A Bivariate Finite Mixture Random Effects Model for Identifying and Accommodating Outliers in Diagnostic Test Accuracy Meta-Analyses

IF 1.8 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Zelalem F. Negeri
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Abstract

Outlying studies are prevalent in meta-analyses of diagnostic test accuracy studies and may lead to misleading inferences and decision-making unless their negative effect is appropriately dealt with. Statistical methods for detecting and down-weighting the impact of such studies have recently gained the attention of many researchers. However, these methods dichotomize each study in the meta-analysis as outlying or non-outlying and focus on examining the effect of outlying studies on the summary sensitivity and specificity only. We developed and evaluated a robust and flexible random-effects bivariate finite mixture model for meta-analyzing diagnostic test accuracy studies. The proposed model accounts for both the within- and across-study heterogeneity in diagnostic test results, generates the probability that each study in a meta-analysis is outlying instead of dichotomizing the status of the studies, and allows assessing the impact of outlying studies on the pooled sensitivity, pooled specificity, and between-study heterogeneity. Our simulation study and real-life data examples demonstrated that the proposed model was robust to the existence of outlying studies, produced precise point and interval estimates of the pooled sensitivity and specificity, and yielded similar results to the standard models when there were no outliers. Extensive simulations demonstrated relatively better bias and confidence interval width, but comparable root mean squared error and lesser coverage probability of the proposed model. Practitioners can use our proposed model as a stand-alone model to conduct a meta-analysis of diagnostic test accuracy studies or as an alternative sensitivity analysis model when outlying studies are present in a meta-analysis.

Abstract Image

诊断测试准确性荟萃分析中识别和容纳异常值的二元有限混合随机效应模型
离群研究在诊断测试准确性研究的荟萃分析中普遍存在,除非其负面影响得到适当处理,否则可能导致误导性推论和决策。用于检测和降低此类研究影响的统计方法最近引起了许多研究人员的注意。然而,这些方法将meta分析中的每项研究分为离群研究和非离群研究,并且只关注离群研究对总体敏感性和特异性的影响。我们开发并评估了一个稳健和灵活的随机效应双变量有限混合模型,用于荟萃分析诊断测试准确性研究。所提出的模型考虑了诊断测试结果中的研究内部和研究间异质性,产生了meta分析中每个研究是孤立的概率,而不是对研究的状态进行二分类,并允许评估孤立研究对合并敏感性、合并特异性和研究间异质性的影响。我们的模拟研究和实际数据示例表明,所提出的模型对离群研究的存在具有鲁棒性,对合并敏感性和特异性产生了精确的点和区间估计,并且在没有离群值的情况下产生了与标准模型相似的结果。广泛的模拟表明,该模型的偏差和置信区间宽度相对较好,但均方根误差相当,覆盖概率较小。从业者可以使用我们提出的模型作为独立模型来进行诊断测试准确性研究的荟萃分析,或者在荟萃分析中存在离群研究时作为替代敏感性分析模型。
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来源期刊
Biometrical Journal
Biometrical Journal 生物-数学与计算生物学
CiteScore
3.20
自引率
5.90%
发文量
119
审稿时长
6-12 weeks
期刊介绍: Biometrical Journal publishes papers on statistical methods and their applications in life sciences including medicine, environmental sciences and agriculture. Methodological developments should be motivated by an interesting and relevant problem from these areas. Ideally the manuscript should include a description of the problem and a section detailing the application of the new methodology to the problem. Case studies, review articles and letters to the editors are also welcome. Papers containing only extensive mathematical theory are not suitable for publication in Biometrical Journal.
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