Department of Biostatistics School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Department of Biostatistics School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
BMC Med Res Methodol. 2021 Jul 28;21(1):154. doi: 10.1186/s12874-021-01325-7.
Collinearity is a common and problematic phenomenon in studies on public health. It leads to inflation in variance of estimator and reduces test power. This phenomenon can occur in any model. In this study, a new ridge mixed-effects logistic model (RMELM) is proposed to overcome consequences of collinearity in correlated binary responses.
Parameters were estimated through penalized log-likelihood with combining expectation maximization (EM) algorithm, gradient ascent, and Fisher-scoring methods. A simulation study was performed to compare new model with mixed-effects logistic model(MELM). Mean square error, relative bias, empirical power, and variance of random effects were used to evaluate RMELM. Also, contribution of various types of violence, and intervention on depression among pregnant women experiencing intimate partner violence(IPV) were analyzed by new and previous models.
Simulation study showed that mean square errors of fixed effects were decreased for RMELM than MELM and empirical power were increased. Inflation in variance of estimators due to collinearity was clearly shown in the MELM in data on IPV and RMELM adjusted the variances.
According to simulation results and analyzing IPV data, this new estimator is appropriate to deal with collinearity problems in the modelling of correlated binary responses.
共线性是公共卫生研究中常见且棘手的问题。它会导致估计量方差膨胀，并降低检验效能。这种现象可能出现在任何模型中。本研究提出了一种新的岭混合效应逻辑回归模型（RMELM），以克服相关二分类反应中存在的共线性的后果。
通过结合期望最大化（EM）算法、梯度上升和 Fisher 评分法的惩罚对数似然来估计参数。通过模拟研究，比较了新模型与混合效应逻辑回归模型（MELM）。均方误差、相对偏差、经验效能和随机效应方差用于评估 RMELM。此外，还通过新模型和旧模型分析了各种类型的暴力以及干预措施对经历亲密伴侣暴力（IPV）的孕妇的抑郁的影响。
模拟研究表明，RMELM 的固定效应均方误差小于 MELM，经验效能增加。在关于 IPV 的 MELM 数据中，共线性导致估计量方差膨胀，而 RMELM 调整了方差。
根据模拟结果和分析 IPV 数据，这个新的估计器适合处理相关二分类反应模型中的共线性问题。
