Decision tree for modeling survival data with competing risks

Dauda, Kazeem Adesina; Pradhan, Biswabrata; Uma Shankar, B.; Mitra, Sushmita

doi:10.1016/j.bbe.2019.05.001

Artykuł - szczegóły

Tytuł artykułu

Decision tree for modeling survival data with competing risks

Autorzy

Dauda Kazeem Adesina , Pradhan Biswabrata , Uma Shankar B. , Mitra Sushmita

Wybrane pełne teksty z tego czasopisma

Identyfikatory

DOI

10.1016/j.bbe.2019.05.001

Warianty tytułu

Języki publikacji

Abstrakty

This work considers decision tree for modeling survival data with competing risks. A Survival Classification and Regression Tree (SCART) technique is proposed for analysing survival data by modifying classification and regression tree (CART) algorithm to handle censored data for both regression and classification problems. Different performance measures for regression and classification tree are proposed. Model validation is done by two different cross-validation methods. Two real life data sets are analyzed for illustration. It is found that the proposed method improve upon the existing classical method for analysis of survival data with competing risks.

Słowa kluczowe

cross validation conditional incident function impurity measure log-odds residual marginal distribution function survival classification tree survival regression tree

walidacja krzyżowa funkcja rozkładu drzewo klasyfikacyjne drzewo regresji

Wydawca

Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier

Czasopismo

Biocybernetics and Biomedical Engineering

Rocznik

2019

Tom

Vol. 39, no. 3

Strony

697--708

Opis fizyczny

Bibliogr. 69 poz., rys., tab., wykr.

Twórcy

autor

Dauda Kazeem Adesina

Department of Statistics and Mathematical Sciences, Kwara State University, Malete, Nigeria

autor

Pradhan Biswabrata

SQC & OR Unit, Indian Statistical Institute, Kolkata, India

autor

Uma Shankar B.

uma@isical.ac.in

MIU, Indian Statistical Institute, Kolkata, India

autor

Mitra Sushmita

MIU, Indian Statistical Institute, Kolkata, India

Bibliografia

[1] Crowder M. Multivariate survival analysis and competing risks. Boca Raton, London New York: Chapman & Hall/CRC Press; 2012.
[2] Crowder M. Classical competing risks. 1st ed. Boca Raton, London New York: Chapman & Hall/CRC Press; 2001.
[3] Zhang Z. Survival analysis in the presence of competing risks. Ann Transl Med 2017;5(3):47 [pages 9].
[4] Gang L, Qing Y. Joint inference for competing risks survival data. J Am Stat Assoc 2016;111(515):1289–300.
[5] Cox D. Regression models and life-tables. J R Stat Soc Ser B (Methodol) 1972;34(2):187–220.
[6] Pepe MS, Mori M. Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Stat Med 1993;12(8):737–51.
[7] Bajorunaite R, Klein JP. Two-sample tests of the equality of two cumulative incidence functions. Comput Stat Data Anal 2007;51(9):4269–81.
[8] Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999;94 (446):496–509.
[9] Chen BE, Kramer JL, Greene MH, Rosenberg PS. Competing risks analysis of correlated failure time data. Biometrics 2008;64(1):172–9.
[10] Webin L, Yu L. Analysis of competing risks data with missing cause of failure under additive hazards model. Stat Sin 2008;18:219–34.
[11] Haller B, Schmidt G, Ulm K. Applying competing risks regression models: an overview. Lifetime Data Anal 2013;19 (1):33–58.
[12] Gerds TA, Scheike TH, Andersen PK. Absolute risk regression for competing risks: interpretation, link functions, and prediction. Stat Med 2012;31(29):3921–30.
[13] Kundu D, Pradhan B. Bayesian analysis of progressively censored competing risks data. Sankhya B 2011;73(2): 276–96.
[14] Bhattacharya S, Pradhan B, Kundu D. Analysis of hybrid censored competing risks data. Statistics 2014;48(5): 1138–54.
[15] Zhang Z, Cortese G, Combescure C, Marshall R, Lee M, Lim HJ, et al. Overview of model validation for survival regression model with competing risks using melanoma study data. Ann Transl Med 2018;6(16):325 [pages 9].
[16] Gordon L, Olshen R. Tree-structured survival analysis. Cancer Treat Rep 1985;69(10):1065–9.
[17] Dauda KA, Yahya WB, Banjoko AW. Survival analysis with multivariate adaptive regression splines using cox-snell residual. Ann Comput Sci Ser 2015;13(2):25–41.
[18] Kriner M. Survival analysis with multivariate adaptive regression splines, Ph.D. thesis. Ludwig Maximilian University of Munich; 2007.
[19] Sparapani RA, Logan BR, McCulloch RE, Laud PW. Nonparametric survival analysis using Bayesian additive regression trees (bart). Stat Med 2016;35(16):2741–53.
[20] Segal MR. Regression trees for censored data. Biometrics 1988;44(1):35–47.
[21] LeBlanc M, Crowley J. Survival trees by goodness of split. J Am Stat Assoc 1993;88(422):457–67.
[22] Keles S, Segal MR. Residual-based tree-structured survival analysis. Stat Med 2002;21(2):313–26.
[23] Feng G, Amita KM, Shande C. Developing multivariate survival trees with a proportional hazards structure. J Data Sci 2006;4:343–56.
[24] Moradian H, Larocque D, Bellavance F. Survival forests for data with dependent censoring. Stat Methods Med Res 2019;28(2):445–61.
[25] Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat 2008;2(3):841–60.
[26] Ishwaran H, Gerds T, Kogalur U, Moore R, Gange SLBM. Random survival forests for competing risks. Biostatistics 2014;15(4):757–73.
[27] Ishwaran H, Kogalur UB. Random forests for survival, regression, and classification (RF-SRC), R package version 2.5.1; 2017, https://cran.r-project.org/web/packages/randomForestSRC/ randomForestSRC.pdf.
[28] Ibrahim NA, Kudus A, Daud I, AbuBakar MR. Decision tree for competing risks survival probability in breast cancer study. Int J Biol Life Sci 2008;3(1):337–44.
[29] Fiona CM. Classification trees for survival data with competing risks, Ph.D. thesis. Department of Biostatistics, University of Pittsburgh; 2008.
[30] Lee C, Zame WR, Yoon J, van der Schaar M. Deephit: a deep learning approach to survival analysis with competing risks. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). 2018. pp. 2314–21.
[31] Bellot A, Schaar M. Tree-based Bayesian mixture model for competing risks. International Conference on Artificial Intelligence and Statistics. 2018. pp. 910–8.
[32] Wu J, Tan Y, Chen Z, Zhao M. Decision based on big data research for non-small cell lung cancer in medical artificial system in developing country. Comput Methods Prog Biomed 2018;159:87–101.
[33] Han S, Andrei A-C, Tsui K-W. Multiple imputation for competing risks survival data via pseudo-observations. CSAM (Commun Stat Appl Methods) 2018;25(4):385–96.
[34] Scrutinio D, Guida P, Passantino A, Ammirati E, Oliva F, Lagioia R, et al. Application of competing risks analysis improved prognostic assessment of patients with decompensated chronic heart failure and reduced left ventricular ejection fraction. J Clin Epidemiol 2018;103:31–9.
[35] Kretowska M. Piecewise-linear criterion functions in oblique survival tree induction. Artif Intell Med 2017;75:32–9.
[36] Linden A, Yarnold PR. Modeling time-to-event (survival) data using classification tree analysis. J Eval Clin Pract 2017;23(6):1299–308.
[37] Huebner M, Wolkewitz M, Enriquez-Sarano M, Schumacher M. Competing risks need to be considered in survival analysis models for cardiovascular outcomes. J Thorac Cardiovasc Surg 2017;153(6):1427–31.
[38] Hsu JY, Roy JA, Xie D, Yang W, Shou H, Anderson AH, et al. Statistical methods for cohort studies of ckd: survival analysis in the setting of competing risks. Clin J Am Soc Nephrol 2017;12(7):1181–9.
[39] Alaa AM, van der Schaar M. Deep multi-task Gaussian processes for survival analysis with competing risks. Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc.; 2017. p. 2326–34.
[40] Xu W, Che J, Kong Q. Recursive partitioning method on competing risk outcomes. Cancer Inform 2016;15:CIN– S39364.
[41] Scrutinio D, Guida P, Ammirati E, Oliva F, Frigerio M. Long-term prognostic implications of the adhf/nt-probnp risk score in patients admitted with advanced heart failure. J Heart Lung Transplant 2016;35(10):1264–7.
[42] Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 2006;15(3):651–74.
[43] Bellot A, van der Schaar M. Multitask boosting for survival analysis with competing risks. Advances in Neural Information Processing Systems. 2018. pp. 1397–406.
[44] Lawless JF. Statistical models and methods for lifetime data. 2nd ed. New York: Wiley; 2003.
[45] Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med 1999;18(17–18):2529–45.
[46] Breiman L, Friedman J, Olshen R, Stone CS. Classification and regression trees. Boca Raton, London, New York: Chapman & Hall/CRC Press; 1984.
[47] Haddad K, Rahman A, Zaman MA, Shrestha S. Applicability of Monte Carlo cross validation technique for model development and validation using generalised least squares regression. J Hydrol 2013;482:119–28.
[48] Haque MM, Hadda K, Rahman A, Hossain M, Hagere D, Kabria G. Long term demand forecasting: use of monte carlo cross validation for the best model selection. 20th International Congress on Modelling and Simulation; 2013. pp. 2311–7.
[49] Konovalov DA, Sim N, Deconinck E, Vander HY, Coomans D. Statistical confidence for variable selection in QSAR models via Monte Carlo cross-validation. J Chem Inf Model 2008;48 (2):370–83.
[50] Mosteller F, Tukey JW. Data analysis and regression: a second course in statistics. Reading, MA: Addison-Wesley; 1977.
[51] Efron B, Tibshirani R. Improvements on cross-validation: the 0.632þ bootstrap method. J Am Stat Assoc 1997;92 (438):548–60.
[52] Xu QS, Liang YZ, Du YP. Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration. J Chemom 2004;18(2):112–20.
[53] Marter H, Martern M. Multivariate analysis of quality: an introduction. Chichester: John Wiley & Sons Ltd.; 2001.
[54] Picard RR, Cook RD. Cross-validation of regression models. J Am Stat Assoc 1984;79(387):575–83.
[55] Brier GW. Verification of forecasts expressed in terms of probability. Mon Weather Rev 1950;78(1):1–3.
[56] Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J 2008;50(4):457–79.
[57] Gerds TA, Schumacher M. Consistent estimation of the expected brier score in general survival models with right-censored event times. Biom J 2006;48(6):1029–40.
[58] Gerds TA, Schumacher M. Efron-type measures of prediction error for survival analysis. Biometrics 2007;63 (4):1283–7.
[59] Uno H, Cai T, Tian L, Wei L. Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc 2007;102(478):527–37.
[60] Choi I, Wells BJ, Yu C, Kattan MW. An empirical approach to model selection through validation for censored survival data. J Biomed Inform 2011;44(4):595–606.
[61] Porzelius C, Schumacher M, Binder H. The benefit of data-based model complexity selection via prediction error curves in time-to-event data. Comput Stat 2011;26(2):293–302.
[62] Porzelius C, Binder H. peperr:parallelised estimation of prediction error, R package version 1; 2010;1–7, https://cran.r-project.org/web/packages/peperr/peperr.pdf.
[63] Porzelius C, Schumacher M, Binder H. A general, prediction error-based criterion for selecting model complexity for high-dimensional survival models. Stat Med 2010; 29(7–8):830–8.
[64] Porzelius C, Binder H, Schumacher M. Parallelized prediction error estimation for evaluation of high-dimensional models. Bioinformatics 2009;25(6):827–9.
[65] Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56(2):337–44.
[66] Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005;61(1):92–105.
[67] Fleming TR, Harrington DP. Counting processes and survival analysis. New York: Wiley; 1991.
[68] Pintilie M. Competing risks: a practical perspective. West Sussex: John Wiley and Sons; 2006.
[69] Gerds TA, Kattan MW, Schumacher M, Yu C. Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring. Stat Med 2013;32(13):2173–84.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-f730ddef-a674-4b80-8b9a-ed5b333a28e2