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:最近,个人医学作为一种通过定制适合个人的治疗方法提供更好的保健的方式,最近受到人们的欢迎。我们的研究在英国国际海军间献血试验的推动下,侧重于在正统治疗武器设置中估计最佳个人化治疗规则(ITR)。对整个献血者的最低长度有限制,以保障捐赠者的健康和所收血液的质量。然而,缺乏这些限制的证据依据。此外,在英格兰,血液服务有意通过整合INTEV的多标记数据,制定个性化捐赠战略,使献血既安全又可持续。由于InterVAL的三种捐赠间隔选择都有明确的命令,我们建议了一种顺序上的重新估算学习方法,在确定最佳ITR时有效地纳入“治疗”命令。此外,我们把不同选择纳入我们处理线性和非线性决定规则的方法,以便处理与决策无关的情况。模拟表明,它优于现有方法,即假设多种名义治疗方法,即降低分类率和更大的价值功能。在IVAL中,对一个高要求的捐赠者间最大选择分组的应用显示,在最高一级分配的ITR中,最高级的捐助者之间估计了最佳选择。