Stratification by stepwise regression, correspondence analysis and recursive partition: A comparison of three methods of analysis for survival data with covaria

By A. Ciampi, J. Thiffault, J. Nakache, B. Asselain. 1986

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Fifty Years of Classification and Regression Trees (2014, methodology): “Ciampi et al. (1986) compared stepwise Cox regression, correspondence analysis, and recursive partitioning models for censored response data.”
Machine Learning for Survival Analysis: A Survey (2017, methodology): “In the second class of splitting criteria, Ciampi et al. [Ciampi et al. 1986] employed log-rank test statistics for between-node heterogeneity measures.”
Machine Learning for Survival Analysis (2019, methodology): “The same group (Ciampi et al. 1987) then went on to propose the use of a likelihood ratio statistic to measure the dissimilarity between two nodes.”
Regression Modeling Strategies (2003, methodology): “3 Transformation and Scaling Variables Without Using Y stateg ytran san C • Reduce p by estimating transformations using associations with other predictors • Purely categorical predictors – correspondence analysis [118, 49, 38, 80, 134]”
Subgroup Analysis via Recursive Partitioning (2009, unknown): “In this case, a merging scheme analogous to the approach of Ciampi et al. (1986) can be useful to further bring down the number of final subgroups.”
A review of survival trees (2011, methodology): “Ciampi et al. (1986) then suggested using the logrank statistic to compare the two groups formed by the children nodes.”