The estimation of the effect of environmental exposures and overall mixtures on a survival time outcome is common in environmental epidemiological studies. While advanced statistical methods are increasingly being used for mixture analyses, their applicability and performance for survival outcomes has yet to be explored. We identified readily available methods for analyzing an environmental mixture's effect on a survival outcome and assessed their performance via simulations replicating various real-life scenarios. Using prespecified criteria, we selected Bayesian Additive Regression Trees (BART), Cox Elastic Net, Cox Proportional Hazards (PH) with and without penalized splines, Gaussian Process Regression (GPR) and Multivariate Adaptive Regression Splines (MARS) to compare the bias and efficiency produced when estimating individual exposure, overall mixture, and interaction effects on a survival outcome. 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 data from the Strong Heart Study (SHS). In the simulation study, there was a consistent bias-variance trade off. The more flexible models (BART, GPR and MARS) were found to be most advantageous in the presence of nonproportional hazards, where the Cox models often did not capture the true effects due to their higher bias and lower variance. In the SHS, estimates of the effect of selenium and the overall mixture indicated negative effects, but the magnitudes of the estimated effects varied across methods. In practice, we recommend evaluating if findings are consistent across methods.

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