Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria

Abdissa Negassa; Antonio Ciampi; Michal Abrahamowicz; Stanley Shapiro; Jean François Boivin

doi:10.1007/s11222-005-1311-z

Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria

Abdissa Negassa, Antonio Ciampi, Michal Abrahamowicz, Stanley Shapiro, Jean François Boivin

Epidemiology & Population Health

Research output: Contribution to journal › Article › peer-review

54 Scopus citations

Abstract

The performance of computationally inexpensive model selection criteria in the context of tree-structured subgroup analysis is investigated. It is shown through simulation that no single model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is proposed and shown to perform satisfactorily. Applied example of subgroup analysis is presented. Problems associated with tree-structured subgroup analysis are discussed and practical solutions are suggested.

Original language	English (US)
Pages (from-to)	231-239
Number of pages	9
Journal	Statistics and Computing
Volume	15
Issue number	3
DOIs	https://doi.org/10.1007/s11222-005-1311-z
State	Published - Jul 2005

Keywords

Censored survival data
Model selection
Regression tree
Subgroup analysis
Two-stage approach

ASJC Scopus subject areas

Theoretical Computer Science
Statistics and Probability
Statistics, Probability and Uncertainty
Computational Theory and Mathematics

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10.1007/s11222-005-1311-z

Cite this

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title = "Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria",

abstract = "The performance of computationally inexpensive model selection criteria in the context of tree-structured subgroup analysis is investigated. It is shown through simulation that no single model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is proposed and shown to perform satisfactorily. Applied example of subgroup analysis is presented. Problems associated with tree-structured subgroup analysis are discussed and practical solutions are suggested.",

keywords = "Censored survival data, Model selection, Regression tree, Subgroup analysis, Two-stage approach",

author = "Abdissa Negassa and Antonio Ciampi and Michal Abrahamowicz and Stanley Shapiro and Boivin, {Jean Fran{\c c}ois}",

note = "Funding Information: This research was supported by the Heart and Stroke foundation of Canada postgraduate scholarship (AN). M.A. is a James McGill Professor at McGill University.",

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AU - Negassa, Abdissa

AU - Ciampi, Antonio

AU - Abrahamowicz, Michal

AU - Shapiro, Stanley

AU - Boivin, Jean François

N1 - Funding Information: This research was supported by the Heart and Stroke foundation of Canada postgraduate scholarship (AN). M.A. is a James McGill Professor at McGill University.

PY - 2005/7

Y1 - 2005/7

N2 - The performance of computationally inexpensive model selection criteria in the context of tree-structured subgroup analysis is investigated. It is shown through simulation that no single model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is proposed and shown to perform satisfactorily. Applied example of subgroup analysis is presented. Problems associated with tree-structured subgroup analysis are discussed and practical solutions are suggested.

AB - The performance of computationally inexpensive model selection criteria in the context of tree-structured subgroup analysis is investigated. It is shown through simulation that no single model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is proposed and shown to perform satisfactorily. Applied example of subgroup analysis is presented. Problems associated with tree-structured subgroup analysis are discussed and practical solutions are suggested.

KW - Censored survival data

KW - Model selection

KW - Regression tree

KW - Subgroup analysis

KW - Two-stage approach

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JO - Statistics and Computing

JF - Statistics and Computing

IS - 3

ER -

Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria

Abstract

Keywords

ASJC Scopus subject areas

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