Drivers of bias in diagnostic test accuracy estimates when using expert panels as a reference standard: a simulation...

Expert panels are often used as a reference standard when no gold standard is available in diagnostic test accuracy research. It is often unclear what study and expert panel characteristics produce the best estimates of diagnostic test accuracy. We simulated a large range of scenarios to assess the impact of study and expert panel characteristics on index test diagnostic accuracy estimates.

Simulations were performed in which an expert panel was the reference standard to estimate the sensitivity and specificity of an index diagnostic test. Diagnostic accuracy was determined by combining probability estimates of target condition presence, provided by experts using four component reference tests, through a predefined threshold. Study and panel characteristics were varied in several scenarios: target condition prevalence, accuracy of component reference tests, expert panel size, study population size, and random or systematic differences between expert's probability estimates. The total bias in each scenario was quantified using mean squared error.

When estimating an index test with 80% sensitivity and 70% specificity, bias in estimates was hardly affected by the study population size or the number of experts. Prevalence had a large effect on bias, scenarios with a prevalence of 0.5 estimated sensitivity between 63.3% and 76.7% and specificity between 56.1% and 68.7%, whereas scenarios with a prevalence of 0.2 estimated sensitivity between 48.5% and 73.3% and specificity between 65.5% and 68.7%. Improved reference tests also reduced bias. Scenarios with four component tests of 80% sensitivity and specificity estimated index test sensitivity between 60.1% and 77.4% and specificity between 62.9% and 69.1%, whereas scenarios with four component tests of 70% sensitivity and specificity estimated index test sensitivity between 48.5% and 73.4% and specificity between 56.1% and 67.0%.

Bias in accuracy estimates when using an expert panel will increase if the component reference tests are less accurate. Prevalence, the true value of the index test accuracy, and random or systematic differences between experts can also impact the amount of bias, but the amount and even direction will vary between scenarios.