Chronic Disease Prevention Research Methods and Their Reliability, With Illustrations From the Women’s Health Initiative
本文综述了慢性病预防研究的统计方法，重点讨论观察性研究和随机对照试验的可靠性，并以妇女健康倡议为例说明混杂控制和测量误差校正方法。
Abstract This article reviews the status of statistical methods for chronic disease prevention research, with emphasis on the reliability of findings and on future methodological needs and opportunities. Observational studies, especially cohort studies, play a major role in disease prevention research, but depend on adequate confounding control methods for a useful interpretation. Stratification and regression methods that are commonly used to control confounding are described, and comparative findings from the Women's Health Initiative (WHI) randomized controlled trial and companion cohort study of the benefits and risk of postmenopausal hormone therapy are used to illustrate the success of these methods. Measurement error in exposure assessment may be a potentially dominating source of bias in such important prevention research areas as nutrition and physical activity epidemiology. Statistical methods to correct for measurement error are briefly reviewed, and the need for methods to accommodate systematic bias in exposure assessment is described. Recent analysis using nutrient exposure biomarkers in WHI cohorts is used to illustrate the impact of such methods. Randomized, controlled intervention trials have the potential to obviate these biases and to reliably assess intervention benefits and risks. However, trials in healthy persons with chronic disease outcomes are typically large, long-term, and expensive. Statistical methods for randomized, controlled prevention trials are briefly reviewed, and the roles of trials in the overall chronic disease prevention research enterprise are examined. Given the logistical and cost challenges, a full-scale disease prevention trial needs to be preceded by careful hypothesis development and initial testing. The potential role of biomarkers, especially high-dimensional biomarkers, in disease prevention hypothesis development, is described and illustrated. The presentation concludes with comments on the methodological research and research infrastructure developments needed to invigorate the chronic disease prevention research agenda, with emphasis on the important role for statisticians in enhancing prevention research methods and applications. Keywords: : BiomarkerChronic disease preventionClinical trialCohort studyConfoundingMeasurement error