基于g-computation联合混合效应模型控制未测混杂因素的因果推断方法模拟研究及实例验证

目的通过模拟实验和实例验证探讨基于g-computation的联合混合效应模型(joint mixed-effects model,JMM)控制纵向研究未测混杂因素进行因果推断时的效果及性能特点。方法通过计算机模拟产生包含基线及两次随访时点的纵向数据,模拟条件包括样本含量、有无未测混杂因素及未测混杂效应大小,分别利用基于g-computation的JMM、线性混合效应模型、固定效应模型和纵向目标极大似然估计方法估计因果效应,通过平均绝对偏差(mean absolute deviation,MAD)、标准误、均方根误差(root mean square error,RMSE)、95%置信区间覆盖率(95%confidence interval coverage,95%CI coverage)评价比较各方法因果推断的效果。利用绝经期女性队列体检数据,应用四类模型分别估计绝经期女性血清卵泡刺激素(follicle-stimulating hormone,FSH)水平与腰椎骨密度间因果关系,对各模型在真实纵向数据中的因果推断效果进行验证。结果JMM控制未测混杂因素的因果推断准确性最佳,但稳定性略差。当研究中存在较强未测混杂效应时,仅JMM可准确估计因果效应,且其在大样本量时估计的精确性和真实性较好。结论基于g-computation的JMM可有效控制纵向研究中未测混杂因素进行近似无偏因果推断。

Correlation between Combined Urinary Metal Exposure and Grip Strength under Three Statistical Models:A Cross-sectional Study in Rural Guangxi

Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.

Association between exposure to a mixture of organochlorine pesticides and hyperuricemia in U.S.adults:A comparison of four statistical models

The association between the exposure of organochlorine pesticides(OCPs)and serum uric acid(UA)levels remained uncertain.In this study,to investigate the combined effects of OCP mixtures on hyperuricemia,we analyzed serum OCPs and UA levels in adults from the National Health and Nutrition Examination Survey(2005–2016).Four statistical models including weighted logistic regression,weighted quantile sum(WQS),quantile g-computation(QGC),and bayesian kernel machine regression(BKMR)were used to assess the relationship between mixed chemical exposures and hyperuricemia.Subgroup analyses were conducted to explore potential modifiers.Among 6,529 participants,the prevalence of hyperuricemia was 21.15%.Logistic regression revealed a significant association between both hexachlorobenzene(HCB)and trans-nonachlor and hyperuricemia in the fifth quintile(OR:1.54,95%CI:1.08–2.19;OR:1.58,95%CI:1.05–2.39,respectively),utilizing the first quintile as a reference.WQS and QGC analyses showed significant overall effects of OCPs on hyperuricemia,with an OR of 1.25(95%CI:1.09–1.44)and 1.20(95%CI:1.06–1.37),respectively.BKMR indicated a positive trend between mixed OCPs and hyperuricemia,with HCB having the largest weight in all three mixture analyses.Subgroup analyses revealed that females,individuals aged 50 years and above,and those with a low income were more vulnerable to mixed OCP exposure.These results highlight the urgent need to protect vulnerable populations from OCPs and to properly evaluate the health effects of multiple exposures on hyperuricemia using mutual validation approaches.

Robust variance estimation for covariate-adjusted unconditional treatment effect in randomized clinical trials with binary outcomes

To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g computation as a reliable method of covariate adjustment.How-ever,the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest.To fill this gap,we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.