The mean does not mean as much anymore: finding sub-groups for tailored therapeutics
The mean does not mean as much anymore: finding sub-groups for tailored therapeutics
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DOI：
10.1177/1740774510369350
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发表时间：
2010-10-01
期刊：
影响因子：
2.7
通讯作者：
Wang, Yanping
中科院分区：
文献类型：
--
作者：
Ruberg, Stephen J.;Chen, Lei;Wang, Yanping
背景基因组学革命仍处于起步阶段，关于如何将生物学知识转化为有用的药物以促进公共卫生，还有很多东西需要学习。在床边，我们询问个体患者如何以及为什么以不同的方式对不同的药物治疗作出反应。除了遗传机制外，还有许多临床标志物（例如，G.病史、疾病严重程度）以及社会/环境因素（例如，G.吸烟习惯），可用于确定谁可能或可能不响应治疗。目的这个问题有一些相当大的统计复杂性，不同的方法来分析临床试验可能会产生更有趣的见解的问题。新的统计方法的应用进行了讨论，并将使用的例子来证明子组identifiation.Methods为了评估许多潜在的预测响应，我们使用递归分区方法来确定预测变量和他们的截止值，以定义子组的差异治疗反应的患者。验证这种变量/模型选择方法是使用独立的数据从其他临床试验。结果在一个例子中，分类树开发使用基线措施，以定义重要的亚组的患者，响应比总体平均响应在研究中更好。在第二个例子中，基于治疗早期的反应指标构建分类树，以预测长期反应者和无反应者。限制分类算法可能容易过度拟合，结果验证是一个重要的考虑因素。显然，分析是有限的可用的预测variables.Conclusions使用分类树被证明是非常有用的，在评估大量的潜在的预测，找到亚组的异常反应的患者。该方法易于使用，临床医生可以很容易地解释和实施结果。这种方法可以帮助为个别患者量身定制治疗。临床试验2010; 7：574-583。http://ctj.sagepub.com
Background The genomics revolution is still in its infancy, and there is much to learn about how to transform biological knowledge into useful medicines to further public health. At the bedside, we are asking how and why individual patients respond to different drug treatments in different ways. In addition to genetic mechanisms, there are many clinical markers (e. g. medical history, disease severity) as well as social/environmental factors (e. g. smoking habits) that can be used to identify who may or may not respond to treatment.Purpose This issue has some considerable statistical complexity, and different approaches to the analysis of clinical trials may yield more interesting insights into the problem. Novel applications of statistical methods will be discussed, and examples will be used to demonstrate sub-group identification.Methods In order to evaluate many potential predictors of response, we use recursive partitioning methods to identify predictor variables and their cut-off values to define sub-groups of patients with differential treatment response. Validation of this variable/model selection approach was done using independent data from other clinical trials.Results In one example, a classification tree was developed using baseline measures to define important sub-groups of patients that responded much better than the overall mean response in the study. In a second example, a classification tree was built based on measures of response early in treatment to predict longer-term responders and nonresponders.Limitation Classification algorithms can be prone to over-fitting, and validation of results is an important consideration. Obviously, analyses are limited by the available predictor variables.Conclusions Using classification trees proved to be very useful in evaluating large numbers of potential predictors to find sub-groups of patients with exceptional response. The method is easy to use, and clinicians can easily interpret and implement results. This approach can be helpful in tailoring treatments to individual patients. Clinical Trials 2010; 7: 574-583. http://ctj.sagepub.com