断点回归设计在临床治疗性研究中的应用

The application of regression discontinuity design in clinical therapeutic research

随机对照试验的证据等级较高,但因其实施成本较高、外部真实性较低和伦理要求等原因而限制了其在临床开展和应用,而传统的观察性研究由于存在各种混杂因素导致内部真实性降低,从而降低了证据等级。断点回归设计(regression discontinuity design,RDD)是在自然条件下观察比较阈值附近的人群,其控制混杂的能力仅次于随机对照试验,可提供较高等级证据。它适用于干预(或暴露)与某连续变量的数值直接相关的情况,如HIV患者是否需要抗逆转录病毒治疗主要取决于其CD4细胞计数是否低于200/μL。因为连续变量的测量存在随机误差,在阈值附近是否给予干预是接近随机的,阈值附近干预和非干预组患者基线应该是均衡可比的。根据这一假设,比较阈值附近人群的结局,即可估计干预(或暴露)与结局的因果效应。RDD在医学中主要适用于分类结局的研究,其中两阶段最小二乘法、基于似然比的估计方法、贝叶斯方法是较常用的模型估计方法。然而,RDD的适用条件和对样本量的要求限制了其在医学中的广泛应用,随着数据可及性的提高和真实世界研究的发展,RDD将更多地应用于临床研究中。

The level of evidence in randomized controlled studies is high, but it cannot be widely applied due to its high cost, external authenticity, ethics and other reasons. The traditional observational studies reduce the internal authenticity due to various confounding factors, and the level of evidence is low. Regression discontinuity design （RDD） is a design that observes and compares outcome of object around the threshold under practical clinical conditions. Its ability to adjust confounding factors is second only to that of randomized control studies. It can be used in cases where the intervention （or exposure） is directly related to the value of a continuous variable, for example, whether an HIV patient needs antiretroviral treatment mainly depends on whether the CD4 cell count is lower than 200/laL. Because the measurement of continuous variables has random error, whether intervention is given near the threshold is close to random, and the baseline of patients in the intervention group and non-intervention group near the threshold should be balanced and comparable. Based on this assumption, the causal effect of intervention （or exposure） and outcome can be estimated by comparing the outcomes of populations near the threshold. RDD is mainly applicable to the study of classification outcomes in medicine, among which two-stage least square method, likelihood ratio based estimation method and Bayesian method are more commonly used model estimation methods. However, the application conditions of RDD and the requirement of sample size limit its extensive application in medicine. With the improvement of data accessibility and the development of real world research, RDD will be more widely used in clinical research.