An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies.

Background: A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ).

An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data.

Background: Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies.

Accuracy of the Patient Health Questionnaire-9 for screening to detect major depression: updated systematic review and individual participant data meta-analysis.

Objective: To update a previous individual participant data meta-analysis and determine the accuracy of the Patient Health Questionnaire-9 (PHQ-9), the most commonly used depression screening tool in general practice, for detecting major depression overall and by study or participant subgroups.

Design: Systematic review and individual participant data meta-analysis.

Data Sources: Medline, Medline In-Process, and Other Non-Indexed Citations via Ovid, PsycINFO, Web of Science searched through 9 May 2018.

Validation of the COVID-19 Fears Questionnaires for Chronic Medical Conditions: A Scleroderma Patient-centered Intervention Network COVID-19 Cohort study.

Objective: Fear associated with medical vulnerability should be considered when assessing mental health among individuals with chronic medical conditions during the COVID-19 pandemic. The objective was to develop and validate the COVID-19 Fears Questionnaire for Chronic Medical Conditions.

Methods: Fifteen initial items were generated based on suggestions from 121 people with the chronic autoimmune disease systemic sclerosis (SSc; scleroderma).

Robust bivariate random-effects model for accommodating outlying and influential studies in meta-analysis of diagnostic test accuracy studies.

Due to the inevitable inter-study correlation between test sensitivity (Se) and test specificity (Sp), mostly because of threshold variability, hierarchical or bivariate random-effects models are widely used to perform a meta-analysis of diagnostic test accuracy studies. Conventionally, these models assume that the random-effects follow the bivariate normal distribution. However, the inference made using the well-established bivariate random-effects models, when outlying and influential studies are present, may lead to misleading conclusions, since outlying or influential studies can extremely influence parameter estimates due to their disproportional weight.

Skew-normal random-effects model for meta-analysis of diagnostic test accuracy (DTA) studies.

Hierarchical models are recommended for meta-analyzing diagnostic test accuracy (DTA) studies. The bivariate random-effects model is currently widely used to synthesize a pair of test sensitivity and specificity using logit transformation across studies. This model assumes a bivariate normal distribution for the random-effects.

Statistical methods for detecting outlying and influential studies in meta-analysis of diagnostic test accuracy studies.

Bivariate random-effects models are currently widely used to synthesize pairs of test sensitivity and specificity across studies. Inferences drawn based on these models may be distorted in the presence of outlying or influential studies. Currently, subjective methods such as inspection of forest plots are used to identify outlying studies in meta-analysis of diagnostic test accuracy studies.

Bivariate random-effects meta-analysis models for diagnostic test accuracy studies using arcsine-based transformations.

Diagnostic or screening tests are widely used in medical fields to classify patients according to their disease status. Several statistical models for meta-analysis of diagnostic test accuracy studies have been developed to synthesize test sensitivity and specificity of a diagnostic test of interest. Because of the correlation between test sensitivity and specificity, modeling the two measures using a bivariate model is recommended.

Galactomannan, β-D-Glucan, and Polymerase Chain Reaction-Based Assays for the Diagnosis of Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation: A Systematic Review and Meta-Analysis.

We systematically reviewed and analyzed the available data for galactomannan (GM), β-D-glucan (BG), and polymerase chain reaction (PCR)-based assays to detect invasive fungal disease (IFD) in patients with pediatric cancer or undergoing hematopoietic stem cell transplantation when used as screening tools during immunosuppression or as diagnostic tests in patients presenting with symptoms such as fever during neutropenia (FN). Of 1532 studies screened, 25 studies reported on GM (n = 19), BG (n = 3), and PCR (n = 11). All fungal biomarkers demonstrated highly variable sensitivity, specificity, and positive predictive values, and these were generally poor in both clinical settings.