scholarly journals Copas-like selection model to correct publication bias in systematic review of diagnostic test studies

2018 ◽  
Vol 28 (10-11) ◽  
pp. 2912-2923 ◽  
Author(s):  
Jin Piao ◽  
Yulun Liu ◽  
Yong Chen ◽  
Jing Ning
Keyword(s):  
Publication Bias ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Selection Model ◽  
Medical Decision ◽  
Finite Sample ◽  
Bivariate Outcomes ◽  
Sensitivity Specificity

The accuracy of a diagnostic test, which is often quantified by a pair of measures such as sensitivity and specificity, is critical for medical decision making. Separate studies of an investigational diagnostic test can be combined through meta-analysis; however, such an analysis can be threatened by publication bias. To the best of our knowledge, there is no existing method that accounts for publication bias in the meta-analysis of diagnostic tests involving bivariate outcomes. In this paper, we extend the Copas selection model from univariate outcomes to bivariate outcomes for the correction of publication bias when the probability of a study being published can depend on its sensitivity, specificity, and the associated standard errors. We develop an expectation-maximization algorithm for the maximum likelihood estimation under the proposed selection model. We investigate the finite sample performance of the proposed method through simulation studies and illustrate the method by assessing a meta-analysis of 17 published studies of a rapid diagnostic test for influenza.

scholarly journals Utility of the methylated SEPT9 test for the early detection of colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy

2020 ◽  
Vol 7 (1) ◽  
pp. e000355 ◽  
Author(s):  
Rohit Hariharan ◽  
Mark Jenkins
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Early Detection ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Diagnostic Test ◽  
Predictive Value ◽  
Meta Analysis ◽  
Test Accuracy ◽  
Sensitivity Specificity

BackgroundCirculating tumour DNA from colorectal cancer (CRC) is a biomarker for early detection of the disease and therefore potentially useful for screening. One such biomarker is the methylated SEPT9 (mSEPT9) gene, which occurs during CRC tumourigenesis. This systematic review and meta-analysis aims to establish the sensitivity, specificity and accuracy of mSEPT9 tests for the early diagnosis of CRC.MethodsA systematic search of the relevant literature was conducted using Medline and Embase databases. Data were extracted from the eligible studies and analysed to estimate pooled sensitivity, specificity and diagnostic test accuracy.ResultsBased on 19 studies, the pooled estimates (and 95% CIs) for mSEPT9 to detect CRC were: sensitivity 69% (62–75); specificity 92% (89–95); positive likelihood ratio 9.1 (6.1–13.8); negative likelihood ratio 0.34 (0.27–0.42); diagnostic OR 27 (15–48) and area under the curve 0.89 (0.86–0.91). The test has a positive predictive value of 2.6% and negative predictive value of 99.9% in an average risk population (0.3% CRC prevalence), and 9.5% (positive predictive value) and 99.6% (negative predictive value) in a high-risk population (1.2% CRC prevalence).ConclusionThe mSEPT9 test has high specificity and moderate sensitivity for CRC and is therefore a potential alternative screening method for those declining faecal immunochemical test for occult blood (FIT) or other screening modalities. However, it is limited by its poor diagnostic performance for precancerous lesions (advanced adenomas and polyps) and its relatively high costs, and little is known about its acceptability to those declining to use the FIT.

Computer-aided diagnosis of gastrointestinal ulcer and hemorrhage using wireless capsule endoscopy: a systematic review and diagnostic test accuracy meta-analysis (Preprint)

2021 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Diagnostic Test ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Cad Models ◽  
Sensitivity Specificity ◽  
Wireless Capsule

BACKGROUND Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. OBJECTIVE To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. METHODS We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95–.98), .93 (.89–.95), .92 (.89–.94), and 138 (79–243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98–.99), .96 (.94–0.97), .97 (.95–.99), and 888 (343–2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs. erosions, hemorrhage vs. angioectasia) was found to be the source of heterogeneity. No publication bias was detected. CONCLUSIONS CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO): CRD42021253454 ; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=42021253454.

scholarly journals P-uniform*

2018 ◽  
Author(s):  
Robbie Cornelis Maria van Aert ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Publication Bias ◽  
Random Effects ◽  
Effect Size ◽  
Meta Analysis ◽  
Statistical Properties ◽  
Selection Model ◽  
Effect Sizes ◽  
Random Effects Model ◽  
Average Effect ◽  
Model Approach

Publication bias is a major threat to the validity of a meta-analysis resulting in overestimated effect sizes. P-uniform is a meta-analysis method that corrects estimates for publication bias but overestimates average effect size if heterogeneity in true effect sizes (i.e., between-study variance) is present. We propose an extension and improvement of p-uniform called p-uniform*. P-uniform* improves upon p-uniform in three important ways, as it (i) entails a more efficient estimator, (ii) eliminates the overestimation of effect size in case of between-study variance in true effect sizes, and (iii) enables estimating and testing for the presence of the between-study variance. We compared the statistical properties of p-uniform* with p-uniform, the selection model approach of Hedges (1992), and the random-effects model. Statistical properties of p-uniform* and the selection model approach were comparable and generally outperformed p-uniform and the random-effects model if publication bias was present. We demonstrate that p-uniform* and the selection model approach estimate average effect size and between-study variance rather well with ten or more studies in the meta-analysis when publication bias is not extreme. P-uniform* generally provides more accurate estimates of the between-study variance in meta-analyses containing many studies (e.g., 60 or more) and if publication bias is present. However, both methods do not perform well if the meta-analysis only includes statistically significant studies. P-uniform performed best in this case but only when between-study variance was zero or small. We offer recommendations for applied researchers, and provide an R package and an easy-to-use web application for applying p-uniform*.

scholarly journals Prognostic Accuracy of Cardiovascular Disease Biomarkers in Patients with COVID-19: A Diagnostic Test Accuracy Meta‐analysis

2021 ◽  
Author(s):  
Atefeh Nasir Kansestani ◽  
Mohammad Erfan Zare ◽  
Jun Zhang
Keyword(s):  
Publication Bias ◽  
Diagnostic Test ◽  
Good Accuracy ◽  
Troponin I ◽  
Meta Analysis ◽  
Diagnostic Test Accuracy ◽  
Critical Conditions ◽  
Test Accuracy ◽  
Acceptable Accuracy ◽  
D Dimer

Background: Several reports have determined that cardiovascular diseases (CVDs) are common complications in patients with coronavirus disease 2019 (COVID-19) and lead them to poor outcomes. CVD biomarkers have, thus, great potential to be used as prognostic biomarkers. We aimed to determine the accuracy of CVD biomarkers for the prognosis of the COVID-19 patient’s outcome via a diagnostic test accuracy (DTA) meta-analysis. Methods: Until September 30, 2020, we searched Web of Sciences, Scopus, and MEDLINE/PubMed databases to obtain related papers. The summary points and lines were calculated using bivariate/HSROC model. As outcomes, we considered critical conditions and mortality. Results: A total of 17 659 patients from 33 studies were included. Five biomarkers, namely increased levels of lactate dehydrogenase (LDH), cardiac troponin I (cTnI), creatine kinase (CK), D-dimer, and thrombocytopenia, met the inclusion criteria. Our results indicated that LDH and cTnI had good accuracy for the prognosis of critical condition (AUCHSROC=0.83 and 0.80, respectively), while LDH, cTnI, and D-dimer had acceptable accuracy (AUCHSROC=0.74, 0.71, and 0.72, respectively) for the prognosis of mortality. LDH and D-dimer had high sensitivity, whereas cTnI had high specificity. The other biomarkers did not have acceptable accuracy. Significant publication bias was found for D-dimer (P=0.053).   Conclusion: Among CVD biomarkers, LDH and cTnI had good accuracy for the prognosis of critical outcomes and acceptable accuracy for the prognosis of mortality, without publication bias. Given their different sensitivities and specificities, we recommend the use of these 2 biomarkers concomitantly. 

scholarly journals Diagnostic efficacy of serum 1,3-β-D-glucan for invasive fungal infection: An update meta-analysis based on 37 case or cohort studies

Open Medicine ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 329-337 ◽  
Author(s):  
Lu Xiaoling ◽  
Tang Tingyu ◽  
Hu Caibao ◽  
Zhao Tian ◽  
Chen Changqin
Keyword(s):  
Cohort Studies ◽  
Fungal Infection ◽  
Publication Bias ◽  
Invasive Fungal Infection ◽  
Meta Analysis ◽  
False Negative ◽  
True Negative ◽  
Diagnostic Efficacy ◽  
Funnel Plot ◽  
Sensitivity Specificity

AbstractObjectiveThe aim of this study was to investigate the diagnostic performance of serum 1,3-β-D-gluan as biomarker for invasive fungal infection through meta-analysis.MethodsThe electronic databases of Medline, Cochrane, Embase, Web of Science, OVID and CNKI were systematic searched to identified the case-control or Cohort studies relevant to diagnostic efficacy of serum 1,3-β-D-glucan for invasive fungal infection. The data of true positive (tp), false positive (fp), false negative (fn) and true negative (tn) patients number were extracted from each of the original included studies. The diagnostic sensitivity, specificity and systematic receiver operating characteristic (SROC) curve were calculated and pooled through random or fixed effect method. The publication bias was evaluated by the Deek’s funnel plot.ResultsThirty-seven relevant studies were fulfilled the inclusion criteria and included in our present meta-analysis. The combined sensitivity, specificity, positive likely hood ratio (+lr), negative likely hood ratio (-lr) and diagnostic odds ratio(dor) for 1,3-β-D-glucan in diagnosis of invasive fungal infectionwere 0.83 (95%CI:0.38-0.61), 0.81 (95%CI:0.80-0.82), 5.13 (95%CI:3.98-6.62), 0.23 (95%CI:0.18-0.30), and 29.68 (95%CI:18.94-46.52) respectively. The pooled area under the ROC curve (AUC) was 0.91.The Deek’s funnel plot asymmetry test showed there was no publication bias for 1,3-β-D-glucan in diagnosis of invasive fungal infection of the included 37 studies.ConclusionSerum 1,3-β-D-glucan assay was a promising biomarker for invasive fungal infection diagnosis.

scholarly journals A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis

2012 ◽  
Vol 32 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Dimitris Mavridis ◽  
Alex Sutton ◽  
Andrea Cipriani ◽  
Georgia Salanti
Keyword(s):  
Publication Bias ◽  
Meta Analysis ◽  
Selection Model ◽  
Fully Bayesian

scholarly journals Robust Fitting of a Wrapped Normal Model to Multivariate Circular Data and Outlier Detection

Stats ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 454-471
Author(s):  
Luca Greco ◽  
Giovanni Saraceno ◽  
Claudio Agostinelli
Keyword(s):  
Outlier Detection ◽  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Circular Data ◽  
Normal Model ◽  
Finite Sample ◽  
Robust Fitting ◽  
Definition Of ◽  
Wrapped Normal

In this work, we deal with a robust fitting of a wrapped normal model to multivariate circular data. Robust estimation is supposed to mitigate the adverse effects of outliers on inference. Furthermore, the use of a proper robust method leads to the definition of effective outlier detection rules. Robust fitting is achieved by a suitable modification of a classification-expectation-maximization algorithm that has been developed to perform a maximum likelihood estimation of the parameters of a multivariate wrapped normal distribution. The modification concerns the use of complete-data estimating equations that involve a set of data dependent weights aimed to downweight the effect of possible outliers. Several robust techniques are considered to define weights. The finite sample behavior of the resulting proposed methods is investigated by some numerical studies and real data examples.

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