Statistical Methods of Meta-Analysis for Count Data with Rare Events

罕见事件计数数据荟萃分析的统计方法

• 批准号: 430210250
• 负责人: Professor Dr. Heinz Holling
• 金额: --
• 依托单位: Arbeitseinheit Statistik und Methoden
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/430210250?language=en
• 关键词: Statistical; Methods; Meta; Analysis; Count

Typical effect measures in meta-analysis of count data are the risk ratio, the odds ratio and risk difference. Meta-analysis of effect measures of count data proceeds as follows. The effect measure is calculated for each component study (partly on the log-scale) accompanied by an estimate of the associated variance. It is then assumed that an approximate normality holds and the tools of meta-analysis are then applied as if these where actually arising from a normal distribution. This approach may be justifiable if both sizes, sample and event sizes, of the component studies are large. However, this becomes evidently flawed in the case of rare events, in the extreme case of no events at all, where some effect measures become undefined and all variance estimates undefined or meaningless. Introduction of smoothing constants will help to avoid undefined estimates but introduce bias instead. Hence, the main theme of the project is to use approaches that are appropriate for the count character of the data and where rare events including zero events are causing not only no problem but are an integral part of the event scale. The model classes considered are the mixed Poisson (for the risk ratio and risk difference) and the mixed logistic regression (for the odds ratio). Here, the study effect is treated as a normally distributed random effect, which is mixed with the appropriate count distribution, the Poisson (for the risk ratio and risk difference) and binomial (for the odds ratio). For the risk ratio and risk difference a particular interesting approach is considered. Using the fact that the conditional distribution of the counts in the experimental group, conditional on the margin of the events, is a binomial distribution, only the parameter of interest are contained as the baseline (control group) parameter get eliminated. Hence, inference can focus on the parameter of interest alone. In these models, the heterogeneity variance, which is the major parameter of interest, is provided as variance of the random effects distribution. This approach will be compared with conventional approaches such as DerSimonian-Laird, REML among others. As an alternative to the conventional chi-square heterogeneity test, a likelihood ratio test is investigated which tests the null hypothesis of homogeneity by setting the variance of the random effects distribution to zero. The mixed Poisson and logistic regression approach will be further generalized to allow inclusion of covariates on study level. In addition, the problem of missing values in one of the groups under comparison will be approached in the mixed effects modelling. Detailed simulation work will investigate and compare the various approaches and, finally, R-packages will be provided which contain the proposed methodology.

计数数据荟萃分析中典型的效应度量是风险比、优势比和风险差。对计数数据的效应测度进行meta分析。对每个组成部分的研究（部分在对数尺度上）计算效果度量，并对相关方差进行估计。然后假设近似正态分布成立，然后应用元分析工具，就好像这些实际上是由正态分布产生的一样。如果组成部分研究的样本和事件规模都很大，这种方法可能是合理的。然而，在罕见事件的情况下，在根本没有事件的极端情况下，一些效果度量变得不确定，所有方差估计都不确定或无意义，这就变得明显存在缺陷。引入平滑常数将有助于避免未定义的估计，但会引入偏差。因此，该项目的主题是使用适合数据计数特征的方法，其中包括零事件在内的罕见事件不仅不会造成问题，而且是事件规模的组成部分。考虑的模型类别是混合泊松（用于风险比和风险差）和混合逻辑回归（用于优势比）。在这里，研究效应被视为一种正态分布的随机效应，混合了适当的计数分布、泊松分布（风险比和风险差）和二项分布（优势比）。对于风险比和风险差，考虑了一种特别有趣的方法。利用实验组中计数的条件分布是二项分布的事实，在事件的边缘条件下，只包含感兴趣的参数作为基线（对照组）参数被消除。因此，推理可以只关注感兴趣的参数。在这些模型中，异质性方差作为随机效应分布的方差，是我们感兴趣的主要参数。该方法将与传统方法（如dersimonan - laird、REML等）进行比较。作为传统卡方异质性检验的替代方法，研究了似然比检验，该检验通过将随机效应分布的方差设置为零来检验齐性的零假设。混合泊松和逻辑回归方法将进一步推广，以允许在研究水平上包含协变量。此外，在混合效应建模中，将探讨比较组中某一组的缺失值问题。详细的模拟工作将调查和比较各种方法，最后，将提供包含所建议方法的r包。