1 |
Nicolaie MA, van Houwelingen JC, deWitte TM, and Putter H (2013). Dynamic pseudo-observations: a robust approach to dynamic prediction in competing risks, Biometrics, 69, 1043-1052.
DOI
|
2 |
Ripley B (2014). tree: Classification and regression trees. R package version 1.0-35, from: http://CRAN.R-project.org/package=tree
|
3 |
Royston P and White IR (2011). Multiple imputation by chained equations (MICE): implementation in Stata, Journal of Statistical Software, 45, 1-20.
|
4 |
Rubin DB (1987). Multiple Imputation for Nonresponse in Surveys, Wiley, New York.
|
5 |
Shah AD, Bartlett JW, Carpenter J, Nicholas O, and Hemingway H (2014). Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study, American Journal of Epidemiology, 179, 764-774.
DOI
|
6 |
Seung KB, Park DW, Kim YH et al. (2008). Stents versus coronary-artery bypass grafting for left main coronary artery disease, The New England Journal of Medicine, 358, 1781-1792.
DOI
|
7 |
Shim JH, Yoon DL, Han S, et al. (2012). Is Serum Alpha-Fetoprotein useful for predicting recurrence and mortality specific to hepatocellular carcinoma after hepatectomy? A test based on propensity scores and competing risks analysis, Annals of Surgical Oncology, 19, 3687-3696.
DOI
|
8 |
van Buuren S, Boshuizen HC, and Knook DL (1999). Multiple imputation of missing blood pressure covariates in survival analysis, Statistics in Medicine, 18, 681-694
DOI
|
9 |
van Buuren S and Groothuis-Oudshoorn K (2011). mice: multivariate imputation by chained equations in R, Journal of Statistical Software, 45, 1-67.
|
10 |
Breiman L (2001). Random forests, Machine Learning, 45, 5-32.
DOI
|
11 |
Burgette LF and Reiter JP (2010). Multiple imputation for missing data via sequential regression trees, American Journal of Epidemiology, 172, 1070-1076.
DOI
|
12 |
Do G and Kim YJ (2017). Analysis of interval censored competing risk data with missing causes of failure using pseudo values approach, Journal of Statistical Computation and Simulation, 87, 631-639.
DOI
|
13 |
Fine J and Gray R (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association, 94, 496-509.
DOI
|
14 |
Graham JW, Olchowski AE, and Gilreath TD (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory, Prevention Science, 8, 206-213.
DOI
|
15 |
Gray B (2014). cmprsk: Subdistribution Analysis of Competing Risks, R package version 2.2-7. http://CRAN.R-project.org/package=cmprsk
|
16 |
Moreno-Betancur M and Latouche A (2013). Regression modeling of the cumulative incidence function with missing causes of failure using pseudo-values, Statistics in Medicine, 32, 3206-3223.
DOI
|
17 |
Graw F, Gerds TA, and Schumacher M (2009). On pseudo-values for regression analysis in competing risks models, Lifetime Data Analysis, 15, 241-255.
DOI
|
18 |
Kim S and Kim YJ (2016). Regression analysis of interval censored competing risk data using a pseudo-value approach, Communications for Statistical Applications and Methods, 23, 555-562.
DOI
|
19 |
Klein JP and Andersen PK (2005). Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function, Biometrics, 61, 223-229.
DOI
|
20 |
Beyersmann J, Allignol A, and Schumacher M (2012). Competing Risks and Multistate Models with R, Springer-Verlag New York, Chapter 3, 45-50.
|
21 |
Liaw A and Wiener M (2002). Classification and regression by randomForest, R News, 2, 18-22.
|
22 |
Logan BR, Zhang MJ, and Klein JP (2011). Marginal models for clustered time to event data with competing risks using pseudovalues, Biometrics, 67, 1-7.
DOI
|
23 |
Mogensen UB and Gerds TA (2013). A random forest approach for competing risks based on pseudovalues, Statistics in Medicine, 32, 3102-3114.
DOI
|
24 |
Andersen PK and Perme MP (2010). Pseudo-observations in survival analysis, Statistical Methods in Medical Research, 19, 71-99.
DOI
|
25 |
Ambler G, Omar RZ, Royston P, Kinsman R, Keogh BE, and Taylor KM (2005). Generic, simple risk stratification model for heart valve surgery, Circulation, 112, 224-231.
DOI
|
26 |
Ahn KW and Mendolia F (2014). Pseudo-value approach for comparing survival medians for dependent data, Statistics in Medicine, 33, 1531-1538.
DOI
|
27 |
Ambler G, Omar RZ, and Royston P (2007). A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome, Statistical Methods in Medical Research, 16, 277-298.
DOI
|
28 |
Aalen O, Borgan O, and Gjessing H (2008). Survival and Event History Analysis, Springer, New York.
|