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个体参与者数据荟萃分析中预测模型异质性的评估:概述和实例。

Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration.

机构信息

Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.

出版信息

Stat Med. 2019 Sep 30;38(22):4290-4309. doi: 10.1002/sim.8296. Epub 2019 Aug 2.

Abstract

Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.

摘要

临床预测模型旨在提供诊断或预后终点的绝对风险估计。此类模型可以从元分析背景下的各种研究数据中推导出来。我们描述并提出了用于评估基于来自不同来源的数据的模型中预测因子效应和预测的异质性的方法。这些方法在一个创伤性脑损伤患者的病例研究中得到了说明,我们旨在使用荟萃分析技术(15 项研究,n = 11022 名患者)基于个体患者数据来预测 6 个月死亡率。深入了解异质性的各个方面对于开发更好的模型和理解绝对风险预测的可转移性问题很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3911/6772012/d3f46d00bb59/SIM-38-4290-g001.jpg

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