![[PDF]](/pb-assets/Icons/adobe-pdf-icon-1498649896243.png){kind=icon}
Abstract
Time-dependent receiver operating characteristic curves allow to evaluate the capacity of a marker to discriminate between subjects who experience the event up to a given prognostic time from those who are free of this event. In this article, we propose an inverse probability weighting estimator of a standardized and weighted time-dependent receiver operating characteristic curve. This estimator provides a measure of the prognostic capacities by taking into account potential confounding factors. We illustrate the robustness of the estimator by a simulation-based study and its usefulness by two applications in kidney transplantation.
References
| 1. | Drucker, E, Krapfenbauer, K. Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine. EPMA J 2013; 4: 7–7. Google Scholar | Medline |
| 2. | Joyner, MJ, Paneth, N. Seven questions for personalized medicine. JAMA 2015; 314: 999–1000. Google Scholar | Medline | ISI |
| 3. | Janes, H, Pepe, MS. Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol 2008; 168: 89–97. Google Scholar | Medline | ISI |
| 4. | Gerds, TA, Cai, T, Schumacher, M. The performance of risk prediction models. Biom J 2008; 50: 457–479. Google Scholar | Medline |
| 5. | Gerds, TA, Schumacher, M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biom J 2006; 48: 1029–1040. Google Scholar | Medline | ISI |
| 6. | Graf, E, Schmoor, C, Sauerbrei, W Assessment and comparison of prognostic classification schemes for survival data. Stat Med 1999; 18: 2529–2545. Google Scholar | Medline | ISI |
| 7. | Austin, PC, Steyerberg, EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med 2014; 33: 517–535. Google Scholar | Medline | ISI |
| 8. | Harrell, FE, Lee, KL, Mark, DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361–387. Google Scholar | Medline | ISI |
| 9. | Steyerberg, EW, Vickers, AJ, Cook, NR Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21: 128–138. Google Scholar | Medline | ISI |
| 10. | Hosmer, DW, Lemesbow, S. Goodness of fit tests for the multiple logistic regression model. Comm Stat Theor Meth 1980; 9: 1043–1069. Google Scholar | ISI |
| 11. | Pencina, MJ, D’Agostino, RB, D’Agostino, RB Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008; 27: 157–172; discussion 207–212. Google Scholar | Medline | ISI |
| 12. | Xanthakis, V, Sullivan, LM, Vasan, RS Assessing the incremental predictive performance of novel biomarkers over standard predictors. Stat Med 2014; 33: 2577–2584. Google Scholar | Medline | ISI |
| 13. | Janes, H, Pepe, MS. Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve. Biometrika 2009; 96: 371–382. Google Scholar | Medline | ISI |
| 14. | Pepe, MS, Cai, T. The analysis of placement values for evaluating discriminatory measures. Biometrics 2004; 60: 528–535. Google Scholar | Medline | ISI |
| 15. | Pepe, MS, Longton, G. Standardizing diagnostic markers to evaluate and compare their performance. Epidemiology 2005; 16: 598–603. Google Scholar | Medline |
| 16. | Heagerty, PJ, Lumley, T, Pepe, MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56: 337–344. Google Scholar | Medline | ISI |
| 17. | Heagerty, PJ, Zheng, Y. Survival model predictive accuracy and ROC curves. Biometrics 2005; 61: 92–105. Google Scholar | Medline | ISI |
| 18. | Moskowitz, CS, Pepe, MS. Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome. Stat Med 2004; 23: 1555–1570. Google Scholar | Medline | ISI |
| 19. | Song, X, Ma, S, Huang, J A semiparametric approach for the nonparametric transformation survival model with multiple covariates. Biostatistics 2007; 8: 197–211. Google Scholar | Medline |
| 20. | Rodríguez-Álvarez, MX, Meira-Machado, L, Abu-Assi, E Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate. Stat Med 2015; 35: 1090–1102. Google Scholar | Medline |
| 21. | Zheng, Y, Cai, T, Stanford, JL Semiparametric models of time-dependent predictive values of prognostic biomarkers. Biometrics 2010; 66: 50–60. Google Scholar | Medline |
| 22. | Blanche, P, Dartigues, J-F, Jacqmin-Gadda, H. Review and comparison of ROC curve estimators for a time-dependent outcome with marker-dependent censoring. Biom J 2013; 55: 687–704. Google Scholar | Medline | ISI |
| 23. | Uno, H, Cai, T, Tian, L Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc 2007; 102: 527–537. Google Scholar | ISI |
| 24. | Hung, H, Chiang, C-T. Estimation methods for time-dependent AUC models with survival data. Can J Stat 2010; 38: 8–26. Google Scholar | ISI |
| 25. | Rosenbaum, PR . Model-based direct adjustment. J Am Stat Assoc 1987; 82: 387–387. Google Scholar | ISI |
| 26. | Rosenbaum, PR, Rubin, DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41–55. Google Scholar | ISI |
| 27. | Cox, DR . Regression models and life-tables (with discussion). J Roy Stat Soc B 1972; 34: 187–220. Google Scholar |
| 28. | Breslow, NE . Discussion of the paper by D. R. Cox. J Roy Stat Soc B 1972; 34: 216–217. Google Scholar |
| 29. | Yarlagadda, SG, Coca, SG, Garg, AX Marked variation in the definition and diagnosis of delayed graft function: a systematic review. Nephrol Dial Transplant 2008; 23: 2995–3003. Google Scholar | Medline | ISI |
| 30. | Tapiawala, SN, Tinckam, KJ, Cardella, CJ Delayed graft function and the risk for death with a functioning graft. J Am Soc Nephrol 2010; 21: 153–161. Google Scholar | Medline |
| 31. | Yarlagadda, SG, Coca, SG, Formica, RN Association between delayed graft function and allograft and patient survival: a systematic review and meta-analysis. Nephrol Dial Transplant 2009; 24: 1039–1047. Google Scholar | Medline | ISI |
| 32. | Irish, WD, Ilsley, JN, Schnitzler, MA A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation. Am J Transplant 2010; 10: 2279–2286. Google Scholar | Medline | ISI |
| 33. | Perico, N, Cattaneo, D, Sayegh, MH Delayed graft function in kidney transplantation. Lancet 2004; 364: 1814–1827. Google Scholar | Medline | ISI |
| 34. | Chapal, M, Le Borgne, F, Legendre, C A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors. Kidney Int 2014; 86: 1130–1139. Google Scholar | Medline | ISI |
| 35. | Opelz, G, Döhler, B. Multicenter analysis of kidney preservation. Transplantation 2007; 83: 247–253. Google Scholar | Medline | ISI |
| 36. | Foucher, Y, Daguin, P, Akl, A A clinical scoring system highly predictive of long-term kidney graft survival. Kidney Int 2010; 78: 1288–1294. Google Scholar | Medline | ISI |
| 37. | Hariharan, S, McBride, MA, Cherikh, WS Post-transplant renal function in the first year predicts long-term kidney transplant survival. Kidney Int 2002; 62: 311–318. Google Scholar | Medline | ISI |
| 38. | Nicol, D, MacDonald, AS, Lawen, J Early prediction of renal allograft loss beyond one year. Transpl Int 1993; 6: 153–157. Google Scholar | Medline |
| 39. | Janes, H, Longton, G, Pepe, M. Accommodating covariates in ROC analysis. Stata J 2009; 9: 17–39. Google Scholar | SAGE Journals | ISI |
| 40. | Combescure, C, Perneger, TV, Weber, DC Prognostic ROC curves: a method for representing the overall discriminative capacity of binary markers with right-censored time-to-event endpoints. Epidemiology 2014; 25: 103–109. Google Scholar | Medline |
