Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
Sci Rep. 2023 Jun 7;13(1):9275. doi: 10.1038/s41598-023-36129-w.
The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings.
筛检工具的诊断准确性通常以其敏感度和特异度来描述。分析这些指标时必须考虑其内在关联性。在个体参与者数据荟萃分析中，异质性是分析的主要组成部分之一。使用随机效应荟萃分析模型时，预测区间可更深入地了解异质性对整个研究人群中估计准确性指标变异性的影响，而不仅仅是平均值。本研究旨在通过个体参与者数据荟萃分析，探讨患者健康问卷-9 用于筛检检测重性抑郁的敏感度和特异度的预测区间中的异质性。从汇总研究中选择了四项研究日期，这些日期包含了大约 25%、50%、75%和 100%的总参与者数量。拟合至包含每项日期的研究，采用双变量随机效应模型联合估计敏感度和特异度。在 ROC 空间中绘制二维预测区间。对性别和年龄进行亚组分析，而不论研究日期如何。该数据集包含 58 项初步研究的 17436 名参与者，其中 2322 名（13.3%）患有重性抑郁症。随着更多的研究被纳入模型，敏感度和特异度的点估计值没有显著差异。然而，这些指标的相关性增加了。正如预期的那样，随着使用的研究越来越多，汇总对数 TPR 和 FPR 的标准误差始终会降低，而随机效应的标准差不会单调降低。按性别进行的亚组分析并未发现观察到的异质性有重要贡献；然而，预测区间的形状不同。按年龄进行的亚组分析并未对异质性产生有意义的贡献，且预测区间的形状相似。预测区间和区域揭示了数据集中以前未见过的趋势。在诊断测试准确性荟萃分析的背景下，预测区间可以显示不同人群和环境下的准确性指标范围。
