What's the Risk? A Simple Approach for Estimating Adjusted Risk Measures from Nonlinear Models Including Logistic Regression
What's the Risk? A Simple Approach for Estimating Adjusted Risk Measures from Nonlinear Models Including Logistic Regression
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DOI：
10.1111/j.1475-6773.2008.00900.x
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发表时间：
2009-02-01
影响因子：
3.4
通讯作者：
Norton, Edward C.
中科院分区：
文献类型：
--
作者：
Kleinman, Lawrence C.;Norton, Edward C.
开发并验证一种通用方法（称为回归风险分析），以估计logistic和其他非线性多元回归模型的调整后风险指标。我们将展示如何估计这些估计的标准误差。这些措施可以取代各种近似（例如，调整后的比值比[AOR]），可能会出现分歧，特别是当结果是常见的。回归风险分析估计值与内部标准以及Mantel-Haenszel估计值，泊松和对数二项回归进行了比较，一种广泛使用的（但有缺陷）计算调整后风险比（ARR）的公式从AOR。使用蒙特卡洛模拟产生的数据集。回归风险分析准确地估计ARR和直接从多个回归模型的差异，即使混杂因素是连续的，分布也是偏斜的，结果是常见的，效应量是大的。它是统计学上的声音和直观的，并具有属性，有利于它在许多cases.Regression风险分析的其他方法应该是新的标准，提出从多元回归分析的二分结果的横截面，队列和人口为基础的病例对照研究，特别是当结果是常见的或效应量是大的。
To develop and validate a general method (called regression risk analysis) to estimate adjusted risk measures from logistic and other nonlinear multiple regression models. We show how to estimate standard errors for these estimates. These measures could supplant various approximations (e.g., adjusted odds ratio [AOR]) that may diverge, especially when outcomes are common.Regression risk analysis estimates were compared with internal standards as well as with Mantel-Haenszel estimates, Poisson and log-binomial regressions, and a widely used (but flawed) equation to calculate adjusted risk ratios (ARR) from AOR.Data sets produced using Monte Carlo simulations.Regression risk analysis accurately estimates ARR and differences directly from multiple regression models, even when confounders are continuous, distributions are skewed, outcomes are common, and effect size is large. It is statistically sound and intuitive, and has properties favoring it over other methods in many cases.Regression risk analysis should be the new standard for presenting findings from multiple regression analysis of dichotomous outcomes for cross-sectional, cohort, and population-based case-control studies, particularly when outcomes are common or effect size is large.