Goodness-of-fit test for meta-analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Chen Z
PROVIDER: S-EPMC4655309 | biostudies-other | 2015 Nov
REPOSITORIES: biostudies-other
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Project description:BackgroundGastric and colorectal cancer (CRC) are both one of the most common cancers worldwide. In many countries fecal immunochemical tests (FIT)-based CRC screening has been implemented. We investigated if FIT can also be applied for detection of H. pylori, the main risk factor for gastric cancer.MethodsThis prospective study included participants over 18 years of age referred for urea breath test (UBT). Patients were excluded if they had used antibiotics/bismuth in the past 4 weeks, or a proton pomp inhibitor (PPI) in the past 2 weeks. Participants underwent UBT, ELISA stool antigen test in standard feces tube (SAT), ELISA stool antigen test in FIT tube (Hp-FIT), and blood sampling, and completed a questionnaire on user friendliness. UBT results were used as reference.ResultsA total of 182 patients were included (37.4% male, median age 52.4 years (IQR 22.4)). Of these, 60 (33.0%) tested H. pylori positive. SAT and Hp-FIT showed comparable overall accuracy 71.1% (95%CI 63.2-78.3) vs. 77.6% (95%CI 70.4-83.8), respectively (p = 0.97). Sensitivity of SAT was 91.8% (95%CI 80.4-97.7) versus 94.2% (95%CI 84.1-98.9) of Hp-FIT (p = 0.98). Serology scored low with an overall accuracy of 49.7% (95%CI 41.7-57.7). Hp-FIT showed the highest overall user convenience.ConclusionsFIT can be used with high accuracy and sensitivity for diagnosis of H. pylori and is rated as the most convenient test. Non-invasive Hp-FIT test is highly promising for combined upper and lower gastrointestinal (pre-) cancerous screening. Further research should investigate the clinical implications, benefits and cost-effectiveness of such an approach.
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Project description:Improved respirator test headforms are needed to measure the fit of N95 filtering facepiece respirators (FFRs) for protection studies against viable airborne particles. A Static (i.e., non-moving, non-speaking) Advanced Headform (StAH) was developed for evaluating the fit of N95 FFRs. The StAH was developed based on the anthropometric dimensions of a digital headform reported by the National Institute for Occupational Safety and Health (NIOSH) and has a silicone polymer skin with defined local tissue thicknesses. Quantitative fit factor evaluations were performed on seven N95 FFR models of various sizes and designs. Donnings were performed with and without a pre-test leak checking method. For each method, four replicate FFR samples of each of the seven models were tested with two donnings per replicate, resulting in a total of 56 tests per donning method. Each fit factor evaluation was comprised of three 86-sec exercises: "Normal Breathing" (NB, 11.2 liters per min (lpm)), "Deep Breathing" (DB, 20.4 lpm), then NB again. A fit factor for each exercise and an overall test fit factor were obtained. Analysis of variance methods were used to identify statistical differences among fit factors (analyzed as logarithms) for different FFR models, exercises, and testing methods. For each FFR model and for each testing method, the NB and DB fit factor data were not significantly different (P > 0.05). Significant differences were seen in the overall exercise fit factor data for the two donning methods among all FFR models (pooled data) and in the overall exercise fit factor data for the two testing methods within certain models. Utilization of the leak checking method improved the rate of obtaining overall exercise fit factors ?100. The FFR models, which are expected to achieve overall fit factors ? 100 on human subjects, achieved overall exercise fit factors ? 100 on the StAH. Further research is needed to evaluate the correlation of FFRs fitted on the StAH to FFRs fitted on people. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: a file providing detailed information on the advanced head form design and fabrication process.].
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Project description:Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.
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Project description:Goodness of fit evaluation should be a natural step in assessing and reporting dose-response meta-analyses from aggregated data of binary outcomes. However, little attention has been given to this topic in the epidemiological literature, and goodness of fit is rarely, if ever, assessed in practice. We briefly review the two-stage and one-stage methods used to carry out dose-response meta-analyses. We then illustrate and discuss three tools specifically aimed at testing, quantifying, and graphically evaluating the goodness of fit of dose-response meta-analyses. These tools are the deviance, the coefficient of determination, and the decorrelated residuals-versus-exposure plot. Data from two published meta-analyses are used to show how these three tools can improve the practice of quantitative synthesis of aggregated dose-response data. In fact, evaluating the degree of agreement between model predictions and empirical data can help the identification of dose-response patterns, the investigation of sources of heterogeneity, and the assessment of whether the pooled dose-response relation adequately summarizes the published results. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
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Project description:In this manuscript, the applicability of the Hausman test to the evaluation of item response models is investigated. The Hausman test is a general test of model fit. The test assesses whether for a model in question the parameter estimates of two different estimators coincide. The test can be implemented for item response models by comparing the parameter estimates of the marginal maximum likelihood estimator with the corresponding parameter estimates of a limited information estimator. For a correctly specified item response model, the difference of the two estimates is normally distributed around zero. The Hausman test can be used for the evaluation of item fit and global model fit. The performance of the test is evaluated in a simulation study. The simulation study suggests that the implemented versions of the test adhere to the nominal Type-I error rate well in samples of 1000 test takers and more. The test is also capable to detect misspecified item characteristic functions, but lacks power to detect violations of the conditional independence assumption.
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Project description:Coarse structural nested mean models are tools to estimate treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified overidentification restrictions tests for evaluating a treatment effect model, and show that our test statistic is doubly-robust in the sense that, with a correct treatment effect model, the test has the correct type-I error if either the treatment initiation model or a nuisance regression outcome model is correctly specified. In a simulation study we show that the test has correct type-I error and can detect model misspecification. We use the test to study how the timing of antiretroviral treatment initiation after HIV infection predicts the effect of one year of treatment in HIV-positive patients with acute and early infection.
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Project description:BackgroundSystematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis.ResultsMeta-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.ConclusionMeta-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.
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Project description:The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain the type-I error rate at the exome-wide level of 2.5 × 10-6 . Further simulations under different models of association show that Meta-MultiSKAT can improve the power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.
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