Landmark risk prediction of residual life for breast cancer survival

Statistics in Medicine

Volumn 32, Issue 20, 2013, Pages 3459-3471

  Parast, Layla ^a   Cai, Tianxi ^b  

^a RAND CORPORATION   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Biomarkers; Disease prognosis; Landmark prediction; Predictive accuracy; Risk prediction; Survival analysis

Indexed keywords

TUMOR MARKER;

ARTICLE; BREAST CANCER; CANCER MORTALITY; CANCER PROGNOSIS; CANCER SURVIVAL; CHROMOSOMAL INSTABILITY; DATA BASE; DIAGNOSTIC ACCURACY; GENETIC SCREENING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; METASTASIS; MINIMAL RESIDUAL DISEASE; OUTCOME ASSESSMENT; PATIENT INFORMATION; PREDICTION; PROPORTIONAL HAZARDS MODEL; RISK ASSESSMENT; SCORING SYSTEM; SIMULATION; STATISTICAL MODEL; SURVIVAL TIME;

BIOMARKERS; DISEASE PROGNOSIS; LANDMARK PREDICTION; PREDICTIVE ACCURACY; RISK PREDICTION; SURVIVAL ANALYSIS;

BREAST NEOPLASMS; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; PREDICTIVE VALUE OF TESTS; PROPORTIONAL HAZARDS MODELS; RISK ASSESSMENT;

EID: 84882271493     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5776     Document Type: Article

References (46)

1. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P, Narod SA. Triple-negative breast cancer: clinical features and patterns of recurrence. Clinical Cancer Research 2007; 13(15):4429. DOI: 10.1158/1078-0432.CCR-06-3045.
2. Goldhirsch A, Wood WC, Senn HJ, Glick JH, Gelber RD. Meeting highlights: international consensus panel on the treatment of primary breast cancer. Journal of the National Cancer Institute 1995; 87(19):1441. DOI: 10.1093/jnci/87.19.1441.
3. Cortese G, Andersen PK. Competing risks and time-dependent covariates. Biometrical Journal 2010; 52(1):138-158. DOI: 10.1002/bimj.200900076.
4. Lee SJ, Klein JP, Barrett AJ, Ringden O, Antin JH, Cahn J-Y, Carabasi MH, Gale RP, Giralt S, Hale GA, Ilhan O, McCarthy PL, Socie G, Verdonck LF, Weisdorf DJ, Horowitz MM. Severity of chronic graft-versus-host disease: association with treatment-related mortality and relapse. Blood 2002; 100(2):406-414. DOI: 10.1182/blood.V100.2.406.
5. Hirschtick RE, Glassroth J, Jordan MC, Wilcosky TC, Wallace JM, Kvale PA, Markowitz N, Rosen MJ, Mangura BT, Hopewell PC, the Pulmonary Complications of HIV Infection Study Group. Bacterial pneumonia in persons infected with the human immunodeficiency virus. New England Journal of Medicine 1995; 333(13):845. DOI: 10.1056/NEJMI199509283331305.
6. Hatteville L, Mahe C, Hill C. Prediction of the long-term survival in breast cancer patients according to the present oncological status. Statistics in Medicine 2002; 21(16):2345-2354. DOI: 10.1002/sim.1046.
7. Putter H, vander Hage J, de Bock GH, Elgalta R, vande Velde CJH. Estimation and prediction in a multi-state model for breast cancer. Biometrical Journal 2006; 48(3):366-380. DOI: 10.1002/bimj.200510218.
8. Kay R. A Markov model for analysing cancer markers and disease states in survival studies. Biometrics 1986; 42(4):855-865. DOI: 10.2307/2530699.
9. Klein JP, Keiding N, Copelan EA. Plotting summary predictions in multistate survival models: probabilities of relapse and death in remission for bone marrow transplantation patients. Statistics in Medicine 1994; 12(24):2315-2332. DOI: 10.1002/sim.4780122408.
10. Datta S, Satten GA, Datta S. Nonparametric estimation for the three-stage irreversible illness-death model. Biometrics 2000; 56(3):841-847. DOI: 10.1111/j.0006-341X.2000.00841.x.
11. Fine J, Jiang H, Chappell R. On semi-competing risks data. Biometrika 2001; 88(4):907-919.
12. Siannis F, Farewell V, Head J. A multi-state model for joint modeling of terminal and non-terminal events with application to whitehall ii. Statistics in Medicine 2007; 26(2):426-442. DOI: 10.1002/sim.2342.
13. Jiang H, Fine JP, Chappell R. Semiparametric analysis of survival data with left truncation and dependent right censoring. Biometrics 2005; 61(2):567-575. DOI: 10.1111/j.1541-0420.2005.00335.x.
14. Shih JH, Louis TA. Inferences on the association parameter in copula models for bivariate survival data. Biometrics 1995; 51(4):1384-1399. DOI: 10.2307/2533269.
15. Klaassen CAJ, Wellner JA. Efficient estimation in the bivariate normal copula model: normal margins are least favourable. Bernoulli 1997; 3(1):55-77.
16. Oakes D, Ritz J. Regression in a bivariate copula model. Biometrika 2000; 87(2):345. DOI: 10.1093/biomet/87.2.345.
17. Pitt M, Chan D, Kohn R. Efficient Bayesian inference for Gaussian copula regression models. Biometrika 2006; 93(3):537. DOI: 10.1093/biomet/93.3.537.
18. Hougaard P. Frailty models for survival data. Lifetime Data Analysis 1995; 1(3):255-273. DOI: 10.1007/BF00985760.
19. Ha ID, Lee Y, Song J. Hierarchical likelihood approach for frailty models. Biometrika 2001; 88(1):233-243. DOI: 10.1093/biomet/88.1.233.
20. Cai T, Cheng S, Wei L. Semiparametric mixed-effects models for clustered failure time data. Journal of the American Statistical Association 2002; 97(458):514-523. DOI: 10.1198/016214502760047041.
21. Zeng D, Lin D. Maximum likelihood estimation in semiparametric regression models with censored data. Journal of the Royal Statistical Society, Series B Statistical Methodology 2007; 69(4):507. DOI: 10.1111/j.1369-7412.2007.00606.x.
22. VanHouwelingen HC. Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics 2007; 34(1):70.
23. VanHouwelingen HC, Putter H. Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Analysis 2008; 14(4):447-463. DOI: 10.1007/s10985-008-9099-8.
24. VanHouwelingen HC, Putter H. Dynamic Prediction in Clinical Survival Analysis. CRC Press: Boca Raton, FL, 2012.
25. Parast L, Cheng SC, Cai T. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 2011; 53(2):294-307. DOI: 10.1002/bimj.201000150.
26. Ware J. The limitations of risk factors as prognostic tools. New England Journal of Medicine 2006; 355(25):2615. DOI: 10.1056/NEJMp068249.
27. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 2004; 159(9):882-90. DOI: 10.1093/aje/kwh101.
28. van de Vijver MJ, He YD, van 't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R. A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine 2002; 345(25):1999-2009. DOI: 10.1056/NEJMoa021967.
29. Carter SL, Eklund AC, Kohane IS, Harris LN, Szallasi Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nature Genetics 2006; 38(9):1043-1048. DOI: 10.1038/ng1861.
30. McIntosh MW, Pepe MS. Combining several screening tests: optimality of the risk score. Biometrics 2002; 58(3):657-664. DOI: 10.1111/j.0006-341X.2002.00657.x.
31. Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56(2):337-344. DOI: 10.1111/j.0006-341X.2000.00337.x.
32. Cai T, Pepe MS, Zheng Y, Lumley T, Jenney NS. The sensitivity and specificity of markers for event times. Biostatistics 2006; 7(2):182-197. DOI: 10.1093/biostatistics/kxi047.
33. Uno H, Cai T, Tian L, Wei LJ. Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 2007; 102:527-37.
34. Cai T, Cheng S. Robust combination of multiple diagnostic tests for classifying censored event times. Biostatistics 2008; 9(2):216. DOI: 10.1093/biostatistics/kxm037.
35. Swets JA, Pickett RM. Evaluation of Diagnostic Systems: Methods from Signal Detection Theory. Academy press: New York, 1982.
36. Hanley HA. Receiver operating characteristic (ROC) methodology: the state of the art. Clinical Reviews in Diagnostic Imaging 1989; 29:307-35.
37. Begg CB. Advances in statistical methodology for diagnostic medicine in the 1980's. Statistics in Medicine 1991; 10:1887-1895.
38. Harrell FE, Lee KL, Mark DB. Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 1996; 15:361-387.
39. Uno H, Cai T, Pencina MJ, DÕAgostino RB, Wei LJ. On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 2011; 30(10):1105-1117. DOI: 10.1002/sim.4154.
40. Jin Z, Ying Z, Wei LJ. A simple resampling method by perturbing the minimand. Biometrika 2001; 88(2):381-390. DOI: 10.1093/biomet/88.2.381.
41. Park Y, Wei LJ. Estimating subject-specific survival functions under the accelerated failure time model. Biometrika 2003; 90:717-723.
42. Tian L, Cai T, Goetghebeur E, Wei LJ. Model evaluation based on the sampling distribution of estimated absolute prediction error. Biometrika 2007; 94(2):297. DOI: 10.1093/biomet/asm036.
43. Pepe MS, Feng Z, Huang Y, Longton G, Prentice R, Thompson IM, Zheng Y. Integrating the predictiveness of a marker with its performance as a classifier. American Journal of Epidemiology 2008; 167(3):362. DOI: 10.1093/aje/kwm305.
44. Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in Medicine 2008; 27(2):157-172.
45. Parast L, Cheng SC, Cai T. Landmark prediction of long term survival incorporating short term event time information. Journal of the American Statistical Association 2012; 107:1492-1501. DOI: 10.1080/01621459.2012.721281.
46. Xu R, OQuigley J. Estimating average regression effect under non-proportional hazards. Biostatistics 2000; 1(4):423-439. DOI: 10.1093/biostatistics/1.4.423.