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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/407

Title: Multicollinearity in prognostic factor analysis using the EORTC QLQ-C30: identification and impact on model selection
Authors: Van Steen, Kristel
Curran, Desmond
Kramer, Jocelyn
Molenberghs, Geert
Van Vreckem, Ann
Sylvester, Richard
Keywords: Clinical trials
Issue Date: 2002
Publisher: JOHN WILEY
Citation: Statistics in Medicine, 21(24). p. 3865-3884
Abstract: Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright © 2002 John Wiley & Sons, Ltd
URI: http://hdl.handle.net/1942/407
Link to publication: https://www.academia.edu/18887680/Multicollinearity_in_prognostic_factor_analyses_using_the_EORTC_QLQ-C30_identification_and_impact_on_model_selection
DOI: 10.1002/sim.1358
ISI #: 000180039600009
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2004
Appears in Collections: Research publications

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