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A two-step method for variable selection in the analysis of a case-cohort study.

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Newcombe, PJ 
Connolly, S 
Sharp, SJ 


Background: Accurate detection and estimation of true exposure-outcome associations is important in aetiological analysis; when there are multiple potential exposure variables of interest, methods for detecting the subset of variables most likely to have true associations with the outcome of interest are required. Case-cohort studies often collect data on a large number of variables which have not been measured in the entire cohort (e.g. panels of biomarkers). There is a lack of guidance on methods for variable selection in case-cohort studies. Methods: We describe and explore the application of three variable selection methods to data from a case-cohort study. These are: (i) selecting variables based on their level of significance in univariable (i.e. one-at-a-time) Prentice-weighted Cox regression models; (ii) stepwise selection applied to Prentice-weighted Cox regression; and (iii) a two-step method which applies a Bayesian variable selection algorithm to obtain posterior probabilities of selection for each variable using multivariable logistic regression followed by effect estimation using Prentice-weighted Cox regression. Results: Across nine different simulation scenarios, the two-step method demonstrated higher sensitivity and lower false discovery rate than the one-at-a-time and stepwise methods. In an application of the methods to data from the EPIC-InterAct case-cohort study, the two-step method identified an additional two fatty acids as being associated with incident type 2 diabetes, compared with the one-at-a-time and stepwise methods. Conclusions: The two-step method enables more powerful and accurate detection of exposure-outcome associations in case-cohort studies. An R package is available to enable researchers to apply this method.



Bayesian variable selection, case-cohort study, fatty acids, survival analysis, type 2 diabetes, variable selection

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International Journal of Epidemiology

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Oxford University Press
MRC (1185)
Medical Research Council (MC_UU_12015/1)
MRC (unknown)
MRC (unknown)
P.J.N. and S.R. were supported by the Medical Research Council [] (Unit Programme number MC_UP_0801/1). P.J.N. also acknowledges partial support from the NIHR Cambridge Biomedical Research Centre. S.S. and S.C. were supported by the Medical Research Council [] (Unit Programme number MC_U105260558). S.J.S. was supported by the Medical Research Council [] (Unit Programme number MC_UU_12015/1). Funding for the EPIC-InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197).