Lei Yang, Mayo Matthew S, Carlson Susan E, Gajewski Byron J
Department of Biostatistics, The University of Kansas Medical Center, School of Medicine, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS 66160, USA.
Department of Dietetics and Nutrition, The University of Kansas Medical Center, Kansas City, KS 66160, USA.
Contemp Clin Trials Commun. 2017 Mar;5:116-122. doi: 10.1016/j.conctc.2017.01.002. Epub 2017 Jan 27.
Personalized medicine aims to match patient subpopulation to the most beneficial treatment. The purpose of this study is to design a prospective clinical trial in which we hope to achieve the highest level of confirmation in identifying and making treatment recommendations for subgroups, when the risk levels in the control arm can be ordered. This study was motivated by our goal to identify subgroups in a DHA (docosahexaenoic acid) supplementation trial to reduce preterm birth (gestational age<37 weeks) rate. We performed a meta-analysis to obtain informative prior distributions and simulated operating characteristics to ensure that overall Type I error rate was close to 0.05 in designs with three different models: independent, hierarchical, and dynamic linear models. We performed simulations and sensitivity analysis to examine the subgroup power of models and compared results to a chi-square test. We performed simulations under two hypotheses: a large overall treatment effect and a small overall treatment effect. Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear. When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect. It also outperformed other methods when resulting subgroup rates are nonlinear and the overall treatment effect is big. When the resulting subgroup rates are nonlinear and the overall treatment effect is small, hierarchical model and chi-square test did better. Compared to independent and hierarchical models, dynamic linear model tends to be relatively robust and powerful when the control arm has ordinal risk subgroups.
个性化医疗旨在使患者亚群与最有益的治疗方法相匹配。本研究的目的是设计一项前瞻性临床试验，当对照组的风险水平可排序时，我们希望在识别亚组并做出治疗建议方面达到最高水平的验证。本研究的动机是我们的目标，即在一项二十二碳六烯酸（DHA）补充试验中识别亚组，以降低早产（孕周<37周）率。我们进行了一项荟萃分析以获得信息性先验分布，并模拟操作特征，以确保在三种不同模型（独立模型、分层模型和动态线性模型）的设计中，总体I型错误率接近0.05。我们进行了模拟和敏感性分析，以检验模型的亚组效能，并将结果与卡方检验进行比较。我们在两个假设下进行了模拟：一个是总体治疗效果大，另一个是总体治疗效果小。在每个假设下，我们设计了三种不同的亚组效应情景，其中产生的亚组率是线性的、平坦的或非线性的。当产生的亚组率是线性或平坦时，动态线性模型似乎是识别有治疗效果亚组的最有效方法。当产生的亚组率是非线性的且总体治疗效果大时，它也优于其他方法。当产生的亚组率是非线性的且总体治疗效果小时，分层模型和卡方检验表现更好。与独立模型和分层模型相比，当对照组有有序风险亚组时，动态线性模型往往相对稳健且有效。
