Coutinho Leticia M S, Scazufca Marcia, Menezes Paulo R
Departamento de Medicina Preventiva, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brasil.
Rev Saude Publica. 2008 Dec;42(6):992-8.
To empirically compare the Cox, log-binomial, Poisson and logistic regressions to obtain estimates of prevalence ratios (PR) in cross-sectional studies.
Data from a population-based cross-sectional epidemiological study (n = 2072) on elderly people in Sao Paulo (Southeastern Brazil), conducted between May 2003 and April 2005, were used. Diagnoses of dementia, possible cases of common mental disorders and self-rated poor health were chosen as outcomes with low, intermediate and high prevalence, respectively. Confounding variables with two or more categories or continuous values were used. Reference values for point and interval estimates of prevalence ratio (PR) were obtained by means of the Mantel-Haenszel stratification method. Adjusted PR estimates were calculated using Cox and Poisson regressions with robust variance, and using log-binomial regression. Crude and adjusted odds ratios (ORs) were obtained using logistic regression.
The point and interval estimates obtained using Cox and Poisson regressions were very similar to those obtained using Mantel-Haenszel stratification, independent of the outcome prevalence and the covariates in the model. The log-binomial model presented convergence difficulties when the outcome had high prevalence and there was a continuous covariate in the model. Logistic regression produced point and interval estimates that were higher than those obtained using the other methods, particularly when for outcomes with high initial prevalence. If interpreted as PR estimates, the ORs would overestimate the associations for outcomes with low, intermediate and high prevalence by 13%, almost by 100% and fourfold, respectively.
In analyses of data from cross-sectional studies, the Cox and Poisson models with robust variance are better alternatives than logistic regression is. The log-binomial regression model produces unbiased PR estimates, but may present convergence difficulties when the outcome is very prevalent and the confounding variable is continuous.
通过实证比较Cox回归、对数二项式回归、泊松回归和逻辑回归，以获得横断面研究中患病率比（PR）的估计值。
使用了2003年5月至2005年4月在巴西东南部圣保罗市进行的一项基于人群的横断面流行病学研究（n = 2072）的数据。分别选择痴呆症诊断、常见精神障碍可能病例和自评健康状况差作为低、中、高患病率的结局。使用具有两个或更多类别或连续值的混杂变量。通过Mantel-Haenszel分层法获得患病率比（PR）的点估计值和区间估计值的参考值。使用具有稳健方差的Cox回归和泊松回归以及对数二项式回归计算调整后的PR估计值。使用逻辑回归获得粗比值比（OR）和调整后的比值比。
使用Cox回归和泊松回归获得的点估计值和区间估计值与使用Mantel-Haenszel分层法获得的估计值非常相似，与结局患病率和模型中的协变量无关。当结局患病率高且模型中有连续协变量时，对数二项式模型存在收敛困难。逻辑回归产生的点估计值和区间估计值高于使用其他方法获得的估计值，特别是对于初始患病率高的结局。如果将OR解释为PR估计值，则对于低、中、高患病率的结局，OR分别高估关联13%、近100%和四倍。
在横断面研究数据分析中，具有稳健方差的Cox模型和泊松模型比逻辑回归是更好的选择。对数二项式回归模型产生无偏的PR估计值，但当结局非常普遍且混杂变量为连续变量时可能存在收敛困难。
