MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard

BACKGROUND

The statistical models developed for meta-analysis of diagnostic test accuracy studies require specialised knowledge to implement. This is especially true since recent guidelines, such as those in Version 2 of the Cochrane Handbook of Systematic Reviews of Diagnostic Test Accuracy, advocate more sophisticated methods than previously. This paper describes a web-based application - MetaBayesDTA - that makes many advanced analysis methods in this area more accessible.

RESULTS

We created the app using R, the Shiny package and Stan. It allows for a broad array of analyses based on the bivariate model including extensions for subgroup analysis, meta-regression and comparative test accuracy evaluation. It also conducts analyses not assuming a perfect reference standard, including allowing for the use of different reference tests.

CONCLUSIONS

Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to researchers with varying levels of expertise. We anticipate that the application will encourage higher levels of uptake of more advanced methods, which ultimately should improve the quality of test accuracy reviews.

Model-based clustering for assessing the prognostic value of imaging biomarkers and mixed type tests

A model-based clustering method is proposed to address two research aims in Alzheimer's disease (AD): to evaluate the accuracy of imaging biomarkers in AD prognosis, and to integrate biomarker information and standard clinical test results into the diagnoses. One challenge in such biomarker studies is that it is often desired or necessary to conduct the evaluation without relying on clinical diagnoses or some other standard references. This is because (1) biomarkers may provide prognostic information long before any standard reference can be acquired; (2) these references are often based on or provide unfair advantage to standard tests. Therefore, they can mask the prognostic value of a useful biomarker, especially when the biomarker is much more accurate than the standard tests. In addition, the biomarkers and existing tests may be of mixed type and vastly different distributions. A model-based clustering method based on finite mixture modeling framework is introduced. The model allows for the inclusion of mixed typed manifest variables with possible differential covariates to evaluate the prognostic value of biomarkers in addition to standard tests without relying on potentially inaccurate reference diagnoses. Maximum likelihood parameter estimation is carried out via the EM algorithm. Accuracy measures and the ROC curves of the biomarkers are derived subsequently. Finally, the method is illustrated with a real example in AD.

Assessing validity of a depression screening instrument in the absence of a gold standard

PURPOSE

We evaluated the extent to which use of a hypothesized imperfect gold standard, the Composite International Diagnostic Interview (CIDI), biases the estimates of diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9). We also evaluate how statistical correction can be used to address this bias.

METHODS

The study was conducted among 926 adults where structured interviews were conducted to collect information about participants' current major depressive disorder using PHQ-9 and CIDI instruments. First, we evaluated the relative psychometric properties of PHQ-9 using CIDI as a gold standard. Next, we used a Bayesian latent class model to correct for the bias.

RESULTS

In comparison with CIDI, the relative sensitivity and specificity of the PHQ-9 for detecting major depressive disorder at a cut point of 10 or more were 53.1% (95% confidence interval: 45.4%-60.8%) and 77.5% (95% confidence interval, 74.5%-80.5%), respectively. Using a Bayesian latent class model to correct for the bias arising from the use of an imperfect gold standard increased the sensitivity and specificity of PHQ-9 to 79.8% (95% Bayesian credible interval, 64.9%-90.8%) and 79.1% (95% Bayesian credible interval, 74.7%-83.7%), respectively.

CONCLUSIONS

Our results provided evidence that assessing diagnostic validity of mental health screening instrument, where application of a gold standard might not be available, can be accomplished by using appropriate statistical methods.