Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
BMC Med Res Methodol. 2016 Aug 2;16:91. doi: 10.1186/s12874-016-0189-0.
Meta-analytical methods are frequently used to combine dose-response findings expressed in terms of relative risks. However, no methodology has been established when results are summarized in terms of differences in means of quantitative outcomes.
We proposed a two-stage approach. A flexible dose-response model is estimated within each study (first stage) taking into account the covariance of the data points (mean differences, standardized mean differences). Parameters describing the study-specific curves are then combined using a multivariate random-effects model (second stage) to address heterogeneity across studies.
The method is fairly general and can accommodate a variety of parametric functions. Compared to traditional non-linear models (e.g. E max, logistic), spline models do not assume any pre-specified dose-response curve. Spline models allow inclusion of studies with a small number of dose levels, and almost any shape, even non monotonic ones, can be estimated using only two parameters. We illustrated the method using dose-response data arising from five clinical trials on an antipsychotic drug, aripiprazole, and improvement in symptoms in shizoaffective patients. Using the Positive and Negative Syndrome Scale (PANSS), pooled results indicated a non-linear association with the maximum change in mean PANSS score equal to 10.40 (95 % confidence interval 7.48, 13.30) observed for 19.32 mg/day of aripiprazole. No substantial change in PANSS score was observed above this value. An estimated dose of 10.43 mg/day was found to produce 80 % of the maximum predicted response.
The described approach should be adopted to combine correlated differences in means of quantitative outcomes arising from multiple studies. Sensitivity analysis can be a useful tool to assess the robustness of the overall dose-response curve to different modelling strategies. A user-friendly R package has been developed to facilitate applications by practitioners.
荟萃分析方法常用于合并以相对风险表示的剂量-反应发现。然而,当以定量结局的均数差值来总结结果时,尚未建立相应的方法学。
我们提出了一种两阶段方法。在每个研究中(第一阶段),考虑数据点的协方差(均数差值,标准化均数差值),估计一个灵活的剂量-反应模型。然后使用多变量随机效应模型(第二阶段)来组合描述各研究曲线的参数,以解决研究间的异质性。
该方法相当通用,可以容纳各种参数函数。与传统的非线性模型(例如 E max 、逻辑)相比,样条模型不假设任何预先指定的剂量-反应曲线。样条模型允许纳入剂量水平较少的研究,并且仅使用两个参数就可以估计几乎任何形状的曲线,甚至是非单调的曲线。我们使用来自五项抗精神病药阿立哌唑治疗精神分裂症谱系障碍患者的症状改善的临床试验的剂量-反应数据说明了该方法。使用阳性和阴性症状量表(PANSS),汇总结果表明与最大平均 PANSS 评分变化呈非线性关联,19.32mg/日阿立哌唑的平均 PANSS 评分最大变化为 10.40(95 %置信区间 7.48 ,13.30)。在该值以上,PANSS 评分没有明显变化。发现 10.43mg/日的估计剂量可产生 80 %的最大预测反应。
应采用描述的方法来合并多个研究中定量结局的相关均数差值。敏感性分析可以作为一种有用的工具,评估总体剂量-反应曲线对不同建模策略的稳健性。已经开发了一个用户友好的 R 包,以方便从业者的应用。