Risk of Bias Assessment and Risk Minimisation Strategies in COVID-19 Diagnostic Test Accuracy Study

Abstract

It is paramount to assess the risk of biases in may arise from diagnostic test accuracy (DTA) study as it will affect the accuracy and validity of the tests. These biases can be found in published researches and here we look at COVID-19 DTA studies. The evaluation of bias risk in diagnostic research is mainly performed using QUADAS-2. The aim of this review was to determine potential selection and information biases in diagnostic test accuracy studies and strategies to minimize risk of biases. Literature review related to diagnostic test accuracy study is identified through an online search of databases namely PubMed, ScienceDirect, Research Gate, Google Scholar, and official government websites range. Six potential biases in four QUADAS-2 domains are identified in COVID-19 diagnostic test accuracy study which are 1) spectrum bias in patient selection; 2) interpretation bias in index test; 3) differential misclassification bias and nondifferential misclassification bias in reference standard; and 4) partial verification bias and differential verification bias in patient flow. The identified biases exert effects on accuracy of COVID-19 diagnostic tests. Six strategies are recommended to reduce these biases, hence, improving the accuracy of COVID-19 diagnostic tests. The best diagnostic test can give benefits to the population in the mass screening program during COVID-19.