Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Health Management and Social Development Research Center, Golestan University of Medical Sciences, Gorgān, Iran.
Sci Rep. 2022 Sep 1;12(1):14819. doi: 10.1038/s41598-022-19241-1.
The multivariate marginal model can be used to simultaneously examine the factors affecting both FBS and HbA1c using longitudinal data. The model fitted to multivariate longitudinal data should prevent redundant parameter estimation in order to have greater efficiency. In this study, a multivariate marginal model is used to simultaneously investigate the factors affecting both FBS and HbA1c with longitudinal data for patients with type 2 diabetes in Northern Iran. The present research is a retrospective cohort study. Overall, 500 medical records with complete information were reviewed. The multivariate marginal model is used to determine the factors associated with FBS and HbA1c using longitudinal data. Data have been analyzed in R-3.4.0 using 'mmm2' package. Given that the coefficients for the interactions of rtype with the intercept, time, family history of diabetes, history of hypertension, history of smoking, insulin therapy, systolic/diastolic blood pressure and duration of disease at first visit are significantly different from zero (P < 0.05), the effect of the independent variables on the two response variables is different and different coefficients should be used for each. Therefore, the interactions of these variables with rtype are kept in the final model. The coefficients for the interactions of rtype with sex, age at first visit, history of high cholesterol, and weight are not significantly different from zero (P > 0.05), indicating that their effect on the two response variables is similar and only one coefficient should be used for each. We examined the similarity of coefficients when fitting the longitudinal multivariate model for the relationship between FBS/HbA1c and sex, age, history of high blood cholesterol, and body weight. If an independent variable has similar effects on both responses, only one coefficient should be estimated, which will increase the efficiency of the model and the reliability of the results.
多元边际模型可用于使用纵向数据同时研究影响 FBS 和 HbA1c 的因素。为了提高效率，拟合多元纵向数据的模型应防止冗余参数估计。本研究使用多元边际模型，对伊朗北部 2 型糖尿病患者的纵向数据同时研究影响 FBS 和 HbA1c 的因素。本研究为回顾性队列研究。共回顾了 500 份信息完整的病历。使用多元边际模型，使用纵向数据确定与 FBS 和 HbA1c 相关的因素。数据分析在 R-3.4.0 中使用“mmm2”包进行。由于 rtype 与截距、时间、糖尿病家族史、高血压史、吸烟史、胰岛素治疗、收缩压/舒张压和首次就诊时疾病持续时间的交互作用的系数显著不同于零（P<0.05），独立变量对两个响应变量的影响不同，应该为每个变量使用不同的系数。因此，将这些变量与 rtype 的交互作用保留在最终模型中。rtype 与性别、首次就诊时的年龄、高胆固醇史和体重的交互作用的系数与零没有显著差异（P>0.05），这表明它们对两个响应变量的影响相似，每个变量只需使用一个系数。我们检查了拟合 FBS/HbA1c 与性别、年龄、高胆固醇血症史和体重的纵向多元模型时系数的相似性。如果一个独立变量对两个响应的影响相似，则只需估计一个系数，这将提高模型的效率和结果的可靠性。
