Statistical design of personalized medicine interventions: The Clarification of Optimal Anticoagulation through Genetics (COAG) trial
Statistical design of personalized medicine interventions: The Clarification of Optimal Anticoagulation through Genetics (COAG) trial
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DOI：
10.1186/1745-6215-11-108
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发表时间：
2010-11-17
期刊：
影响因子：
2.5
通讯作者：
Ellenberg, Jonas H.
中科院分区：
文献类型：
--
作者：
French, Benjamin;Joo, Jungnam;Ellenberg, Jonas H.
工作背景：目前，人们对药物遗传学很感兴趣：确定调节药物作用的基因变异，特别强调提高药物的安全性和有效性。确定这种变化的能力促使个性化药物治疗的应用，该个性化药物治疗利用患者的遗传组成来确定正确剂量的安全有效的药物。为了确定基因型指导的药物治疗是否改善了患者护理，可以在随机对照试验的框架内评估个性化药物干预。这种类型的个性化药物干预的统计设计需要特别考虑：研究人群中相关等位基因变异的分布;以及药物遗传学干预在等位基因变异定义的亚群中是否同样有效。通过遗传学阐明最佳抗凝（COAG）的统计设计该试验作为使用每个受试者的基因型信息的个性化药物干预的说明性示例。COAG试验是一项多中心、双盲、随机临床试验，将比较两种开始华法林治疗的方法：基因型指导给药，根据使用临床信息和CYP 2C 9和VKORC 1多态性基因型的算法开始华法林治疗;临床指导给药，根据仅使用临床信息的算法开始华法林治疗。结果：基于假设60%的人群在CYP 2C 9或VKORC 1中存在零个或多个遗传变异，以及假设对于零个或多个遗传变异的患者，基因型指导的华法林启动治疗的相对有效性为15%，我们确定绝对最小可检测差异为5.49%。因此，我们计算出样本量为1238，以达到主要结局的80%的把握度水平。我们发现，合理偏离这些假设可能会使统计功效降低至65%.Conclusions：在个性化药物干预中，样本量计算中使用的最小可检测差异不是一个已知量，而是一个未知量，取决于所招募受试者的基因组成。考虑到样本量和把握度计算对这些关键假设的可能敏感性，我们建议在进行个性化药物干预期间对其进行监测。
Background: There is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.Methods: The statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in CYP2C9 and VKORC1; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.Results: We determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either CYP2C9 or VKORC1 and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.Conclusions: In a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.