A two-step method for variable selection in the analysis of a case-cohort study. (10th November 2017)

• Record Type: Journal Article
• Authors: Newcombe, P J; Connolly, S; Seaman, S; Richardson, S; Sharp, S J
• Is Part Of: International journal of epidemiology. Volume 47:Number 2(2018)
• Page Start: 597
• Page End: 604
• Publication Date: 2017-11-10
• Subjects: Case-cohort study -- survival analysis -- variable selection -- Bayesian variable selection -- type 2 diabetes -- fatty acids; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyx224
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: 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.