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1.
Multivariate meta-analysis is gaining prominence in evidence synthesis research because it enables simultaneous synthesis of multiple correlated outcome data, and random-effects models have generally been used for addressing between-studies heterogeneities. However, coverage probabilities of confidence regions or intervals for standard inference methods for random-effects models (eg, restricted maximum likelihood estimation) cannot retain their nominal confidence levels in general, especially when the number of synthesized studies is small because their validities depend on large sample approximations. In this article, we provide permutation-based inference methods that enable exact joint inferences for average outcome measures without large sample approximations. We also provide accurate marginal inference methods under general settings of multivariate meta-analyses. We propose effective approaches for permutation inferences using optimal weighting based on the efficient score statistic. The effectiveness of the proposed methods is illustrated via applications to bivariate meta-analyses of diagnostic accuracy studies for airway eosinophilia in asthma and a network meta-analysis for antihypertensive drugs on incident diabetes, as well as through simulation experiments. In numerical evaluations performed via simulations, our methods generally provided accurate confidence regions or intervals under a broad range of settings, whereas the current standard inference methods exhibited serious undercoverage properties. 相似文献
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Zelalem F. Negeri Mateen Shaikh Joseph Beyene 《Biometrical journal. Biometrische Zeitschrift》2018,60(4):827-844
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. In this paper, we extend the current standard bivariate linear mixed model (LMM) by proposing two variance‐stabilizing transformations: the arcsine square root and the Freeman–Tukey double arcsine transformation. We compared the performance of the proposed methods with the standard method through simulations using several performance measures. The simulation results showed that our proposed methods performed better than the standard LMM in terms of bias, root mean square error, and coverage probability in most of the scenarios, even when data were generated assuming the standard LMM. We also illustrated the methods using two real data sets. 相似文献
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Antonia Zapf;Cornelia Frömke;Juliane Hardt;Gerta Rücker;Dina Voeltz;Annika Hoyer; 《Biometrical journal. Biometrische Zeitschrift》2024,66(7):e202300101
The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf et al., Biometrical Journal. 2021; 63(4): 699–711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice. 相似文献
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Sarah K. Helman Riley O. Mummah Katelyn M. Gostic Michael G. Buhnerkempe Katherine C. Prager James O. Lloyd‐Smith 《Ecology and evolution》2020,10(14):7221-7232
- Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.
- In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near‐perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.
- We used simulations to explore how the trade‐off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real‐world conditions.
- We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.
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Y.S. Mahmmod L. Svennesen J. Katholm K. Pedersen I.C. Klaas 《Journal of applied microbiology》2019,127(2):406-417
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Meta-analysis of binary data is challenging when the event under investigation is rare, and standard models for random-effects meta-analysis perform poorly in such settings. In this simulation study, we investigate the performance of different random-effects meta-analysis models in terms of point and interval estimation of the pooled log odds ratio in rare events meta-analysis. First and foremost, we evaluate the performance of a hypergeometric-normal model from the family of generalized linear mixed models (GLMMs), which has been recommended, but has not yet been thoroughly investigated for rare events meta-analysis. Performance of this model is compared to performance of the beta-binomial model, which yielded favorable results in previous simulation studies, and to the performance of models that are frequently used in rare events meta-analysis, such as the inverse variance model and the Mantel–Haenszel method. In addition to considering a large number of simulation parameters inspired by real-world data settings, we study the comparative performance of the meta-analytic models under two different data-generating models (DGMs) that have been used in past simulation studies. The results of this study show that the hypergeometric-normal GLMM is useful for meta-analysis of rare events when moderate to large heterogeneity is present. In addition, our study reveals important insights with regard to the performance of the beta-binomial model under different DGMs from the binomial-normal family. In particular, we demonstrate that although misalignment of the beta-binomial model with the DGM affects its performance, it shows more robustness to the DGM than its competitors. 相似文献
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Multivariate meta-analysis models can be used to synthesize multiple, correlated endpoints such as overall and disease-free survival. A hierarchical framework for multivariate random-effects meta-analysis includes both within-study and between-study correlation. The within-study correlations are assumed known, but they are usually unavailable, which limits the multivariate approach in practice. In this paper, we consider synthesis of 2 correlated endpoints and propose an alternative model for bivariate random-effects meta-analysis (BRMA). This model maintains the individual weighting of each study in the analysis but includes only one overall correlation parameter, rho, which removes the need to know the within-study correlations. Further, the only data needed to fit the model are those required for a separate univariate random-effects meta-analysis (URMA) of each endpoint, currently the common approach in practice. This makes the alternative model immediately applicable to a wide variety of evidence synthesis situations, including studies of prognosis and surrogate outcomes. We examine the performance of the alternative model through analytic assessment, a realistic simulation study, and application to data sets from the literature. Our results show that, unless rho is very close to 1 or -1, the alternative model produces appropriate pooled estimates with little bias that (i) are very similar to those from a fully hierarchical BRMA model where the within-study correlations are known and (ii) have better statistical properties than those from separate URMAs, especially given missing data. The alternative model is also less prone to estimation at parameter space boundaries than the fully hierarchical model and thus may be preferred even when the within-study correlations are known. It also suitably estimates a function of the pooled estimates and their correlation; however, it only provides an approximate indication of the between-study variation. The alternative model greatly facilitates the utilization of correlation in meta-analysis and should allow an increased application of BRMA in practice. 相似文献
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Objectives: Fine needle aspiration cytology (FNAC) of the thyroid is a non-invasive, cost-effective screening procedure that is valuable for distinguishing neoplastic lesions from non-neoplastic nodules. The aim of this study was to determine the diagnostic accuracy of FNACs performed at our institution by correlating FNAC results with histopathological diagnoses.
Methods: Two hundred and seventy-one aspiration cytology specimens followed by thyroidectomy were included in the study, and the results of 260 adequate FNACs were compared with their histological diagnoses.
Results: The sensitivity and specificity of thyroid FNAC for detecting neoplasia were 92.6% and 91.6%, respectively. There were 15 (5.7%) false positives and six (2.3%) false negatives.
Conclusions: The results showed that follicular cells that exhibit some of the features of papillary carcinoma could be observed in a cytology slide of Hashimoto's thyroiditis, leading to a diagnostic pitfall. In addition, cellularity and overlapping cytological criteria in hyperplasia might lead to a false diagnosis. 相似文献
Methods: Two hundred and seventy-one aspiration cytology specimens followed by thyroidectomy were included in the study, and the results of 260 adequate FNACs were compared with their histological diagnoses.
Results: The sensitivity and specificity of thyroid FNAC for detecting neoplasia were 92.6% and 91.6%, respectively. There were 15 (5.7%) false positives and six (2.3%) false negatives.
Conclusions: The results showed that follicular cells that exhibit some of the features of papillary carcinoma could be observed in a cytology slide of Hashimoto's thyroiditis, leading to a diagnostic pitfall. In addition, cellularity and overlapping cytological criteria in hyperplasia might lead to a false diagnosis. 相似文献
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B. Lon&#;ar M. Pajtler V. Mili&#;i&#;-Juhas &#;. Kotromanovi&#; B. Staklenac B. Pauzar 《Cytopathology》2007,18(1):40-43
OBJECTIVE: The aim of this study was to evaluate the role of cytology in providing a reliable diagnosis upon which the clinician can base further investigative or treatment strategies in patients with laryngeal and pharyngeal tumours. METHODS: Imprint cytology diagnoses from 174 patients were correlated with the histological result of a corresponding biopsy. RESULTS: We found that the imprint cytology proved to be a useful, quick and reliable method with complete diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive values of 97%, 96%, 100%, 100% and 92% respectively. CONCLUSION: Imprint cytology allows diagnostic statements in a shorter time than is possible with histological sections and proves a useful adjunct in evaluating laryngeal and pharyngeal lesions. The validity of the method depends on the care with which the specimen is sampled and on the experience of the investigator. 相似文献
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Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from nondiseased individuals. Among various performance metrics, specificity at a controlled sensitivity level (or vice versa) is often desirable because it directly targets the clinical utility of the intended clinical test. Meanwhile, covariates, such as age, race, as well as sample collection conditions, could impact the biomarker distribution and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. Most existing covariate adjustment methods do not specifically target the desired sensitivity/specificity level, but rather do so for the entire biomarker distribution. As such, they might be more prone to model misspecification. In this paper, we suggest a parsimonious quantile regression model for the diseased population, only locally at the controlled sensitivity level, and assess specificity with covariate-specific control of the sensitivity. Variance estimates are obtained from a sample-based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate-adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer. 相似文献
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B. M. Bennett 《Biometrical journal. Biometrische Zeitschrift》1982,24(1):59-62
The usual definitions of sensitivity, specificity and predictive value of a diagnostic test T refer only to the situation of the presence or absence of disease. There is then question of the appropriate extensions of these definitions in cases where there are more than two diagnostic categories, especially with ordered or graded responses. This paper deals with some aspects of this problem. An example with the data of BERGESON and STEINFELD (1974) on gradings of fever in children by palpation is analyzed, using the methods discussed in this paper. 相似文献
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Marie Beisemann Philipp Doebler Heinz Holling 《Biometrical journal. Biometrische Zeitschrift》2020,62(7):1597-1630
Pooling the relative risk (RR) across studies investigating rare events, for example, adverse events, via meta-analytical methods still presents a challenge to researchers. The main reason for this is the high probability of observing no events in treatment or control group or both, resulting in an undefined log RR (the basis of standard meta-analysis). Other technical challenges ensue, for example, the violation of normality assumptions, or bias due to exclusion of studies and application of continuity corrections, leading to poor performance of standard approaches. In the present simulation study, we compared three recently proposed alternative models (random-effects [RE] Poisson regression, RE zero-inflated Poisson [ZIP] regression, binomial regression) to the standard methods in conjunction with different continuity corrections and to different versions of beta-binomial regression. Based on our investigation of the models' performance in 162 different simulation settings informed by meta-analyses from the Cochrane database and distinguished by different underlying true effects, degrees of between-study heterogeneity, numbers of primary studies, group size ratios, and baseline risks, we recommend the use of the RE Poisson regression model. The beta-binomial model recommended by Kuss (2015) also performed well. Decent performance was also exhibited by the ZIP models, but they also had considerable convergence issues. We stress that these recommendations are only valid for meta-analyses with larger numbers of primary studies. All models are applied to data from two Cochrane reviews to illustrate differences between and issues of the models. Limitations as well as practical implications and recommendations are discussed; a flowchart summarizing recommendations is provided. 相似文献
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Isabel M. D. Rosa Ana Teresa Marques Gustavo Palminha Hugo Costa Miguel Mascarenhas Carlos Fonseca Joana Bernardino 《Ibis》2016,158(1):28-42
Radar systems have been increasingly used to monitor birds. To take full advantage of the large datasets provided by radars, researchers have implemented machine learning (ML) techniques that automatically read and attempt to classify targets. Here we used data collected from two locations in Portugal with two marine radar antennas (VSR and HSR) to apply and compare the performance of six ML algorithms that are widely used in the literature: random forests (RF), support vector machine (SVM), artificial neural networks (NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and decision trees (DT), all trained with several dataset configurations. We found that all algorithms performed well (area under the receiver operating characteristic (AUC) and accuracy > 0.80, P < 0.001) when discriminating birds from non‐biological targets such as vehicles, rain or wind turbines, but greater variance in the performance among algorithms was apparent when separating different bird functional groups or bird species (e.g. herons vs. gulls). In our case study, only RF was able to hold an accuracy > 0.80 for all classification tasks, although SVM and DT also performed well. Further, all algorithms correctly classified 86% and 66% (VSR and HSR) of the target points, and only 2% and 4% of these points were misclassified by all algorithms. Our results suggest that ML algorithms are suitable for classifying radar targets as birds, and thereby separating them from other non‐biological targets. The ability of these algorithms to correctly identify among bird species functional groups was found to be much weaker, but if properly trained and supported by a good ground truthing dataset, targeted to the relevant species groups, some of these algorithms are still able to achieve high accuracies in classification tasks. Such results indicate that ML algorithms are suitable for use in near real‐time monitoring of bird movements, and may help to mitigate collision of birds with, for example, wind turbines or airplanes. 相似文献
18.
Yasser E Nassef Mones M Abu Shady Essam M Galal Manal A Hamed 《Memórias do Instituto Oswaldo Cruz》2013,108(7):887-893
The aim of the present study was to identify specific markers that mirror liver fibrosis progression as an alternative to biopsy when biopsy is contraindicated, especially in children. After liver biopsies were performed, serum samples from 30 hepatitis C virus (HCV) paediatric patients (8-14 years) were analysed and compared with samples from 30 healthy subjects. All subjects were tested for the presence of serum anti-HCV antibodies. Direct biomarkers for liver fibrosis, including transforming growth factor-β1, tissue inhibitor of matrix metalloproteinase-1 (TIMP-1), hyaluronic acid (HA), procollagen type III amino-terminal peptide (PIIINP) and osteopontin (OPN), were measured. The indirect biomarkers aspartate and alanine aminotransferases, albumin and bilirubin were also tested. The results revealed a significant increase in the serum marker levels in HCV-infected children compared with the healthy group, whereas albumin levels exhibited a significant decrease. Significantly higher levels of PIIINP, TIMP-1, OPN and HA were detected in HCV-infected children with moderate to severe fibrosis compared with children with mild fibrosis (p < 0.05). The diagnostic accuracy of these direct biomarkers, represented by sensitivity, specificity and positive predictive value, emphasises the utility of PIIINP, TIMP-1, OPN and HA as indicators of liver fibrosis among HCV-infected children. 相似文献
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Romane H. Cristescu Russell L. Miller Anthony J. Schultz Lyndal Hulse Damian Jaccoud Stephen Johnston Jon Hanger Rosie Booth Cline H. Frre 《Molecular ecology resources》2019,19(4):957-969
Wildlife diseases are a recognized driver of global biodiversity loss, have substantial economic impacts, and are increasingly becoming a threat to human health. Disease surveillance is critical but remains difficult in the wild due to the substantial costs and potential biases associated with most disease detection methods. Noninvasive scat surveys have been proposed as a health monitoring methodology to overcome some of these limitations. Here, we use the known threat of Chlamydia disease to the iconic, yet vulnerable, koala Phascolarctos cinereus to compare three methods for Chlamydia detection in scats: multiplex quantitative PCR, next generation sequencing, and a detection dog specifically trained on scats from Chlamydia‐infected koalas. All three methods demonstrated 100% specificity, while sensitivity was variable. Of particular interest is the variable sensitivity of these diagnostic tests to detect sick individuals (i.e., not only infection as confirmed by Chlamydia‐positive swabs, but with observable clinical signs of the disease); for koalas with urogenital tract disease signs, sensitivity was 78% with quantitative PCR, 50% with next generation genotyping and 100% with the detection dog method. This may be due to molecular methods having to rely on high‐quality DNA whereas the dog most likely detects volatile organic compounds. The most appropriate diagnostic test will vary with disease prevalence and the specific aims of disease surveillance. Acknowledging that detection dogs might not be easily accessible to all, the future development of affordable and portable “artificial noses” to detect diseases from scats in the field might enable cost‐effective, rapid and large‐scale disease surveillance. 相似文献
20.
Hodgkin's disease: diagnostic accuracy of fine needle aspiration; a report based on 62 consecutive cases 总被引：1，自引：0，他引：1
F. FULCINITI A. VETRANI P. ZEPPA G. GIORDANO M. MARINO G. de ROSA L. PALOMBINI 《Cytopathology》1994,5(4):226-233
We report on our series of 62 cases occurring between January 1977 and December 1990, which were diagnosed as Hodgkin's disease by fine needle aspiration (FNA) samples. the overall accuracy of the cytological diagnosis was high, with only four incorrect diagnoses and a positive predictive value of 93.5%. the value of FNA as a first level diagnostic technique in the screening of lymphadenopathies is discussed, as well as the limitations and pitfalls of the cytological diagnosis. 相似文献