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Meta-DiSc: a software for meta-analysis of test accuracy data
Javier Zamora, Victor Abraira, Alfonso Muriel, Khalid Khan, Arri Coomarasamy
BMC Medical Research Methodology , 2006, DOI: 10.1186/1471-2288-6-31
Abstract: Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities.Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at http://www.hrc.es/investigacion/metadisc_en.htm webcite.Accurate diagnosis forms the basis of good clinical care, as without it one can neither prognosticate correctly nor choose the right treatment. Indeed, a wrong diagnosis can harm patients by exposing them to inappropriate or sub-optimal therapy [1]. Thus studies of diagnostic accuracy, and particularly their systematic reviews and meta-analyses, are being recognised as instrumental in underpinning evidence-based clinical practice. Initiatives such as STARD [2] and developments within the Cochrane Collaboration [3] to accept protocols and reviews of test accuracy studies highlight the emphasis being given to evidence-based diagnosis.Currently, there is only one test accuracy meta-analysis package, Meta-Test [4], which addresses some of the unique statistical issues related to test accuracy, such as pooling of sensitivities and specificities and summary receiver o
meta4diag: Bayesian Bivariate Meta-analysis of Diagnostic Test Studies for Routine Practice  [PDF]
Jingyi Guo,Andrea Riebler
Statistics , 2015,
Abstract: This paper introduces the \proglang{R} package \pkg{meta4diag} for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package \pkg{meta4diag} is a purpose-built front end of the \proglang{R} package \pkg{INLA}. While \pkg{INLA} offers full Bayesian inference for the large set of latent Gaussian models using integrated nested Laplace approximations, \pkg{meta4diag} extracts the features needed for bivariate meta-analysis and presents them in an intuitive way. It allows the user a straightforward model-specification and offers user-specific prior distributions. Further, the newly proposed penalised complexity prior framework is supported, which builds on prior intuitions about the behaviours of the variance and correlation parameters. Accurate posterior marginal distributions for sensitivity and specificity as well as all hyperparameters, and covariates are directly obtained without Markov chain Monte Carlo sampling. Further, univariate estimates of interest, such as odds ratios, as well as the SROC curve and other common graphics are directly available for interpretation. An interactive graphical user interface provides the user with the full functionality of the package without requiring any \proglang{R} programming. The package is available through CRAN \url{https://cran.r-project.org/web/packages/meta4diag/} and its usage will be illustrated using three real data examples.
Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors  [PDF]
Jingyi Guo,H?vard Rue,Andrea Riebler
Statistics , 2015,
Abstract: In a bivariate meta-analysis the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Bayesian inference is attractive as informative priors that add small amount of information can stabilise the analysis without overwhelming the data. However, Bayesian analysis is often computationally demanding and the selection of the prior for the covariance matrix of the bivariate structure is crucial with little data. The integrated nested Laplace approximations (INLA) method provides an efficient solution to the computational issues by avoiding any sampling, but the important question of priors remain. We explore the penalised complexity (PC) prior framework for specifying informative priors for the variance parameters and the correlation parameter. PC priors facilitate model interpretation and hyperparameter specification as expert knowledge can be incorporated intuitively. We conduct a simulation study to compare the properties and behaviour of differently defined PC priors to currently used priors in the field. The simulation study shows that the use of PC priors results in more precise estimates when specified in a sensible neighbourhood around the truth. To investigate the usage of PC priors in practice we reanalyse a meta-analysis using the telomerase marker for the diagnosis of bladder cancer.
A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence  [PDF]
Aristidis K. Nikoloulopoulos
Statistics , 2015, DOI: 10.1177/0962280215596769
Abstract: A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that is superior to the standard generalized linear mixed model (GLMM) in this context. Here we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate GLMM as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analysing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate GLMM in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness including reflection asymmetric tail dependence, and, computational feasibility despite their three-dimensionality.
A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution  [PDF]
Aristidis K. Nikoloulopoulos
Statistics , 2015, DOI: 10.1002/sim.6595
Abstract: Diagnostic test accuracy studies typically report the number of true positives, false positives, true negatives and false negatives. There usually exists a negative association between the number of true positives and true negatives, because studies that adopt less stringent criterion for declaring a test positive invoke higher sensitivities and lower specificities. A generalized linear mixed model (GLMM) is currently recommended to synthesize diagnostic test accuracy studies. We propose a copula mixed model for bivariate meta-analysis of diagnostic test accuracy studies. Our general model includes the GLMM as a special case and can also operate on the original scale of sensitivity and specificity. Summary receiver operating characteristic curves are deduced for the proposed model through quantile regression techniques and different characterizations of the bivariate random effects distribution. Our general methodology is demonstrated with an extensive simulation study and illustrated by re-analysing the data of two published meta-analyses. Our study suggests that there can be an improvement on GLMM in fit to data and makes the argument for moving to copula random effects models. Our modelling framework is implemented in the package CopulaREMADA within the open source statistical environment R.
The oral glucose tolerance test for the diagnosis of diabetes mellitus in patients during acute coronary syndrome hospitalization: a meta-analysis of diagnostic test accuracy
Yicong Ye, Hongzhi Xie, Xiliang Zhao, Shuyang Zhang
Cardiovascular Diabetology , 2012, DOI: 10.1186/1475-2840-11-155
Abstract: A systematic search of databases (MEDLINE [1985 to March 2012], EMBASE [1985 to March 2012]) was conducted. All prospective cohort studies assessing the accuracy or reproducibility of an OGTT in ACS or non-ACS individuals were included. A bivariate model was used to calculate the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was explored using subgroup analysis and meta-regression.Fifteen studies with 8,027 participants were included (10 ACS and 5 non-ACS studies). The pooled results on SEN, SPE, PLR, NLR, and DOR were 0.70 (95% CI, 0.60-0.78), 0.91 (95% CI, 0.86-0.94), 7.6 (95% CI, 4.9-11.7), 0.33 (95% CI, 0.25-0.45), and 23 (95% CI, 12–41), respectively. The OGTT has a slightly lower SPE in diagnosing DM in ACS than in non-ACS patients (0.86 [95% CI 0.81-0.92] versus 0.95 [95% CI 0.93-0.98], p<0.01), while the SEN values are comparable (0.71 [95% CI 0.60-0.82] versus 0.67 [95% CI 0.54-0.81], p=0.43). After adjusting the interval between repeated tests and age, the meta-regression did not show a difference in DOR between ACS and non-ACS studies.Despite the discrepancy in the interval between the two OGTTs, performing an OGTT in patients with ACS provides accuracy that is similar to that in in non-ACS patients. It is reasonable to screen patients hospitalized for ACS for previously undiagnosed DM using an OGTT.Numerous studies have demonstrated that hyperglycemia is common among patients with acute coronary syndrome (ACS) [1,2], and the relationship between hyperglycemia and increased mortality risk in ACS has been well established across various glucose metrics [3,4].However, considering its accuracy and reproducibility in stress condition, the routine performance of an oral glucose tolerance test (OGTT) to diagnose diabetes during the acute phase of ACS is still the subject of ongoing debate. The European guidelines on diabetes, pre-diabetes, and cardiov
Bayesian Methods for Medical Test Accuracy  [PDF]
Lyle D. Broemeling
Diagnostics , 2011, DOI: 10.3390/diagnostics1010001
Abstract: Bayesian methods for medical test accuracy are presented, beginning with the basic measures for tests with binary scores: true positive fraction, false positive fraction, positive predictive values, and negative predictive value. The Bayesian approach is taken because of its efficient use of prior information, and the analysis is executed with a Bayesian software package WinBUGS?. The ROC (receiver operating characteristic) curve gives the intrinsic accuracy of medical tests that have ordinal or continuous scores, and the Bayesian approach is illustrated with many examples from cancer and other diseases. Medical tests include X-ray, mammography, ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine and tests based on biomarkers, such as blood glucose values for diabetes. The presentation continues with more specialized methods suitable for measuring the accuracies of clinical studies that have verification bias, and medical tests without a gold standard. Lastly, the review is concluded with Bayesian methods for measuring the accuracy of the combination of two or more tests.
The urine dipstick test useful to rule out infections. A meta-analysis of the accuracy
Walter LJM Devillé, Joris C Yzermans, Nico P van Duijn, P Dick Bezemer, Dani?lle AWM van der Windt, Lex M Bouter
BMC Urology , 2004, DOI: 10.1186/1471-2490-4-4
Abstract: Literature from 1990 through 1999 was searched in Medline and Embase, and by reference tracking. Selected publications should be concerned with the diagnosis of bacteriuria or urinary tract infections, investigate the use of dipstick tests for nitrites and/or leukocyte esterase, and present empirical data. A checklist was used to assess methodological quality.70 publications were included. Accuracy of nitrites was high in pregnant women (Diagnostic Odds Ratio = 165) and elderly people (DOR = 108). Positive predictive values were ≥80% in elderly and in family medicine. Accuracy of leukocyte-esterase was high in studies in urology patients (DOR = 276). Sensitivities were highest in family medicine (86%). Negative predictive values were high in both tests in all patient groups and settings, except for in family medicine. The combination of both test results showed an important increase in sensitivity. Accuracy was high in studies in urology patients (DOR = 52), in children (DOR = 46), and if clinical information was present (DOR = 28). Sensitivity was highest in studies carried out in family medicine (90%). Predictive values of combinations of positive test results were low in all other situations.Overall, this review demonstrates that the urine dipstick test alone seems to be useful in all populations to exclude the presence of infection if the results of both nitrites and leukocyte-esterase are negative. Sensitivities of the combination of both tests vary between 68 and 88% in different patient groups, but positive test results have to be confirmed. Although the combination of positive test results is very sensitive in family practice, the usefulness of the dipstick test alone to rule in infection remains doubtful, even with high pre-test probabilities.Testing for the presence of micro-organisms in the urinary tract, in order to diagnose asymptomatic bacteriuria or symptomatic urinary tract infections (UTI), is very common at all levels of health care. UTI are a comm
Non-invasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and meta-analysis
Caroline F Wright, Yinghui Wei, Julian PT Higgins, Gurdeep S Sagoo
BMC Research Notes , 2012, DOI: 10.1186/1756-0500-5-476
Abstract: Ninety studies, incorporating 9,965 pregnancies and 10,587 fetal sex results met our inclusion criteria. Overall mean sensitivity was 96.6% (95% credible interval 95.2% to 97.7%) and mean specificity was 98.9% (95% CI?=?98.1% to 99.4%). These results vary very little with trimester or week of testing, indicating that the performance of the test is reliably high.Based on this review and meta-analysis we conclude that fetal sex can be determined with a high level of accuracy by analyzing cffDNA. Using cffDNA in prenatal diagnosis to replace or complement existing invasive methods can remove or reduce the risk of miscarriage. Future work should concentrate on the economic and ethical considerations of implementing an early non-invasive test for fetal sex.Knowledge of the genetic status of the fetus in an on-going pregnancy gives couples the power to make an informed decision about their unborn child. When a fetus is known to have a particular genetic abnormality, a decision may be made either to choose termination or to continue with the pregnancy and take steps to provide appropriate care for the newborn child. Prenatal testing falls into two categories: screening and diagnosis. Prenatal screening is offered to all pregnant women as part of routine prenatal care to determine if the fetus is at substantial risk of having a particular disorder such as Down Syndrome or sickle cell anaemia. In cases deemed to be at high risk, prenatal diagnosis is offered to provide a definitive diagnosis and determine whether the fetus has inherited a disorder.Prenatal genetic diagnosis is often used where there is a family history of a sex-linked disease. Most sex-linked diseases are recessive X-linked diseases caused by a particular mutation on the X chromosome. The disease is normally manifested only in males, who carry a single X chromosome, whilst in females the normal allele on the second X chromosome compensates for the diseased allele. The most common X-linked recessive diseases
The Effectiveness of Noninvasive Biomarkers to Predict Hepatitis B-Related Significant Fibrosis and Cirrhosis: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy  [PDF]
Xue-Ying Xu, Hong Kong, Rui-Xiang Song, Yu-Han Zhai, Xiao-Fei Wu, Wen-Si Ai, Hong-Bo Liu
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0100182
Abstract: Noninvasive biomarkers have been developed to predict hepatitis B virus (HBV)-related fibrosis owing to the significant limitations of liver biopsy. Those biomarkers were initially derived from evaluation of hepatitis C virus (HCV)-related fibrosis, and their accuracy among HBV-infected patients was under constant debate. A systematic review was conducted on records in PubMed, EMBASE and the Cochrane Library electronic databases, up until April 1st, 2013, in order to systematically assess the effectiveness and accuracy of these biomarkers for predicting HBV-related fibrosis. The questionnaire for quality assessment of diagnostic accuracy studies (QUADAS) was used. Out of 115 articles evaluated for eligibility, 79 studies satisfied the pre-determined inclusion criteria for meta-analysis. Eventually, our final data set for the meta-analysis contained 30 studies. The areas under the SROC curve for APRI, FIB-4, and FibroTest of significant fibrosis were 0.77, 0.75, and 0.84, respectively. For cirrhosis, the areas under the SROC curve for APRI, FIB-4 and FibroTest were 0.75, 0.87, and 0.90, respectively. The heterogeneity of FIB-4 and FibroTest were not statistically significant. The heterogeneity of APRI for detecting significant fibrosis was affected by median age (P = 0.0211), and for cirrhosis was affected by etiology (P = 0.0159). Based on the analysis we claim that FibroTest has excellent diagnostic accuracy for identification of HBV-related significant fibrosis and cirrhosis. FIB-4 has modest benefits and may be suitable for wider scope implementation.
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