Article type
Year
Abstract
Background: systematic reviews include primary studies that differ in sample sizes, with larger studies contributing more to the meta-analysis. At present, study size is not considered in 'Risk of bias' evaluations.
Objectives: to develop an alternative way to present the contribution of individual studies to the total body of evidence on diagnostic accuracy, in terms of risk of bias and concerns about applicability, one that takes the effective sample size into account.
Methods: we used the results of a systematic review of diagnostic accuracy studies of the Enhanced Liver Fibrosis (ELF) test for diagnosing liver fibrosis among people with non-alcoholic fatty liver disease. We assessed the 11 studies identified from our systematic search of five databases with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. We first used number of studies to show the proportion of studies at low, unclear and high risk of bias. We then developed an alternative version of the graph, which relies on the proportion of the total sample size of studies at different levels of risk of bias.
Results: the risk of bias levels for each domain of the QUADAS-2 checklist changed after replacing the number of studies with the relative sample sizes of the individual studies. For instance, the risk of bias was high in the patient selection domain in 45% of the studies, and low and unclear in 27% of studies (Figure 1). The alternative graph using the sample sizes showed 25%, 41% and 34% of included population with high risk, unclear and low risk of bias, respectively (Figure 2).
Conclusions: a fair representation of the risk-of-bias and concerns about applicability in the available body of evidence from diagnostic accuracy studies should be based on the total sample size, not on the number of studies.
Patient or healthcare consumer involvement: systematic reviews of diagnostic test accuracy studies (DTA) are important medical decision-making tools for patient health improvement and reliable quality assessment of primary DTA studies can inform the decisions regarding the suitability and applicability of their meta-analysis.
Objectives: to develop an alternative way to present the contribution of individual studies to the total body of evidence on diagnostic accuracy, in terms of risk of bias and concerns about applicability, one that takes the effective sample size into account.
Methods: we used the results of a systematic review of diagnostic accuracy studies of the Enhanced Liver Fibrosis (ELF) test for diagnosing liver fibrosis among people with non-alcoholic fatty liver disease. We assessed the 11 studies identified from our systematic search of five databases with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. We first used number of studies to show the proportion of studies at low, unclear and high risk of bias. We then developed an alternative version of the graph, which relies on the proportion of the total sample size of studies at different levels of risk of bias.
Results: the risk of bias levels for each domain of the QUADAS-2 checklist changed after replacing the number of studies with the relative sample sizes of the individual studies. For instance, the risk of bias was high in the patient selection domain in 45% of the studies, and low and unclear in 27% of studies (Figure 1). The alternative graph using the sample sizes showed 25%, 41% and 34% of included population with high risk, unclear and low risk of bias, respectively (Figure 2).
Conclusions: a fair representation of the risk-of-bias and concerns about applicability in the available body of evidence from diagnostic accuracy studies should be based on the total sample size, not on the number of studies.
Patient or healthcare consumer involvement: systematic reviews of diagnostic test accuracy studies (DTA) are important medical decision-making tools for patient health improvement and reliable quality assessment of primary DTA studies can inform the decisions regarding the suitability and applicability of their meta-analysis.