Super Learner is an ensemble machine learning method that estimates a conditional outcome risk model as the optimal combination of individual regression algorithms that maximize a cross-validated criterion for best disease classification accuracy. Specifically, we minimized the leave-one-out, cross-validated area under the ROC curve (cv-AUC). Super Learner prediction models were built with the same baseline covariates and VL kinetics defined for the logistic regression analysis and were fit on data from the placebo group alone, the ganciclovir group alone, and the combined treatment groups, with individual regression algorithms specified in the Supplemental Methods. cv-AUCs were calculated for each Super Learner prediction model, with a predefined benchmark that cv-AUCs greater than 85% would provide evidence for the fitted values (i.e., predicted outcome risks) as potentially valid surrogates. Super Learning was implemented using the R package Super Learner (51).

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