Relative risk trees for censored survival data.

By Michael LeBlanc, J. Crowley. 1992

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Generalized random forests (2016, background): “…(2016) and Su et al. (2009) for treatment effect estimation, Beygelzimer and Langford (2009) and Kallus (2016) for personalized policy allocation, and Gordon and Olshen (1985), LeBlanc and Crowley (1992), Molinaro, Dudoit and Van der Laan (2004) as well as several others for survival analysis.”
A review of survival trees (2011, methodology): “With a survival outcome, the splitting criterion used by rpart is equivalent to the one of Leblanc and Crowley (1992).”
Tree-based multivariate regression and density estimation with right-censored data (2004, methodology): “The proposed method for tree-based estimation with censoring is evaluated using a simulation study and the analysis of CGH copy number and survival data from breast cancer patients. r 2004 Elsevier Inc.”
Machine Learning for Survival Analysis (2019, methodology): “For example, Gordon and Olshen (1985) measured the homogeneity and Hellinger distances between the estimated distribution functions using the Wasserstein metric, while an exponential log-likelihood function was employed in Davis and Anderson (1989) for recursive partitioning based on the sum of resi”
Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples (2003, background): “Examples of such approaches are provided in Gordon and Olshen (1985), Davis and Anderson (1989), and Leblanc and Crowley (1992).”
Fifty Years of Classification and Regression Trees (2014, methodology): “LeBlanc & Crowley (1992) fitted a proportional hazards model with the hazard rate in node t being t .u/ D t 0.u/, where t is a constant and 0.u/ is the baseline hazard function.”
Unbiased Recursive Partitioning: A Conditional Inference Framework (2006, methodology): “, Segal 1988; LeBlanc and Crowley 1992). Survival trees using P value adjusted logrank statistics are used by Schumacher, Holländer, Schwarzer, and Sauerbrei (2001) for the evaluation of prognostic factors for the German Breast Cancer Study Group (GBSG2) data, a prospective controlled clinical trial”
Tree-augmented Cox proportional hazards models. (2005, background): “A brief and not exhaustive survey includes Gordon and Olshen (1985), Ciapmi et al. (1986), Segal (1988), Davis and Anderson (1989), Therneau et al. (1990), Loh (1991), LeBlanc and Crowley (1992, 1993), Ahn and Loh (1994), and Xu and Adak (2002).”
Heart Rate Recovery after Submaximal Exercise Testing as a Predictor of Mortality in a Cardiovascularly Healthy Cohort (2000, methodology): “001 Regular exercise Absent 106/2570 (4) 142/1393 (10) 2.”
Machine Learning for Survival Analysis: A Survey (2017, methodology): “Leblanc and Crowley [LeBlanc and Crowley 1992] measured the node deviance based on the ﬁrst step of a full likelihood estimation procedure.”
Random survival forests (2008, methodology): “One such example is Ishwaran et al. (2004), where under a proportional hazards assumption, CART methodology is used to produce a relative risk forest by exploiting an equivalence to Poisson tree likelihoods [LeBlanc and Crowley (1992)].”