Prevalence proportion ratios: estimation and hypothesis testing
Prevalence proportion ratios: estimation and hypothesis testing
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DOI：
10.1093/ije/27.1.91
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发表时间：
1998-02-01
影响因子：
7.7
通讯作者：
Endahl, L
中科院分区：
文献类型：
--
作者：
Skov, T;Deddens, J;Endahl, L
背景最近的研究表明，通常情况下，用Logistic回归模型分析流行结果的横断面研究可能不合适。本文旨在比较三种应用于横断面研究的方法：(1)乘性广义线性模型，我们称之为对数二项模型；(2)基于Logistic回归和标准误差稳健估计的方法，我们称之为Gee-Logistic模型；(3)Cox回归模型。方法使用代表14种不同模拟条件的5组模拟来检验方法的性能。结果三种模型都产生了接近真实参数的点估计，即与暴露相关的参数的估计值具有可忽略的偏差。COX回归产生的标准误差太大，特别是当疾病的患病率很高时，而对数二项模型和Gee-Logistic模型具有正确的I型错误概率。实例表明，Gee-Logistic模型可以产生大于1的流行率，而对数二项模型不能产生大于1的流行率。应首选对数二项式模型。
Background Recent communications have argued that often it may not be appropriate to analyse cross-sectional studies of prevalent outcomes with logistic regression models. The purpose of this communication is to compare three methods that have been proposed for application to cross sectional studies: (1) a multiplicative generalized linear model, which we will call the log-binomial model, (2) a method based on logistic regression and robust estimation of standard errors, which we will call the GEE-logistic model, and (3) a Cox regression model.Methods Five sets of simulations representing fourteen separate simulation conditions were used to test the performance of the methods.Results All three models produced point estimates close to the true parameter, i.e. the estimators of the parameter associated with exposure had negligible bias. The Cox regression produced standard errors that were too large, especially when the prevalence of the disease war high, whereas the log-binomial model and the GEE-logistic model had the correct type I error probabilities. It was shown by example that the GEE-logistic model could produce prevalences greater than one, whereas it was proven that this could not happen with the log-binomial model. The log-binomial model should be preferred.