利用SAS MIXED和SAS NLMIXED实现线性或非线性多水平模型的Meta分析

目的介绍利用SAS MIXED和SAS NLMIXED实现线性或非线性多水平模型的Meta分析。方法以Shim等发表的辅助手术降低宫颈癌根治性同步放化疗患者局部复发风险的系统评价结果作为实例数据,利用SAS软件实现基于线性或非线性多水平模型的Meta分析,并提供编程代码。结果在没有协变量情况下,基于双变量随机效应模型的PROC MIXED和非线性混合效应模型的PROC NLMIXED的OR合并效应值分别为0.63,95%CI(0.46,0.87),P=0.005 7]和0.60,95%CI(0.39,0.81),P=0.000 3]。在带有协变量情况下,双变量随机效应模型和非线性混合效应模型OR效应值为0.65,95%CI(0.47,0.91),P=0.011]和0.59,95%CI(0.38,0.80),P=0.000 3]。协变量OR效应值分别为2.70,95%CI(0.16,45.23),P>0.05]和1.86,95%CI(-0.07,3.79),P=0.06]。结论利用SAS NLMIXED非线性混合效应模型拟合的Meta分析结果与SAS MIXED线性混合效应模型的Meta分析结果相似,鉴于PROC NLMIXED具有强大的编程能力及非线性混合效应模型对稀疏数据具有灵活的建模能力,PROC NLMIXED在Meta分析领域将发挥越来越重要的作用。

Implementing meta-analysis based on linear or nonlinear multi-level models using SAS MIXED and SAS NLMIXED

Objective To introduce a meta-analysis of linear or nonlinear multilevel models using SAS MIXED and SAS NLMIXED. Methods A systematic review performed to evaluate the risk of local recurrence in patients with cervical cancer treated with radical chemoradiotherapy and adjuvant surgery published by Shim et al. was selected as an illustration. An SAS software was used to implement meta-analysis based on linear or nonlinear multi-level models, and programming codes were provided. Results In the absence of covariates, the OR combined effect values of PROC MIXED based on the bivariate random effects model and PROC NLMIXED of the nonlinear mixed effects model were0.63(95%CI 0.46 to 0.87, P=0.005 7) and 0.60(95%CI 0.39 to 0.81, P=0.000 3), respectively. In the case of covariates, the bivariate random effects model and the nonlinear mixed effects model provided an effect value of OR=0.65(95%CI 0.47 to0.91, P=0.011) and 0.59(95%CI 0.38 to 0.80, P=0.000 3). Covariate OR effect values were 2.70(95%CI 0.16 to 45.23,P>0.05) and 1.86(95%CI-0.07 to 3.79, P=0.06). Conclusions The meta-analysis results of the SAS NLMIXED nonlinear mixed-effects model are similar to those of the SAS MIXED linear mixed-effects model. PROC NLMIXED has powerful programming capability and nonlinear mixed-effects model has flexible modeling capabilities for sparse data. Therefore,PROC NLMIXED will play an increasingly important role in meta-analysis.