用逻辑斯谛回归模型计算横断面研究中的校正患病率
Obtaining adjusted prevalence ratios from logistic regression model in cross-sectional studies
摘要：
在过去的几十年中，它已经讨论了使用流行病学患病率（PR），而不是比值比作为衡量关联估计的横断面研究。使用统计模型计算PR的主要困难是收敛问题、适当工具的可用性和强有力的假设。本研究的目的是说明如何估计PR及其置信区间直接从逻辑斯谛回归估计。我们提出了三个例子，并比较调整后的估计PR的估计使用对数二项式，稳健泊松回归和调整后的患病率比值比（POR）。从逻辑斯谛回归估计的边际和条件患病率比显示出以下优点：没有数值不稳定性;易于在统计软件中实现;并假设结果具有足够的概率分布。
英文摘要：
In the last decades, it has been discussed the use of epidemiological prevalence ratio (PR) rather than odds ratio as a measure of association to be estimated in cross-sectional studies. The main difficulties in use of statistical models for the calculation of PR are convergence problems, availability of adequate tools and strong assumptions. The goal of this study is to illustrate how to estimate PR and its confidence interval directly from logistic regression estimates. We present three examples and compare the adjusted estimates of PR with the estimates obtained by use of log-binomial, robust Poisson regression and adjusted prevalence odds ratio (POR). The marginal and conditional prevalence ratios estimated from logistic regression showed the following advantages: no numerical instability; simple to implement in a statistical software; and assumes the adequate probability distribution for the outcome.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
