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

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.

Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival.

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.

Comprehensive genome-wide evaluation of lapatinib-induced liver injury yields a single genetic signal centered on known risk allele HLA-DRB1*07:01.

Lapatinib is associated with a low incidence of serious liver injury. Previous investigations have identified and confirmed the Class II allele HLA-DRB1*07:01 to be strongly associated with lapatinib-induced liver injury; however, the moderate positive predictive value limits its clinical utility. To assess whether additional genetic variants located within the major histocompatibility complex locus or elsewhere in the genome may influence lapatinib-induced liver injury risk, and potentially lead to a genetic association with improved predictive qualities, we have taken two approaches: a genome-wide association study and a whole-genome sequencing study. This evaluation did not reveal additional associations other than the previously identified association for HLA-DRB1*07:01. The present study represents the most comprehensive genetic evaluation of drug-induced liver injury (DILI) or hypersensitivity, and suggests that investigation of possible human leukocyte antigen associations with DILI and other hypersensitivities represents an important first step in understanding the mechanism of these events.

Early infection with Trichuris trichiura and allergen skin test reactivity in later childhood.

BACKGROUND: Allergic diseases cause a large and increasing burden in developed countries and in urban centres in middle-income countries. The causes of this increase are unknown and, currently, there are no interventions to prevent the development of allergic diseases. The 'hygiene hypothesis' has tried to explain the increase through a reduction in the frequency of childhood infections causing a failure to program the immune system for adequate immune regulation. Intestinal helminth parasites are prevalent in childhood in developing countries and are associated with a lower prevalence of allergen skin test reactivity and asthma. OBJECTIVES: To investigate whether children who had intestinal helminth infections during early childhood have a lower prevalence of allergen skin test reactivity later in childhood. METHODS: We re-visited a population of 1055 children from whom stool samples had been collected for detection of intestinal helminth infections for another study, and collected new stool samples and performed allergen skin prick testing. Information on potential confounding variables was collected. RESULTS: Children with heavy infections with Trichuris trichiura in early childhood had a significantly reduced prevalence of allergen skin test reactivity in later childhood, even in the absence of T. trichiura infection at the time of skin testing in later childhood. CONCLUSION: Early heavy infections with T. trichiura may protect against the development of allergen skin test reactivity in later childhood. Novel treatments to program immune-regulation in early childhood in a way that mimics the effects of early infections with T. trichiura may offer new strategies for the prevention of allergic disease.