Psaty B M, Koepsell T D, Lin D, Weiss N S, Siscovick D S, Rosendaal F R, Pahor M, Furberg C D
Department of Medicine, University of Washington, Seattle, USA.
J Am Geriatr Soc. 1999 Jun;47(6):749-54. doi: 10.1111/j.1532-5415.1999.tb01603.x.
In the evaluation of pharmacologic therapies, the controlled clinical trial is the preferred design. When clinical trial results are not available, the alternative designs are observational epidemiologic studies. A traditional concern about the validity of findings from epidemiologic studies is the possibility of bias from uncontrolled confounding. In studies of pharmacologic therapies, confounding by indication may arise when a drug treatment serves as a marker for a clinical characteristic or medical condition that triggers the use of the treatment and that, at the same time, increases the risk of the outcome under study. Confounding by indication is not conceptually different from confounding by other factors, and the approaches to detect and control for confounding--matching, stratification, restriction, and multivariate adjustment--are the same. Even after adjustment for known risk factors, residual confounding may occur because of measurement error or unmeasured or unknown risk factors. Although residual confounding is difficult to exclude in observational studies, there are limits to what this "unknown" confounding can explain. The degree of confounding depends on the prevalence of the putative confounding factor, the level of its association with the disease, and the level of its association with the exposure. For example, a confounding factor with a prevalence of 20% would have to increase the relative odds of both outcome and exposure by factors of 4 to 5 before the relative risk of 1.57 would be reduced to 1.00. Observational studies have provided important scientific evidence about the risks associated with several risk factors, including drug therapies, and they are often the only option for assessing safety. Understanding the methods to detect and control for confounding makes it possible to assess the plausibility of claims that confounding is an alternative explanation for the findings of particular studies.
在药物治疗评估中，对照临床试验是首选设计。当无法获得临床试验结果时，替代设计是观察性流行病学研究。对流行病学研究结果有效性的一个传统担忧是，不受控制的混杂因素可能导致偏差。在药物治疗研究中，当药物治疗作为一种临床特征或医疗状况的标志，而这种特征或状况既引发了治疗的使用，又同时增加了所研究结局的风险时，就可能出现指征性混杂。指征性混杂在概念上与其他因素导致的混杂并无不同，检测和控制混杂的方法——匹配、分层、限制和多变量调整——也是相同的。即使对已知风险因素进行了调整，由于测量误差或未测量或未知的风险因素，仍可能发生残余混杂。尽管在观察性研究中很难排除残余混杂，但这种“未知”混杂所能解释的内容是有限的。混杂程度取决于假定混杂因素的患病率、其与疾病的关联程度以及其与暴露的关联程度。例如，一个患病率为20%的混杂因素，在将相对风险从1.57降至1.00之前，必须将结局和暴露的相对比值分别提高4至5倍。观察性研究提供了关于包括药物治疗在内的几种风险因素相关风险的重要科学证据，而且它们往往是评估安全性的唯一选择。了解检测和控制混杂的方法，有助于评估关于混杂是特定研究结果的另一种解释这一说法的合理性。
