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首页> 外文期刊>Journal of applied statistics >Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies
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Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies

机译：测量和估计二分结果暴露之间的相互作用，以进行观察性研究

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摘要

In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.
机译：在针对特定人群二分结果的暴露之间的相互作用的观察性研究中，通常使用回归模型的一个参数来描述相互作用，从而得出相互作用的一种度量。在本文中，我们使用给定暴露和协变量的结果的条件风险来描述相互作用，并获得相互作用的五种不同度量，即边际风险差异之间的差异，边际风险比率之比，边际优势比之比比率，条件风险比率的比率和条件优势比率的比率。这些措施反映了互动的不同方面。通过仅将一个回归模型用于条件风险，我们获得了基于最大似然（ML）的点和区间估计值，这些估计值由于ML的性质而最为有效。我们使用模型参数的ML估计来获得这些度量的ML估计。我们使用模型参数的ML估计值的近似正态分布来获取这些度量的ML估计值的近似非正态分布，然后获得这些度量的置信区间。该方法可以容易地实现并且通过医学实例来呈现。

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