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Assessment of Optimal Selected Prognostic Factors

Lausen, B and Hothorn, T and Bretz, F and Schumacher, M (2004) 'Assessment of Optimal Selected Prognostic Factors.' Biometrical Journal, 46 (3). pp. 364-374. ISSN 0323-3847

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The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B-cell lymphoma. Using the null distribution for a p-value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well.

Item Type: Article
Uncontrolled Keywords: Prognostic factor; Clinical research; Statistical computing; Variable selection; Maximally selected tests; p-value adjusted regression trees
Subjects: H Social Sciences > HA Statistics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 29 May 2012 15:13
Last Modified: 06 Jan 2022 13:24

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