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The estimation of the effect of environmental exposures and overall mixtureson a survival time outcome is common in environmental epidemiological studies.While advanced statistical methods are increasingly being used for mixtureanalyses, their applicability and performance for survival outcomes has yet tobe explored. We identified readily available methods for analyzing anenvironmental mixture's effect on a survival outcome and assessed theirperformance via simulations replicating various real-life scenarios. Usingprespecified criteria, we selected Bayesian Additive Regression Trees (BART),Cox Elastic Net, Cox Proportional Hazards (PH) with and without penalizedsplines, Gaussian Process Regression (GPR) and Multivariate Adaptive RegressionSplines (MARS) to compare the bias and efficiency produced when estimatingindividual exposure, overall mixture, and interaction effects on a survivaloutcome. We illustrate the selected methods in a real-world data application.We estimated the effects of arsenic, cadmium, molybdenum, selenium, tungsten,and zinc on incidence of cardiovascular disease in American Indians using datafrom the Strong Heart Study (SHS). In the simulation study, there was aconsistent bias-variance trade off. The more flexible models (BART, GPR andMARS) were found to be most advantageous in the presence of nonproportionalhazards, where the Cox models often did not capture the true effects due totheir higher bias and lower variance. In the SHS, estimates of the effect ofselenium and the overall mixture indicated negative effects, but the magnitudesof the estimated effects varied across methods. In practice, we recommendevaluating if findings are consistent across methods.

Comparison of methods for analyzing environmental mixtures effects on survival outcomes and application to a population-based cohort study