Personalized medicine has gained much popularity recently as a way of providing better healthcare by tailoring treatments to suit individuals. Our research, motivated by the UK INTERVAL blood donation trial, focuses on estimating the optimal individualized treatment rule (ITR) in the ordinal treatment-arms setting. Restrictions on minimum lengths between whole blood donations 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 and sustainable by integrating multi-marker data from INTERVAL and developing personalized donation strategies. As the three inter-donation interval options in INTERVAL have clear orderings, we propose a sequential re-estimation learning method that effectively incorporates "treatment" orderings when identifying optimal ITRs. Furthermore, we incorporate variable selection into our method for both linear and nonlinear decision rules to handle situations with (noise) covariates irrelevant for decision-making. Simulations demonstrate its superior performance over existing methods that assume multiple nominal treatments by achieving smaller misclassification rates and larger value functions. Application to a much-in-demand donor subgroup shows that the estimated optimal ITR achieves both the highest utilities and largest proportions of donors assigned to the safest inter-donation interval option in INTERVAL.

翻译：个性化医疗近年来因通过定制治疗以提供更佳医疗护理而广受关注。本研究受英国INTERVAL献血试验启发，聚焦于在有序治疗组设置中估计最优个体化治疗方案。为保障献血者健康及所采集血液的质量，全血捐献的最小间隔限制存在，但支持这些限制的证据基础不足。此外，在英格兰，血液服务机构通过整合INTERVAL试验的多标志物数据并开发个性化献血策略，致力于使献血既安全又可持续。鉴于INTERVAL中的三种献血间隔选项具有明确顺序，我们提出一种序贯重估计学习方法，在识别最优个体化治疗方案时有效纳入“治疗”顺序。进一步地，我们在线性和非线性决策规则中融入变量选择，以处理与决策无关的噪声协变量情况。模拟实验表明，与假定多种名义治疗的现有方法相比，本方法通过更低的误分类率和更大的价值函数实现了更优性能。应用于高需求献血者亚组的结果显示，估计的最优个体化治疗方案在INTERVAL中同时实现了最高效用和最大比例献血者被分配至最安全献血间隔选项。