Wyss Richard, Lunt Mark, Brookhart M Alan, Glynn Robert J, Stürmer Til
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.
Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.
J Causal Inference. 2014 Sep 1;2(2):131-146. doi: 10.1515/jci-2014-0009.
The prognostic score, or disease risk score (DRS), is a summary score that is used to control for confounding in non-experimental studies. While the DRS has been shown to effectively control for measured confounders, unmeasured confounding continues to be a fundamental obstacle in non-experimental research. Both theory and simulations have shown that in the presence of unmeasured confounding, controlling for variables that affect treatment (both instrumental variables and measured confounders) amplifies the bias caused by unmeasured confounders. In this paper, we use causal diagrams and path analysis to review and illustrate the process of bias amplification. We show that traditional estimation strategies for the DRS do not avoid bias amplification when controlling for predictors of treatment. We then discuss estimation strategies for the DRS that can potentially reduce bias amplification that is caused by controlling both instrumental variables and measured confounders. We show that under certain assumptions, estimating the DRS in populations outside the defined study cohort where treatment has not been introduced, or in outside populations with reduced treatment prevalence can control for the confounding effects of measured confounders while at the same time reduce bias amplification.
预后评分，即疾病风险评分（DRS），是一种汇总评分，用于在非实验性研究中控制混杂因素。虽然DRS已被证明能有效控制已测量的混杂因素，但未测量的混杂因素仍然是非实验性研究中的一个基本障碍。理论和模拟均表明，在存在未测量的混杂因素的情况下，控制影响治疗的变量（包括工具变量和已测量的混杂因素）会放大由未测量的混杂因素导致的偏差。在本文中，我们使用因果图和路径分析来回顾和说明偏差放大的过程。我们表明，当控制治疗预测因素时，DRS的传统估计策略无法避免偏差放大。然后，我们讨论了DRS的估计策略，这些策略可能会减少因同时控制工具变量和已测量的混杂因素而导致的偏差放大。我们表明，在某些假设下，在尚未引入治疗的定义研究队列之外的人群中，或在治疗患病率较低的外部人群中估计DRS，可以控制已测量混杂因素的混杂效应，同时减少偏差放大。
