Université Clermont Auvergne, CHU de Clermont-Ferrand, Inserm, Neuro-Dol, Clermont-Ferrand, France.
Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France/Hospices Civils de Lyon, Hôpital Neurologique, Service de Neurologie, Sclérose en Plaques, Pathologies de la Myéline et Neuro-inflammation, Bron, France/Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, Lyon, France/EUGENE DEVIC EDMUS Foundation Against Multiple Sclerosis, state-approved foundation, Bron, France.
Mult Scler. 2022 Aug;28(9):1457-1466. doi: 10.1177/13524585211052400. Epub 2021 Oct 26.
In multiple sclerosis (MS) studies, the most appropriate model for the distribution of the number of relapses was shown to be the negative binomial (NB) distribution.
To determine whether the sample-size estimation (SSE) and the analysis of annualized relapse rates (ARRs) in randomized controlled trials (RCTs) were aligned and compare the SSE between normal and NB distributions.
Systematic review of phase 3 and 4 RCTs for which the primary endpoint was ARR in relapsing remitting MS published since 2008 in pre-selected major medical journals. A PubMed search was performed on 30 November 2020. We checked whether the SSE and ARR analyses were congruent. We also performed standardized (fixed α/β, number of arms and overdispersion) SSEs using data collected from the studies.
Twenty articles (22 studies) were selected. NB distribution (or quasi-Poisson) was used for SSE in only 7/22 studies, whereas 21/22 used it for ARR analyses. SSE relying on NB regression necessitated a smaller sample size in 21/22 of our calculations.
SSE was rarely performed using the most appropriate model. However, the use of an NB model is recommended to optimize the number of included patients and to be congruent with the final analysis.
在多发性硬化症(MS)研究中,复发次数的分布最适合的模型是负二项式(NB)分布。
确定随机对照试验(RCT)中的样本量估计(SSE)和年化复发率(ARR)分析是否一致,并比较正态和 NB 分布之间的 SSE。
对 2008 年以来在选定的主要医学期刊上发表的 3 期和 4 期主要终点为 RRMS 复发 ARR 的 RCT 进行系统性综述。于 2020 年 11 月 30 日在 PubMed 上进行检索。我们检查了 SSE 和 ARR 分析是否一致。我们还使用从研究中收集的数据进行了标准化(固定α/β、臂数和过离散度)的 SSE。
选择了 20 篇文章(22 项研究)。只有 7/22 项研究使用 NB 分布(或拟泊松分布)进行 SSE,而 21/22 项研究则使用 NB 分布进行 ARR 分析。在我们的计算中,21/22 的研究中,基于 NB 回归的 SSE 需要的样本量更小。
很少有研究使用最合适的模型进行 SSE。但是,建议使用 NB 模型来优化纳入患者的数量,并与最终分析保持一致。