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职业队列数据的比例风险、泊松和逻辑回归建模的实证比较。

Empirical comparisons of proportional hazards, poisson, and logistic regression modeling of occupational cohort data.

作者信息

Callas P W, Pastides H, Hosmer D W

机构信息

Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, USA.

出版信息

Am J Ind Med. 1998 Jan;33(1):33-47. doi: 10.1002/(sici)1097-0274(199801)33:1<33::aid-ajim5>3.0.co;2-x.

DOI:10.1002/(sici)1097-0274(199801)33:1<33::aid-ajim5>3.0.co;2-x
PMID:9408527
Abstract

This research was conducted to examine the effect of model choice on the epidemiologic interpretation of occupational cohort data. Three multiplicative models commonly employed in the analysis of occupational cohort studies--proportional hazards. Poisson, and logistic regression--were used to analyze data from an historical cohort study of workers exposed to formaldehyde. Samples were taken from this dataset to create a number of predetermined scenarios for comparing the models, varying study size, outcome frequency, strength of risk factors, and follow-up length. The Poisson and proportional hazards models yielded nearly identical relative risk estimates and confidence intervals in all situations except when confounding by age could not be closely controlled in the Poisson analysis. Logistic regression findings were more variable, with risk estimates differing most from the proportional hazards results when there was a common outcome or strong relative risk. The logistic model also provided less precise estimates than the other two. Thus, although logistic was the easiest model to implement, it should be used only in occupational cohort studies when the outcome is rare (5% or less), and the relative risk is less than approximately 2. Even then, the proportional hazards and Poisson models are better choices. Selecting between these two can be based on convenience in most circumstances.

摘要

本研究旨在探讨模型选择对职业队列数据的流行病学解释的影响。在职业队列研究分析中常用的三种乘法模型——比例风险模型、泊松模型和逻辑回归模型——被用于分析一项针对甲醛暴露工人的历史性队列研究的数据。从该数据集中抽取样本,以创建一些预定场景,用于比较不同研究规模、结局频率、危险因素强度和随访时长下的模型。除了在泊松分析中无法严格控制年龄混杂因素的情况外,泊松模型和比例风险模型在所有情况下得出的相对风险估计值和置信区间几乎相同。逻辑回归的结果更具变异性,当存在共同结局或强相对风险时,风险估计值与比例风险结果差异最大。逻辑模型提供的估计也比其他两个模型更不精确。因此,尽管逻辑回归模型是最容易实施的模型,但仅应在结局罕见(5%或更低)且相对风险小于约2的职业队列研究中使用。即便如此,比例风险模型和泊松模型仍是更好的选择。在大多数情况下,可以根据便利性在这两者之间进行选择。

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