Cad Saude Publica. 2014 Jan;30(1):21-9. doi: 10.1590/0102-311x00077313.
Recent studies have emphasized that there is no justification for using the odds ratio (OR) as an approximation of the relative risk (RR) or prevalence ratio (PR). Erroneous interpretations of the OR as RR or PR must be avoided, as several studies have shown that the OR is not a good approximation for these measures when the outcome is common (> 10%). For multinomial outcomes it is usual to use the multinomial logistic regression. In this context, there are no studies showing the impact of the approximation of the OR in the estimates of RR or PR. This study aimed to present and discuss alternative methods to multinomial logistic regression based upon robust Poisson regression and the log-binomial model. The approaches were compared by simulating various possible scenarios. The results showed that the proposed models have more precise and accurate estimates for the RR or PR than the multinomial logistic regression, as in the case of the binary outcome. Thus also for multinomial outcomes the OR must not be used as an approximation of the RR or PR, since this may lead to incorrect conclusions.
最近的研究强调，没有理由将优势比（OR）用作相对风险（RR）或患病率比（PR）的近似值。必须避免将 OR 错误地解释为 RR 或 PR，因为多项研究表明，当结局常见（>10%）时，OR 不是这些指标的良好近似值。对于多项结局，通常使用多项逻辑回归。在这种情况下，没有研究表明 OR 近似值对 RR 或 PR 的估计值的影响。本研究旨在介绍和讨论基于稳健泊松回归和对数二项式模型的替代多项逻辑回归方法。通过模拟各种可能的情况对这些方法进行了比较。结果表明，与多项逻辑回归相比，所提出的模型对 RR 或 PR 的估计更精确和准确，就像二项结局的情况一样。因此，对于多项结局，也不能将 OR 用作 RR 或 PR 的近似值，因为这可能导致错误的结论。
