分析常见结果的横断面研究的替代方法：卫生保健专业人员指南

IF 1.9 Q2 MEDICINE, GENERAL & INTERNAL

Journal of Taibah University Medical Sciences Pub Date : 2025-08-01 DOI:10.1016/j.jtumed.2025.07.014

Mohammad H. Aljawadi BPharm, PharmD, MSc, PhD

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引用次数: 0

摘要

目的：本研究以高血压患病率为例，比较了logistic、泊松和对数二项回归模型在具有共同结果的横断面研究中的患病率（pr）估计。目的是确定最可靠的方法，并在结果患病率高时减少误解。方法对哈立德国王大学医院的2022例患者病历进行横断面分析。高血压是主要结局，阿司匹林使用暴露和糖尿病（DM）是混杂因素。统计模型包括Mantel-Haenszel患病率（MHPR，参考）、logistic回归、泊松回归（带或不带标准误差修正）和对数二项回归。将MHPR与pr和95%置信区间（ci）进行比较，并使用百分比变化来量化偏差。在stata17中进行分析。结果数据集包括43,789例患者。高血压患病率高（44.7%），阿司匹林使用率为38.6%，糖尿病使用率为52.3%。逻辑回归产生了夸大的估计，未经调整的OR为4.26，而MHPR为2.11。在调整DM后，OR下降到3.78，但相对于MHPR仍然高估了110%的相关性。泊松模型相对于调整后的MHPR偏差最小（高0.67%），而对数二项模型相对于零值的偏差低2.28%。逻辑回归的置信区间比MHPR宽得多（高出3.74%），而泊松模型的估计ci较窄，鲁棒性和折刀程序的差异很小（高出0.04%）。结论logistic回归在具有共同结局的横截面数据中引入了较大的偏倚。泊松回归提供了更准确的估计，特别是鲁棒或折刀标准误差，而对数二项回归是有效的，但容易出现收敛问题。具有稳健标准误差或折刀标准误差的泊松回归是首选，以产生可靠的PR估计，同时避免在健康研究中产生误解。

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Alternative methods for analyzing cross-sectional studies of common outcomes: A guide for healthcare professionals

Objectives

This study compared logistic, Poisson, and log-binomial regression models for estimating prevalence ratios (PRs) in cross-sectional studies with common outcomes, using hypertension prevalence as an applied example. The objective was to identify the most reliable method and reduce misinterpretation when outcome prevalence is high.

Methods

A cross-sectional analysis was conducted on 2022 patient records from King Khalid University Hospital. Hypertension was the primary outcome, aspirin use the exposure, and diabetes mellitus (DM) the confounder. Statistical models included the Mantel–Haenszel prevalence ratio (MHPR, reference), logistic regression, Poisson regression with or without standard error corrections, and log-binomial regression. The MHPR was compared with PRs and 95% confidence intervals (CIs), and percentage changes were used to quantify deviations. Analyses were performed in STATA 17.

Results

The dataset included 43,789 patients. Hypertension prevalence was high (44.7%), aspirin use was reported in 38.6%, and DM in 52.3%. Logistic regression produced inflated estimates, with an unadjusted OR of 4.26 versus MHPR 2.11. After adjusting for DM, the OR declined to 3.78 but still overestimated the association by 110% relative to the MHPR. The Poisson model had the smallest deviation with respect to the adjusted MHPR (0.67% higher), whereas the log-binomial model showed a 2.28% lower value toward the null. Logistic regression yielded a much wider confidence interval (3.74% higher) than the MHPR, whereas Poisson models showed narrower estimated CIs, and the robust and jackknife procedures resulted in minimal differences (0.04% higher).

Conclusion

Logistic regression introduced substantial bias in cross-sectional data with common outcomes. Poisson regression provided more accurate estimates, particularly with robust or jackknife standard errors, while log-binomial regression was valid but prone to convergence issues. Poisson regression with robust standard errors or jackknife standard errors is preferred, to produce reliable PR estimation while avoiding misinterpretation in health research.

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