Ely J W, Dawson J D, Mehr D R, Burns T L
Department of Family Practice, University of Iowa Hospitals and Clinics, Iowa City, USA.
Fam Med. 1996 Feb;28(2):134-40; discussion 141-3.
Logistic regression is a valuable statistical tool that is often used in primary care research. When researchers explore the association between a possible risk factor and a disease, they attempt to control the effects of extraneous factors (confounders) that can obscure the true association. Using logistic regression, researchers can simultaneously control for the effects of multiple confounders. When investigators use logistic regression, they make subjective decisions about which factors to include in the analysis and in the final predictive model. Critical readers must understand basic concepts of logistic regression and potential problems with its use before they can accurately interpret study results. This article uses a familiar example to explain the principles of logistic regression to make it understandable to nonstatisticians.
逻辑回归是一种有价值的统计工具，常用于初级保健研究。当研究人员探索一个可能的风险因素与一种疾病之间的关联时，他们试图控制那些可能掩盖真实关联的外部因素（混杂因素）的影响。使用逻辑回归，研究人员可以同时控制多个混杂因素的影响。当研究者使用逻辑回归时，他们会对分析以及最终预测模型中应纳入哪些因素做出主观决策。批判性的读者在准确解释研究结果之前，必须了解逻辑回归的基本概念及其使用中可能存在的问题。本文通过一个常见的例子来解释逻辑回归的原理，以便非统计专业人员能够理解。
