Department of Biostatistics, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
Division of Biostatistics, Malankara Orthodox Syrian Church Medical College, Kolenchery, Ernakulam, Kerala, India.
J Res Health Sci. 2022 Dec;22(4):e00561. doi: 10.34172/jrhs.2022.96.
Traditional meta-analyses often assess the effectiveness of different doses of the same intervention separately or examine the overall differences between intervention and placebo groups. The present study aimed to model the effect sizes obtained from different doses in multiple studies using a two-stage dose-response meta-analytic approach while taking dose variations into account.
Different dose-response meta-analysis models using linear, quadratic, and restricted cubic spline (RCS) functions were fitted. A two-stage approach utilizing multivariate meta-analysis was performed and the obtained results were compared with those of the univariate meta-analysis. A random effect dose-response meta-analysis was performed using data from an existing systematic review on combination therapy with zonisamide and anti-Parkinson drugs for Parkinson's disease. The effective or optimum dose for producing maximum response was also investigated. Moreover, a sensitivity analysis was performed by changing the knots of the RCS model.
Dose-response meta-analysis was performed using data from four double-blinded randomized controlled trials with 724 and 309 patients with Parkinson's disease in dose and placebo arms, respectively. The quadratic model yielded the smallest Akaike information criterion (AIC), compared to the linear and RCS models, indicating it to be the best fit for the data.
Compared to the traditional approach, the two-stage approach could model the dose-dependent effect of zonisamide on the Unified Parkinson's Disease Rating Scale (UPRDS) part III score and predict the outcome for different doses through a single analysis.
传统的荟萃分析通常分别评估同一干预措施不同剂量的效果，或者检验干预组与安慰剂组之间的总体差异。本研究旨在使用两阶段剂量反应荟萃分析方法，在考虑剂量变化的情况下，对多项研究中获得的不同剂量的效应大小进行建模。
使用线性、二次和限制性三次样条（RCS）函数拟合了不同的剂量反应荟萃分析模型。采用多元荟萃分析的两阶段方法进行分析，并将得到的结果与单变量荟萃分析的结果进行比较。利用一项关于佐尼沙胺与抗帕金森药物联合治疗帕金森病的系统评价中现有的数据，进行了随机效应剂量反应荟萃分析。还研究了产生最大反应的有效或最佳剂量。此外，还通过改变 RCS 模型的节点进行了敏感性分析。
对来自四项双盲随机对照试验的数据进行了剂量反应荟萃分析，这些试验分别有 724 名和 309 名帕金森病患者在剂量组和安慰剂组。与线性和 RCS 模型相比，二次模型产生了最小的 Akaike 信息准则（AIC），表明它最适合该数据。
与传统方法相比，两阶段方法可以对佐尼沙胺对统一帕金森病评定量表（UPDRS）第三部分评分的剂量依赖性效应进行建模，并通过单次分析预测不同剂量的结果。
