Field experience with the 8-HPV-type oncoprotein test for cervical cancer screening among HPV-positive women living with and without HIV in LMICs.

Overexpression of HPV-oncoproteins E6 and E7 is necessary for HPV-driven cervical carcinogenesis. Hence, these oncoproteins are promising disease-specific biomarkers. We assessed the technical and operational characteristics of the 8-HPV-type OncoE6/E7 Cervical Test in different laboratories using cervical samples from HPV-positive women living with (WLWH) and without HIV. The 8-HPV-type OncoE6/E7 Test (for short: "OncoE6/E7 test") was performed in 2833 HIV-negative women and 241 WLWH attending multicentric studies in Latin America (ESTAMPA study), and in Africa (CESTA study). Oncoprotein positivity were evaluated at each testing site, according to HIV status as well as type-specific agreement with HPV-DNA results. A feedback questionnaire was given to the operators performing the oncoprotein test to evaluate their impression and acceptability regarding the test. The OncoE6/E7 test revealed a high positivity rate heterogeneity across all testing sites (I2: 95.8%, p < .01) with significant lower positivity in WLWH compared to HIV-negative women (12% vs 25%, p < .01). A similar HPV-type distribution was found between HPV DNA genotyping and oncoprotein testing except for HPV31 and 33 (moderate agreement, k = 0.57). Twenty-one laboratory technicians were trained on oncoprotein testing. Despite operators' concerns about the time-consuming procedure and perceived need for moderate laboratory experience, they reported the OncoE6/E7 test as easy to perform and user-friendly for deployment in resource-limited settings. The high positivity rate variability found across studies and subjectivity in test outcome interpretation could potentially results in oncoprotein false positive/negative, and thus the need for further refinements before implementation of the oncoprotein testing in screen-triage-and-treat approaches is warranted.

Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg.

BACKGROUND: Despite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network meta-analysis, and meta-regression of proportions. Nonetheless, generic methods for meta-analysis of proportions based on the approximation to the normal distribution continue to dominate. METHODS: We developed metapreg, a tool with advanced statistical procedures to perform a meta-analysis, network meta-analysis, and meta-regression of binomial proportions in Stata using binomial, logistic and logistic-normal models. First, we explain the rationale and concepts essential in understanding statistical methods for meta-analysis of binomial proportions and describe the models implemented in metapreg. We then describe and demonstrate the models in metapreg using data from seven published meta-analyses. We also conducted a simulation study to compare the performance of metapreg estimators with the existing estimators of the population-averaged proportion in metaprop and metan under a broad range of conditions including, high over-dispersion and small meta-analysis. CONCLUSION: metapreg is a flexible, robust and user-friendly tool employing a rigorous approach to evidence synthesis of binomial data that makes the most efficient use of all available data and does not require ad-hoc continuity correction or data imputation. We expect its use to yield higher-quality meta-analysis of binomial proportions.

Diagnostic value of α-synuclein seeding amplification assays in α-synucleinopathies: A systematic review and meta-analysis.

INTRODUCTION: Alpha-synuclein(αSyn) aggregates are definite pathological hallmarks of α-synucleinopathies. Seeding amplification assays (SAAs) have been developed to detect trace amounts of αSyn oligomers in vivo.. Herein, we assessed the diagnostic accuracy of the αSyn-SAAs across biospecimens, diagnostic references, methods, and subtypes. METHODS: A systematic literature search yielded 36 eligible studies for a meta-analysis of the sensitivity and specificity of αSyn-SAAs in patients with α-synucleinopathies(n = 2722) and controls(n = 2278). Pooled sensitivities and specificities with 95% confidence intervals (CIs) were calculated using bivariate random-effects models and a meta-regression analysis was performed. RESULTS: The summary sensitivity and specificity of αSyn-SAAs positivity for the diagnosis of α-synucleinopathies were 0.88(95% CIs = 0.84-0.91) and 0.95(0.93-0.97), respectively. Two covariates (biospecimen and diagnostic reference) were significant in fitting the meta-regression model (likelihood-ratio test for sensitivity and specificity, p < 0.01, p = 0.01, respectively). Skin αSyn-SAAs exhibited the highest sensitivity 0.92(0.87-0.95), which was not different from that of cerebrospinal fluid (CSF)(0.90(0.86-0.93), p = 0.39). Olfactory mucosa αSyn-SAAs exhibited a lower sensitivity 0.64(0.49-0.76) than those of the other two specimens(p = 0.02, 0.01, compared to CSF and skin, respectively). Application of pathological diagnostic standards were associated with a higher specificity of αSyn-SAAs compared to clinical diagnosis (p < 0.01). The diagnostic sensitivity and specificity of CSF αSyn-SAAs were 0.91(0.87-0.94) and 0.96(0.93-0.98) for Lewy body disease, 0.90(0.79-0.95) and 0.96(0.90-0.98) for prodromal α-synucleinopathies, and 0.63(0.24-0.90) and 0.97(0.93-0.99) for multiple system atrophy. CONCLUSIONS: αSyn-SAAs are promising in vivo detectors of abnormal αSyn aggregates and may aid the early diagnosis of α-synucleinopathies.

Metadta: a Stata command for meta-analysis and meta-regression of diagnostic test accuracy data - a tutorial.

BACKGROUND: Although statistical procedures for pooling of several epidemiological metrics are generally available in statistical packages, those for meta-analysis of diagnostic test accuracy studies including options for multivariate regression are lacking. Fitting regression models and the processing of the estimates often entails lengthy and tedious calculations. Therefore, packaging appropriate statistical procedures in a robust and user-friendly program is of great interest to the scientific community. METHODS: metadta is a statistical program for pooling of diagnostic accuracy test data in Stata. It implements both the bivariate random-effects and the fixed-effects model, allows for meta-regression, and presents the results in tables, a forest plot and/or summary receiver operating characteristic (SROC) plot. For a model without covariates, it quantifies the unexplained heterogeneity due to between-study variation using an I2 statistic that accounts for the mean-variance relationship and the correlation between sensitivity and specificity. To demonstrate metadta, we applied the program on two published meta-analyses on: 1) the sensitivity and specificity of cytology and other markers including telomerase for primary diagnosis of bladder cancer, and 2) the accuracy of human papillomavirus (HPV) testing on self-collected versus clinician-collected samples to detect cervical precancer. RESULTS: Without requiring a continuity correction, the pooled sensitivity and specificity generated by metadta of telomerase for the diagnosis of primary bladder cancer was 0.77 [95% CI, 0.70, 0.82] and 0.91 [95% CI, 0.75, 0.97] respectively. Metadta also allowed to assess the relative accuracy of HPV testing on self- versus clinician-taken specimens using data from comparative studies conducted in different clinical settings. The analysis showed that HPV testing with target-amplification assays on self-samples was as sensitive as on clinician-samples in detecting cervical pre-cancer irrespective of the clinical setting. CONCLUSION: The metadta program implements state of art statistical procedures in an attempt to close the gap between methodological statisticians and systematic reviewers. We expect the program to popularize the use of appropriate statistical methods for diagnostic meta-analysis further.