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Personalized medicine has gained much popularity recently as a way ofproviding better healthcare by tailoring treatments to suit individuals. Ourresearch, motivated by the UK INTERVAL blood donation trial, focuses onestimating the optimal individualized treatment rule (ITR) in the ordinaltreatment-arms setting. Restrictions on minimum lengths between whole blooddonations exist to safeguard donor health and quality of blood received.However, the evidence-base for these limits is lacking. Moreover, in England,the blood service is interested in making blood donation both safe andsustainable by integrating multi-marker data from INTERVAL and developingpersonalized donation strategies. As the three inter-donation interval optionsin INTERVAL have clear orderings, we propose a sequential re-estimationlearning method that effectively incorporates "treatment" orderings whenidentifying optimal ITRs. Furthermore, we incorporate variable selection intoour method for both linear and nonlinear decision rules to handle situationswith (noise) covariates irrelevant for decision-making. Simulations demonstrateits superior performance over existing methods that assume multiple nominaltreatments by achieving smaller misclassification rates and larger valuefunctions. Application to a much-in-demand donor subgroup shows that theestimated optimal ITR achieves both the highest utilities and largestproportions of donors assigned to the safest inter-donation interval option inINTERVAL.

Sequential Re-estimation Learning of Optimal Individualized Treatment  Rules Among Ordinal Treatments with Application to Recommended Intervals  Between Blood Donations