Martina R, Kay R, van Maanen R, Ridder A
Department of Health Sciences, University of Leicester, Leicester, UK.
Pharm Stat. 2015 Mar-Apr;14(2):151-60. doi: 10.1002/pst.1664. Epub 2014 Dec 18.
Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well.
传统上，膀胱过度活动症的临床研究使用协方差分析或非参数方法来分析尿失禁发作次数及其他计数数据。众所周知，如果特定参数方法的潜在分布假设不成立，那么一种替代参数方法可能比非参数方法更有效，因为非参数方法对数据的潜在分布不作任何假设。因此，使用基于泊松分布或该方法扩展的方法具有优势，这些方法纳入了为计数数据提供建模框架的特定特征。计数数据面临的一个挑战是过度离散，但可以通过在建模中引入随机效应项来解决这个问题的方法是可用的，正是这种建模框架导致了负二项分布。这些模型还可以为临床医生提供关于率比方面治疗效果更清晰、更合适的解释。在本文中，在评估索利那新和米拉贝隆治疗膀胱过度活动症患者的试验中，将先前使用的参数和非参数方法与基于泊松回归及各种扩展的方法进行了对比。在这些应用中，负二项模型被证明能很好地拟合数据。
