ObjectiveTo observe whether multipoint target muscle injection of botulinum toxin type A (BTX-A) in the treatment of spastic cerebral palsy in children is better than non-multipoint target muscle injection. MethodsFrom February to October 2013, 42 children with spastic cerebral palsy were treated in our hospital. According to the treatment sequence, the children were numbered. Those with an odd number were designated into multipoint target muscle injection group (group A), and those with an even number were put into non-multipoint target muscle ordinary injection group (group B). Each group had 21 children, and all of them were treated with the injection of BTX-A. Modified Ashworth Scoring (MAS) was performed for all the children before treatment, and 2 weeks, one month, and three months after treatment. The change of dorsiflexion range of motion with knee flexion and extension was recorded and compared. The analysis was done by using multilevel statistical method. ResultsBoth groups of children had significantly improved their ankle range and modified Ashworth score (P<0.05). No interaction between measurement time and group was detected, and the differences between the two groups had no statistical significance (P>0.05). ConclusionLower muscle tone, greater ankle mobility and better motor function can be achieved after Botulinum toxin A treatment. For now, we cannot draw the conclusion that the effect of multipoint target muscle injection is better than that of non-multipoint target muscle injection in the treatment of spastic cerebral palsy in children.
Longitudinal data had intrinsic correlation problems at different time points, and traditional meta-analysis techniques cannot resolve this problem. Regression coefficients based on multi-level models can fully consider the correlations of longitudinal data at various time points. This paper uses SAS software to perform multi-level regression coefficient model meta-analysis and provides programming code which is simple and easy to operate.
ObjectiveTo introduce a meta-analysis of linear or nonlinear multilevel models using SAS MIXED and SAS NLMIXED.MethodsA 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.ResultsIn 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 were 0.63 (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 to 0.91, 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).ConclusionsThe 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.
With the establishment and development of regional healthcare big data platforms, regional healthcare big data is playing an increasingly important role in health policy program evaluations. Regional healthcare big data is usually structured hierarchically. Traditional statistical models have limitations in analyzing hierarchical data, and multilevel models are powerful statistical analysis tools for processing hierarchical data. This method has frequently been used by healthcare researchers overseas, however, it lacks application in China. This paper aimed to introduce the multilevel model and several common application scenarios in medicine policy evaluations. We expected to provide a methodological framework for medicine policy evaluation using regional healthcare big data or hierarchical data.
Interrupted time series (ITS) analysis is a quasi-experimental design for evaluating the effectiveness of health interventions. By controlling the time trend before the intervention, ITS is often used to estimate the level change and slope change after the intervention. However, the traditional ITS modeling strategy might indicate aggregation bias when the data was collected from different clusters. This study introduced two advanced ITS methods of handling hierarchical data to provide the methodology framework for population-level health intervention evaluation.