamtani, R., Long, Q., & Parikh, R. B. (2025) · Nature Medicine · doi:10.1038/s41591-024-03352-5 · Cited by 58 · see citing works
Yap, W., Shih, M., Kuo, C., Pai, P., Chou, W., Chang, K., Tsai, M., & Tsang, N. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.3242 · Cited by 57 · see citing works
Dudek, A. Z., Wang, X., Gu, L., Duong, S. Q., Stinchcombe, T. E., Kratzke, R. A., Borghaei, H., Vokes, E. E., & Kindler, H. L. (2020) · Clinical Lung Cancer · doi:10.1016/j.cllc.2020.06.025 · Cited by 51 · see citing works
Wu, V., Moshier, E. L., Leng, S., Barlogie, B., Cho, H. J., Jagannath, S., Madduri, D., Mazumdar, M., Parekh, S. S., & Chari, A. (2018) · Blood Advances · doi:10.1182/bloodadvances.2018016998 · Cited by 50 · see citing works
Credit risk modelling under recessionary and financially distressed conditions
Dendramis, Y., Tzavalis, E., & Adraktas, G. (2017) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2017.03.020 · Cited by 49 · see citing works
Makhani, N., Lebrun‐Frénay, C., Sıva, A., Narula, S., Wassmer, E., Brassat, D., Brenton, J. N., Cabre, P., Dalliere, C. C., Seze, J. D., et al. (2019) · Multiple Sclerosis Journal - Experimental Translational and Clinical · doi:10.1177/2055217319836664 · Cited by 48 · see citing works
Li, K., O'Brien, R., Lutz, M., Luo, S., & Initiative, T. A. D. N. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.11.004 · Cited by 44 · see citing works
A comparison of estimators to evaluate the discriminatory power of time‐to‐event models
Schmid, M. C., & Potapov, S. (2012) · Statistics in Medicine · doi:10.1002/sim.5464 · Cited by 46 · see citing works
Ramspek, C. L., Teece, L., Snell, K. I. E., Evans, M., Riley, R. D., van Smeden, M., van Geloven, N., & van Diepen, M. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab256 · Cited by 45 · see citing works
Biccler, J. L., Eloranta, S., Brown, P. D. N., Frederiksen, H., Jerkeman, M., Smedby, K. E. E., Bøgsted, M., & El‐Galaly, T. C. (2017) · Cancer Medicine · doi:10.1002/cam4.1271 · Cited by 44 · see citing works
Kahles, F., Rückbeil, M. V., Mertens, R. W., Foldenauer, A. C., Arrivas, M. C., Moellmann, J., Lebherz, C., Biener, M., Giannitsis, E., Katus, H. A., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz728 · Cited by 44 · see citing works
Robust estimation of the proportion of treatment effect explained by surrogate marker information
Parast, L. M., McDermott, M. M., & Tian, L. (2015) · Statistics in Medicine · doi:10.1002/sim.6820 · Cited by 42 · see citing works
Self-citationDeveloping and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology
Halabi, S., Li, C., & Luo, S. (2019) · JCO Precision Oncology · doi:10.1200/po.19.00068 · Cited by 42 · see citing works
Estimation of prediction error for survival models
Lawless, J. F., & Yuan, Y. (2009) · Statistics in Medicine · doi:10.1002/sim.3758 · Cited by 42 · see citing works
Calibrating parametric subject-specific risk estimation
Cai, T., Tian, L., Uno, H., Solomon, S. D., & Wei, L. J. (2010) · Biometrika · doi:10.1093/biomet/asq012 · Cited by 41 · see citing works
Self-citationClaggett, B. L., Tian, L., Castagno, D., & Wei, L. J. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu037 · Cited by 41 · see citing works
Self-citationLandmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial
Parast, L. M., Tian, L., & Cai, T. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.842488 · Cited by 39 · see citing works
Self-citationDysglycemia and Index60 as Prediagnostic End Points for Type 1 Diabetes Prevention Trials
Nathan, B. M., Boulware, D. C., Geyer, S. M., Atkinson, M. A., Colman, P. G., Goland, R., Russell, W. E., Wentworth, J. M., Wilson, D. M., Evans‐Molina, C., et al. (2017) · Diabetes Care · doi:10.2337/dc17-0916 · Cited by 39 · see citing works
Deep Cox Mixtures for Survival RegressionPreprint
Nagpal, C., Yadlowsky, S., Rostamzadeh, N., & Heller, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06536 · Cited by 38 · see citing works
Haider, S. P., Sharaf, K., Zeevi, T., Baumeister, P., Reichel, C. A., Forghani, R., Kann, B. H., Petukhova, A., Judson, B. L., Prasad, M. L., et al. (2020) · Translational Oncology · doi:10.1016/j.tranon.2020.100906 · Cited by 37 · see citing works
Rival, M., Thouvenot, É., de Terdonck, L. D. T., Laurent‐Chabalier, S., Dematteï, C., Uygunoğlu, U., Castelnovo, G., Cohen, M., Okuda, D. T., Kantarci, O. H., et al. (2022) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000200044 · Cited by 36 · see citing works
Wong, C. W., & Chaudhry, A. A. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd-2019-pitd-10 · Cited by 35 · see citing works
Cardiac risk stratification in cancer patients: A longitudinal patient–patient network analysis
Hou, Y., Zhou, Y., Hussain, M., Budd, G. T., Tang, W. H. W., Abraham, J., Xu, B., Shah, C., Moudgil, R., Popović, Z., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003736 · Cited by 36 · see citing works
Song, X., Huang, Y., Neuhouser, M. L., Tinker, L. F., Vitolins, M. Z., Prentice, R. L., & Lampe, J. W. (2017) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.117.153072 · Cited by 34 · see citing works
A Data Analytics Suite for Exploratory Predictive, and Visual Analysis of Type 2 Diabetes
Philip, N. Y., Razaak, M., Chang, J., Suchetha, M., O’Kane, M., & Pierscionek, B. K. (2022) · IEEE Access · doi:10.1109/access.2022.3146884 · Cited by 33 · see citing works
A C-index for Recurrent Event Data: Application to Hospitalizations among Dialysis Patients
Kim, S., Schaubel, D. E., & McCullough, K. P. (2017) · Biometrics · doi:10.1111/biom.12761 · Cited by 32 · see citing works
Nishimura, S., Kumamaru, H., Shoji, S., Nakatani, E., Yamamoto, H., Ichihara, N., Miyachi, Y., Sandhu, A. T., Heidenreich, P. A., Yamauchi, K., et al. (2022) · Age and Ageing · doi:10.1093/ageing/afac009 · Cited by 33 · see citing works
Time-Dependent AUC with Right-Censored Data: A Survey
Blanche, P., Latouche, A., & Viallon, V. (2013) · Lecture notes in statistics · doi:10.1007/978-1-4614-8981-8_11 · Cited by 33 · see citing works
Pfohl, S., Xu, Y., Foryciarz, A., Ignatiadis, N., Genkins, J., & Shah, N. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533166 · Cited by 30 · see citing works
Halabi, S., Yang, Q., Roy, A., Luo, B., Araujo, J. C., Logothetis, C., Sternberg, C. N., Armstrong, A. J., Carducci, M. A., Chi, K. N., et al. (2023) · Journal of Clinical Oncology · doi:10.1200/jco.22.02661 · Cited by 30 · see citing works
Sloan, R. A., Haaland, B. A., Sawada, S. S., Lee, I.-M. M., Sui, X., Lee, D.-C., Ridouane, Y., Müller‐Riemenschneider, F., & Blair, S. N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157703 · Cited by 30 · see citing works
Terrematte, P., Andrade, D. S., Justino, J. R., Stransky, B., de Araújo, D. S. A., & Neto, A. D. D. (2022) · Cancers · doi:10.3390/cancers14092111 · Cited by 30 · see citing works
Li, Z., Shen, Y., & Ning, J. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2210336 · Cited by 29 · see citing works
Smooth time-dependent receiver operating characteristic curve estimators
Martínez‐Camblor, P., & Pardo–Fernández, J. C. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217740786 · Cited by 29 · see citing works
Chu, H. H., Kim, J. H., Shim, J. H., Gwon, D. I., Ko, H.-K., Shin, J. H., Ko, G., Yoon, H., & Kim, N. (2021) · Cancers · doi:10.3390/cancers13112830 · Cited by 29 · see citing works
Keogh, R. H., & van Geloven, N. (2024) · Epidemiology · doi:10.1097/ede.0000000000001713 · Cited by 29 · see citing works
Faradmal, J., Soltanian, A. R., Roshanaei, G., Khodabakhshi, R., & Kasaeian, A. (2014) · Asian Pacific Journal of Cancer Prevention · doi:10.7314/apjcp.2014.15.14.5883 · Cited by 29 · see citing works
Yan, F., Lin, X., & Huang, X. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1050 · Cited by 29 · see citing works
Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction
Kolossváry, M., Lin, A., Kwieciński, J., Cadet, S. J., Slomka, P. J., Newby, D. E., Dweck, M. R., Williams, M. C., & Dey, D. (2024) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2024.08.012 · Cited by 30 · see citing works
Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information
Parast, L. M., Cheng, S., & Cai, T. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.721281 · Cited by 28 · see citing works
Self-citationBlanche, P., Holt, A., & Scheike, T. (2022) · Lifetime Data Analysis · doi:10.1007/s10985-022-09564-6 · Cited by 28 · see citing works
Bayesian variable selection for survival data using inverse moment priors
Nikooie, A., Wang, W., & Johnson, V. E. (2020) · The Annals of Applied Statistics · doi:10.1214/20-aoas1325 · Cited by 26 · see citing works
Han, Y., Yan, L., Zhang, L., Ouyang, P.-H., Li, P., Goyal, H., & Hu, Z.-D. (2019) · Clinica Chimica Acta · doi:10.1016/j.cca.2019.08.008 · Cited by 27 · see citing works
Jiang, S., Cao, J., ROSNER, B. A., & Colditz, G. A. (2021) · Biometrics · doi:10.1111/biom.13611 · Cited by 27 · see citing works
GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
Chapman, E. A., Baker, J., Aggarwal, P., Hughes, D. M., Nwosu, A. C., Boyd, M. T., Mayland, C. R., Mason, S. R., Ellershaw, J., Probert, C. S., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021591 · Cited by 27 · see citing works
Crombé, A., Fadli, D., Buy, X., Italiano, A., Saut, O., & Kind, M. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27040 · Cited by 27 · see citing works
Belhechmi, S., Bin, R. D., Rotolo, F., & Michiels, S. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03618-y · Cited by 27 · see citing works
Li, G., & Zhou, K. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000364 · Cited by 26 · see citing works
Gissi, D. B., Fabbri, V. P., Gabusi, A., Lenzi, J., Morandi, L., Melotti, S., Asioli, S., Tarsitano, A., Balbi, T., Marchetti, C., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186691 · Cited by 25 · see citing works
Predicting the onset of breast cancer using mammogram imaging data with irregular boundary
Jiang, S., Cao, J., Colditz, G. A., & ROSNER, B. A. (2021) · Biostatistics · doi:10.1093/biostatistics/kxab032 · Cited by 25 · see citing works
Machine learning-based prediction of 1-year mortality for acute coronary syndrome✰
Hadanny, A., Shouval, R., Wu, J., Gale, C. P., Unger, R., Zahger, D., Gottlieb, S., Matetzky, S., Goldenberg, I., Beigel, R., et al. (2021) · Journal of Cardiology · doi:10.1016/j.jjcc.2021.11.006 · Cited by 25 · see citing works
Voordes, G. H. D., Davison, B. A., Biegus, J., Edwards, C., Damman, K., ter Maaten, J. M., Mebazaa, A., Takagi, K., Adamo, M., Ambrosy, A. P., et al. (2024) · European Journal of Heart Failure · doi:10.1002/ejhf.3336 · Cited by 25 · see citing works
Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides
Garberis, I., Gaury, V., Saillard, C., Drubay, D., Elgui, K., Schmauch, B., Jaeger, A., Herpin, L., Linhart, J., Sapateiro, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60824-z · Cited by 24 · see citing works
- Authors
- Uno, H., Cai, T., Tian, L., & Wei, L. J.
- Affiliations
- Northwestern University (US)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review