Valeri Linda, Vanderweele Tyler J
Department of Biostatistics and Epidemiology, Harvard School of Public Health, 655 Huntington avenue, Boston, MA 02115, USA
Department of Biostatistics and Epidemiology, Harvard School of Public Health, 655 Huntington avenue, Boston, MA 02115, USA.
Biostatistics. 2014 Jul;15(3):498-512. doi: 10.1093/biostatistics/kxu007. Epub 2014 Mar 26.
Mediation analysis serves to quantify the effect of an exposure on an outcome mediated by a certain intermediate and to quantify the extent to which the effect is direct. When the mediator is misclassified, the validity of mediation analysis can be severely undermined. The contribution of the present work is to study the effects of non-differential misclassification of a binary mediator in the estimation of direct and indirect causal effects when the outcome is either continuous or binary and exposure-mediator interaction can be present, and to allow the correction of misclassification. A hybrid of likelihood-based and predictive value weighting method for misclassification correction coupled with sensitivity analysis is proposed and a second approach using the expectation-maximization algorithm is developed. The correction strategy requires knowledge of a plausible range of sensitivity and specificity parameters. The approaches are applied to a perinatal epidemiological study of the determinants of pre-term birth.
中介分析用于量化暴露因素通过某种中间变量对结局的影响，并量化该影响的直接程度。当中介变量被错误分类时，中介分析的有效性可能会受到严重影响。本研究的贡献在于，当结局为连续型或二分类变量且可能存在暴露-中介变量交互作用时，研究二分类中介变量的非差异性错误分类对直接和间接因果效应估计的影响，并对错误分类进行校正。提出了一种基于似然性和预测值加权的混合方法用于错误分类校正，并结合敏感性分析，同时开发了第二种使用期望最大化算法的方法。校正策略需要了解敏感性和特异性参数的合理范围。这些方法应用于一项关于早产决定因素的围产期流行病学研究。
