Personalized treatment selection is often based on a single primary endpoint, even though complex diseases are typically monitored using multiple, heterogeneous outcomes. We develop a rank-based framework for individualized treatment selection that accommodates mixed collections of continuous, ordinal, binary, and right-censored survival outcomes. For each outcome and treatment, we summarize the conditional outcome distribution given patient covariates using clinically meaningful parameters, including conditional quantiles, survival probabilities, and tail probabilities at ordered thresholds, and assemble these summaries across treatment arms into vectors with a common structure. Each summary component induces a ranking of the treatments, and discrepancies between outcome-specific rankings and candidate ranking vectors are quantified using a Mahalanobis-type distance that incorporates user-specified outcome weights and an empirically estimated scaling matrix. The optimal treatment is defined as the arm with the most favorable overall rank. Outcome summaries are estimated using flexible tree-based ensemble methods, although other regression or machine-learning tools can be used without altering the decision rule. Monte Carlo simulations across a range of scenarios show reasonably high probabilities of correct treatment selection, with performance improving as the signal-to-noise ratio increases and only mildly affected by the choice of outcome weights. We illustrate the method using data from an AIDS clinical trial, deriving individualized antiretroviral regimen recommendations that balance CD4 response, CD4/CD8 ratio, and a composite time-to-event endpoint.
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