A generalized meta-analysis model for binary diagnostic test performance

Methods for meta-analysis of diagnostic test accuracy studies must, in addition to unobserved heterogeneity, account for covariate heterogeneity, threshold effects, methodological quality and small study bias, whic constitute the major threats to the validity of meta-analytic results. These have traditionally been addressed independent of each other. Two recent methodological advances include (1) the bivariate random-effects model for joint synthesis of sensitivity and specificity, which accounts for unobsrved heterogeneity and threshold variation using random-effects and covariate and qualty effects as indepedent variables in a meta-regression; and (2) a linear regression test for funnel plot asymmetry in which the diagnostic odds ratio as effect size measure is regressed on effective sample size as a precision measure. I propose a generalized framework for diagnostic meta-analysis which integrates both developments based on a modification of the bivariate Dale's model in which two univariate random-effects logistic models for sensitivity and specificity are associated through a log-linear model of odds ratios with the effective sample size as an independent variable,. This unifies the estimation of summary test performance and assessment of the presence, extent and sources of variability. Taking advantage of the ability of gllamm to model a mixture of discrete and continous outcomes, I will discuss specification, estimation, diagnostics and prediction of the model, using a motivating dataset of 43 studies investigating FDG-PET for staging the axilla in patients with newly-diagnosed breast cancer.

"Metagraphiti By Stata": Visuographical Exploration and Presentation of Meta-analytic Data Using Stata

Meta-analysis is considered the highest level of evidence on effectiveness of healthcare interventions. It provides important information by capitalizing on the large numbers of studies performed to assess the impact of healthcare interventions, helps reduce variability and uncertainty among published reports of efficacy, produce summary estimates of effectiveness for clinical decision making and evaluate the quality of the published evidence. However, a large proportion of meta-analyses pose a surprising challenge for the uninitiated user: in order to figure out what the researchers found, the user must struggle through a maze of textual jargon, statistical formulae and lengthy lists of actual studies and extensive tables of overall average effect size and mean effect sizes for important subgroups of studies. On the premise that "a picture is worth more than a thousand words but a 'metagraphita' is worth more than a thousand words and statistical tests", the purpose of this presentation is to provide an idiot-proof overview of statistical graphics/diagnostic plots for exploration of publication bias, data distribution, heterogeneity and for summarizing overall datasets. Discussion will include the construction and interpretation of general graphical displays such as weighted histograms, normal quantile plots, forest plots, funnel graphs, scatter diagrams, as well as plots unique to diagnostic meta-analysis (e.g. ROC plane graphic, Accuracy-Threshold regression plots, summary receiver operator characteristic curves and likelihood ratio scattergrams). Presentation will consist of didactic slide presentation supplemented by handouts and an annotated bibliography and illustration of derivation and interpretation of visual displays from published meta-analyses using Stata.

Virtual colonoscopy; real misses

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72356/1/j.1572-0241.2003.08448.x.pd

Multimodal MRI as a diagnostic biomarker for amyotrophic lateral sclerosis

Objective Reliable biomarkers for amyotrophic lateral sclerosis ( ALS ) are needed, given the clinical heterogeneity of the disease. Here, we provide proof‐of‐concept for using multimodal magnetic resonance imaging ( MRI ) as a diagnostic biomarker for ALS . Specifically, we evaluated the added diagnostic utility of proton magnetic resonance spectroscopy ( MRS ) to diffusion tensor imaging ( DTI ). Methods Twenty‐nine patients with ALS and 30 age‐ and gender‐matched healthy controls underwent brain MRI which used proton MRS including spectral editing techniques to measure γ‐aminobutyric acid ( GABA ) and DTI to measure fractional anisotropy of the corticospinal tract. Data were analyzed using logistic regression, t ‐tests, and generalized linear models with leave‐one‐out analysis to generate and compare the resulting receiver operating characteristic ( ROC ) curves. Results The diagnostic accuracy is significantly improved when the MRS data were combined with the DTI data as compared to the DTI data only (area under the ROC curves ( AUC ) = 0.93 vs. AUC  = 0.81; P  = 0.05). The combined MRS and DTI data resulted in sensitivity of 0.93, specificity of 0.85, positive likelihood ratio of 6.20, and negative likelihood ratio of 0.08 whereas the DTI data only resulted in sensitivity of 0.86, specificity of 0.70, positive likelihood ratio of 2.87, and negative likelihood ratio of 0.20. Interpretation Combining multiple advanced neuroimaging modalities significantly improves disease discrimination between ALS patients and healthy controls. These results provide an important step toward advancing a multimodal MRI approach along the diagnostic test development pathway for ALS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106065/1/acn330.pd

Multidetector computed tomography angiography for assessment of in-stent restenosis: meta-analysis of diagnostic performance

<p>Abstract</p> <p>Background</p> <p>Multi-detector computed tomography angiography (MDCTA)of the coronary arteries after stenting has been evaluated in multiple studies.</p> <p>The purpose of this study was to perform a structured review and meta-analysis of the diagnostic performance of MDCTA for the detection of in-stent restenosis in the coronary arteries.</p> <p>Methods</p> <p>A Pubmed and manual search of the literature on in-stent restenosis (ISR) detected on MDCTA compared with conventional coronary angiography (CA) was performed. Bivariate summary receiver operating curve (SROC) analysis, with calculation of summary estimates was done on a stent and patient basis. In addition, the influence of study characteristics on diagnostic performance and number of non-assessable segments (NAP) was investigated with logistic meta-regression.</p> <p>Results</p> <p>Fourteen studies were included. On a stent basis, Pooled sensitivity and specificity were 0.82(0.72–0.89) and 0.91 (0.83–0.96). Pooled negative likelihood ratio and positive likelihood ratio were 0.20 (0.13–0.32) and 9.34 (4.68–18.62) respectively. The exclusion of non-assessable stents and the strut thickness of the stents had an influence on the diagnostic performance. The proportion of non-assessable stents was influenced by the number of detectors, stent diameter, strut thickness and the use of an edge-enhancing kernel.</p> <p>Conclusion</p> <p>The sensitivity of MDTCA for the detection of in-stent stenosis is insufficient to use this test to select patients for further invasive testing as with this strategy around 20% of the patients with in-stent stenosis would be missed. Further improvement of scanner technology is needed before it can be recommended as a triage instrument in practice. In addition, the number of non-assessable stents is also high.</p

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164\ua0Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models

MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies

midas is a user-written command for idiot-proof implementation of some of the contemporary statistical methods for meta-analysis of binary diagnostic test accuracy. Primary data synthesis is performed within the bivariate mixed-effects logistic regression modeling framework. Likelihood-based estimation is by adaptive gaussian quadrature using xtmelogit (Stata release 10) with post-estimation procedures for model diagnostics and empirical Bayes predictions. Average sensitivity and specificity (optionally depicted in SROC space with or without confidence and prediction regions), and their derivative likelihood and odds ratios are calculated from the maximum likelihood estimates. midas facilitates exploratory analysis of heterogeneity, threshold-related variability, methodological quality bias, publication and other precision-related biases. Bayes' nomograms, likelihood-ratio matrices, and probability modifying plots may be derived and used to guide patient-based diagnostic decision making. A dataset of studies evaluating axillary staging performance of positron emission tomography in breast cancer patients is provided for illustration of the omnibus capabilities of midas.meta-analysis, meta-regression, roc curve, mixed models, diagnosis, sensitivity and specificity, Bayes' nomogram, bivariate analysis, publication bias, predictive values, likelihood ratios, heterogeneity, forest plot

FAGAN: Stata module for Fagan's Bayesian nomoigram

fagan creates a plot showing the relationship between the prior probability specified by user over the range 0-1, the likelihood ratio (combination of sensitivity and specificity), and posterior test probability. fagan plots an axis on the left with the prior log-odds, an axis in the middle representing the log likelihood ratio and an axis on the right representing the posterior log-odds. Lines are then drawn from the prior probability on the left through the likelihood ratios in the center and extended to the posterior probabilities on the right.Bayes' nomogram, likelihood ratios, probability revision, post-test probability

UMETA: Stata module for u-statistic-based univariate and multivariate random-effects meta-analysis

The umeta command performs u-statistics-based random-effects meta-analysis on a dataset of univariate, bivariate or trivariate point estimates, sampling variances, and for bivariate or trivariate data, within-study correlations or covariances. The methodology is described in Ma and Mazumdar, Statistics in Medicine (2011).u-statistic, meta-analysis, random effects

Meta-analytical Integration of Diagnostic Accuracy Studies in Stata

This presentation will demonstrate how to perform diagnostic meta-analysis using midas , a user-written macro. midas is is comprehensive program of statistical and graphical routines for undertaking meta-analysis of diagnostic test performance in Stata. Primary data synthesis is performed within the bivariate generalized linear mixed modeling framework. Model specification, estimation and prediction are carried out with gllamm (Rabe-Hesketh et.al, spherical adaptive quadrature). Using the estimated coefficients and variance-covariance matrices, midas calculates the summary operating sensitivity and specificity (with confidence and prediction ellipses) in SROC space. Summary likelihood and odds ratios with relevant heterogeneity statistics are provided. midas facilitates extensive statistical and graphical data synthesis and exploratory analyses of unobserved heterogeneity, covariate effects, publication bias and subgroup analyses. Bayes' nomograms, likelihood ratio matrices and conditional probability plots may be obtained and used to guide clinical decision-making.