Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
J Clin Epidemiol. 2013 Aug;66(8):818-25. doi: 10.1016/j.jclinepi.2013.02.009. Epub 2013 May 4.
Individuals vary in their response to a treatment. Understanding this heterogeneity of treatment effect is critical for evaluating how well a treatment can be expected to work for an individual or a subgroup of individuals. An overemphasis on hypothesis testing has resulted in a dichotomy of all heterogeneity of treatment effect analyses into confirmatory (hypothesis testing) and exploratory (hypothesis finding) analyses. This limited view of heterogeneity of treatment effect is inadequate for creating evidence that is useful for informing patient-centered decisions. An expanded framework for heterogeneity of treatment effect assessment is proposed. It recognizes four distinct goals of heterogeneity of treatment effect analyses: hypothesis testing, hypothesis finding, reporting subgroup effects for meta-analysis, and individual-level prediction. Accordingly, two new types of heterogeneity of treatment effect analyses are proposed: descriptive and predictive. Descriptive heterogeneity of treatment effect analyses report treatment effects for prespecified subgroups in accordance with prospectively specified analytic strategy. They need not be powered to detect heterogeneity of treatment effect. They emphasize estimation and reporting of subgroup effects rather than hypothesis testing. Sampling properties (e.g., standard error) of descriptive analysis can be characterized, thus facilitating meta-analysis of subgroup effects. Predictive heterogeneity of treatment effect analyses estimate probabilities of beneficial and adverse responses of individuals to treatments and facilitates optimal treatment decisions for different types of individuals. Procedures are also suggested to improve reliability of heterogeneity of treatment effect assessment from observational studies. Heterogeneity of treatment effect analysis should be identified as confirmatory, descriptive, exploratory, or predictive analysis. Evidence should be interpreted in a manner consistent with the analytic goal.
个体对治疗的反应存在差异。了解这种治疗效果的异质性对于评估治疗对个体或亚组的效果如何至关重要。过度强调假设检验导致所有治疗效果异质性分析分为确证性（假设检验）和探索性（假设发现）分析。这种对治疗效果异质性的有限看法不足以提供有用的证据，以支持以患者为中心的决策。本文提出了一种扩展的治疗效果异质性评估框架。它认识到治疗效果异质性分析的四个不同目标：假设检验、假设发现、为荟萃分析报告亚组效应和个体水平预测。因此，提出了两种新的治疗效果异质性分析类型：描述性和预测性。描述性治疗效果异质性分析按照前瞻性指定的分析策略报告预先指定亚组的治疗效果。它们不需要有检测治疗效果异质性的功效。它们强调亚组效应的估计和报告，而不是假设检验。可以描述描述性分析的抽样特性（例如标准误差），从而促进亚组效应的荟萃分析。预测性治疗效果异质性分析估计个体对治疗的有益和不良反应的概率，并促进不同类型个体的最佳治疗决策。还提出了一些程序来提高来自观察性研究的治疗效果异质性评估的可靠性。治疗效果异质性分析应被确定为确证性、描述性、探索性或预测性分析。应根据分析目标的一致性来解释证据。
