A statistical method was used for the meta-analysis of tests for latent TB in the absence of a gold standard, combining random-effect and latent-class methods to estimate test accuracy

2010 ◽  
Vol 63 (3) ◽  
pp. 257-269 ◽  
Author(s):  
Mohsen Sadatsafavi ◽  
Neal Shahidi ◽  
Fawziah Marra ◽  
Mark J. FitzGerald ◽  
Kevin R. Elwood ◽  
...  
Keyword(s):  
Statistical Method ◽  
Gold Standard ◽  
Latent Class ◽  
Meta Analysis ◽  
Random Effect ◽  
Test Accuracy ◽  
Latent Tb ◽  
Latent Class Methods

scholarly journals Statistical modeling and verification for the synthesis of median survival time in multilevel meta-analysis of survival data

2015 ◽  
Vol 1 (1) ◽  
pp. 25 ◽  
Author(s):  
Jiajie Zang ◽  
Jinfang Xu ◽  
Chun Xiang ◽  
Shurong Zou ◽  
Jia He
Keyword(s):  
Median Survival ◽  
Survival Data ◽  
Gold Standard ◽  
Multilevel Models ◽  
Meta Analysis ◽  
Random Effect ◽  
Simulated Data ◽  
Multilevel Methods ◽  
Survival Times ◽  
Gold Standards

Objective: Past meta-analyses of survival data have been over simplistic because of restricting to proportional hazard model, lackof intuitive results, and potential omitting information. These had the potential to recommend sub-optimal policies. Here wedevelop multilevel methods for combining median survival times (MSTs) for meta-analysis of survival data.Methods: We used simulated data to test and verify the synthesis model we developed. We generated the study-level data to fit multilevel model and individual patient data to calculate gold standard. We then used the Bland-Altman method and the relativechange from the gold standard to evaluate the fit of the models. Examples were presented in a meta-analysis to illustrate the feasibility of the models.Results: We generated eight sets of simulated datasets of different number of studies and sample size. We established themulti-level fixed and random effect models to pool the MSTs. The test of the fitness of the model showed that the means ofdifference (d) for all simulated datasets between the calculated values and the gold standards are no more than -0.230 and -0.329days and the largest 95% CIs of d are -3.823 3.364 and -3.936 3.278 days respectively. At least 91.9% and 92.3% of the difference between the estimated values and the gold standards are small. The real examples of a meta-analysis were provided with combined MSTs along with pooled HR.Conclusions: The multilevel models of synthesizing MSTs in survival data AD meta-analysis were verified with good fitting effects and provide more intuitive information.

An Epidemiologic Critique of Current Microbial Risk Assessment Practices: The Importance of Prevalence and Test Accuracy Data

2004 ◽  
Vol 67 (9) ◽  
pp. 2000-2007 ◽  
Author(s):  
IAN A. GARDNER
Keyword(s):  
Risk Assessment ◽  
Bayesian Methods ◽  
Gold Standard ◽  
Latent Class ◽  
Test Accuracy ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
True Prevalence ◽  
Apparent Prevalence ◽  
Prevalence Estimates

Data deficiencies are impeding the development and validation of microbial risk assessment models. One such deficiency is the failure to adjust test-based (apparent) prevalence estimates to true prevalence estimates by correcting for the imperfect accuracy of tests that are used. Such adjustments will facilitate comparability of data from different populations and from the same population over time as tests change and the unbiased quantification of effects of mitigation strategies. True prevalence can be estimated from apparent prevalence using frequentist and Bayesian methods, but the latter are more flexible and can incorporate uncertainty in test accuracy and prior prevalence data. Both approaches can be used for single or multiple populations, but the Bayesian approach can better deal with clustered data, inferences for rare events, and uncertainty in multiple variables. Examples of prevalence inferences based on results of Salmonella culture are presented. The opportunity to adjust test-based prevalence estimates is predicated on the availability of sensitivity and specificity estimates. These estimates can be obtained from studies using archived gold standard (reference) samples, by screening with the new test and follow-up of test-positive and test-negative samples with a gold standard test, and by use of latent class methods, which make no assumptions about the true status of each sampling unit. Latent class analysis can be done with maximum likelihood and Bayesian methods, and an example of their use in the evaluation of tests for Toxoplasma gondii in pigs is presented. Guidelines are proposed for more transparent incorporation of test data into microbial risk assessments.

scholarly journals Role of diffusional kurtosis imaging in grading of brain gliomas: A diagnostic test accuracy systematic review and meta-analysis

2019 ◽  
Vol 21 (Supplement_4) ◽  
pp. iv12-iv12
Author(s):  
Gehad Abdalla ◽  
Eser Sanverdi ◽  
Pedro M Machado ◽  
Joey S W Kwong ◽  
Jasmina Panovska-Griffiths ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Test Accuracy ◽  
Low Grade ◽  
Moderate Degree ◽  
Diffusional Kurtosis Imaging ◽  
Low Grade Gliomas ◽  
Effect Model

Abstract Aim and objectives We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. Methods and materials A review protocol was developed according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P), registered in the international prospective register of systematic reviews, PROSPERO and published. Literature search in 4 databases was performed using the keywords “glioma” and “diffusional kurtosis”.After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool.Data extraction was done in a pre-designed pro forma.Reported sensitivities and specificities were used to construct 2x2 tables and paired forest plots using the Review Manager (RevMan®) software.Random-effect model was pursued using the hierarchical summary receiver operator characteristics. Results Initially, 216 hits were retrieved. Considering duplicates and inclusion criteria; 23 articles were eligible for full-text reading. Ultimately, 19 studies were deemed to be eligible for final inclusion. Quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis; summary ROC curve showed pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI: 0.78 - 0.92) in high/low grade gliomas’ differentiation.A mean difference in Mean Kurtosis (MK) value between HGG and LGG of 0.22 [95% CI: 0.25 - 0.19] was illustrated (p value = 0.0014) and a moderate degree of heterogeneity (I²= 73.8%). Conclusion DKI shows good diagnostic accuracy in high/low grade gliomas’ differentiation; which might qualify it to be part of the routine clinical practice, however; further evidence is deemed for technique standardization.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Prevalence and risk factors for antibiotic utilization in Chinese children

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shasha Guo ◽  
Qiang Sun ◽  
Xinyang Zhao ◽  
Liyan Shen ◽  
Xuemei Zhen
Keyword(s):  
Risk Factors ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Healthcare Research ◽  
Chinese Children ◽  
Rural Setting ◽  
Antibiotic Prescribing ◽  
Antibiotic Utilization ◽  
At Home

Abstract Background Antibiotic resistance poses a significant threat to public health globally. Irrational utilization of antibiotics being one of the main reasons of antibiotic resistant. Children as a special group, there's more chance of getting infected. Although most of the infection is viral in etiology, antibiotics still are the most frequently prescribed medications for children. Therefore, high use of antibiotics among children raises concern about the appropriateness of antibiotic prescribing. This systematic review aims to measuring prevalence and risk factors for antibiotic utilization in children in China. Methods English and Chinese databases were searched to identify relevant studies evaluating the prevalence and risk factors for antibiotic utilization in Chinese children (0-18 years), which were published between 2010 and July 2020. A Meta-analysis of prevalence was performed using random effect model. The Agency for Healthcare Research and Quality (AHRQ) and modified Jadad score was used to assess risk of bias of studies. In addition, we explored the risk factors of antibiotic utilization in Chinese children using qualitative analysis. Results Of 10,075 studies identified, 98 eligible studies were included after excluded duplicated studies. A total of 79 studies reported prevalence and 42 studies reported risk factors for antibiotic utilization in children. The overall prevalence of antibiotic utilization among outpatients and inpatients were 63.8% (35 studies, 95% confidence interval (CI): 55.1-72.4%), and 81.3% (41 studies, 95% CI: 77.3-85.2%), respectively. In addition, the overall prevalence of caregiver’s self-medicating of antibiotics for children at home was 37.8% (4 studies, 95% CI: 7.9-67.6%). The high prevalence of antibiotics was associated with multiple factors, while lacking of skills and knowledge in both physicians and caregivers was the most recognized risk factor, caregivers put pressure on physicians to get antibiotics and self-medicating with antibiotics at home for children also were the main factors attributed to this issue. Conclusion The prevalence of antibiotic utilization in Chinese children is heavy both in hospitals and home. It is important for government to develop more effective strategies to improve the irrational use of antibiotic, especially in rural setting.

scholarly journals A meta-analysis comparing short-term weight and cardiometabolic changes between olanzapine/samidorphan and olanzapine

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Manit Srisurapanont ◽  
Sirijit Suttajit ◽  
Surinporn Likhitsathian ◽  
Benchalak Maneeton ◽  
Narong Maneeton
Keyword(s):  
Weight Gain ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Dropout Rates ◽  
Lower Percentage ◽  
Current Evidence ◽  
Weight Changes ◽  
Short Term ◽  
Short Term Treatment

AbstractThis study compared weight and cardiometabolic changes after short-term treatment of olanzapine/samidorphan and olanzapine. Eligible criteria for an included trial were ≤ 24 weeks, randomized controlled trials (RCTs) that compared olanzapine/samidorphan and olanzapine treatments in patients/healthy volunteers and reported weight or cardiometabolic outcomes. Three databases were searched on October 31, 2020. Primary outcomes included weight changes and all-cause dropout rates. Standardized mean differences (SMDs) and risk ratios (RRs) were computed and pooled using a random-effect model. This meta-analysis included four RCTs (n = 1195). The heterogeneous data revealed that weight changes were not significantly different between olanzapine/samidorphan and olanzapine groups (4 RCTs, SDM = − 0.19, 95% CI − 0.45 to 0.07, I2 = 75%). The whole-sample, pooled RR of all-cause dropout rates (4 RCTs, RR = 1.02, 95% CI 0.84 to 1.23, I2 = 0%) was not significant different between olanzapine/samidorphan and olanzapine groups. A lower percentage of males and a lower initial body mass index were associated with the greater effect of samidorphan in preventing olanzapine-induced weight gain. Current evidence is insufficient to support the use of samidorphan to prevent olanzapine-induced weight gain and olanzapine-induced cardiometabolic abnormalities. Samidorphan is well accepted by olanzapine-treated patients.

scholarly journals Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis

QJM ◽  
2021 ◽  
Author(s):  
Marco Zuin ◽  
Gianluca Rigatelli ◽  
Claudio Bilato ◽  
Carlo Cervellati ◽  
Giovanni Zuliani ◽  
...  
Keyword(s):  
Random Effects ◽  
Mortality Risk ◽  
Meta Analysis ◽  
Random Effect ◽  
Final Analysis ◽  
Short Term ◽  
Random Effects Models ◽  
Pooled Prevalence ◽  
Statistical Heterogeneity ◽  
Trim And Fill

Abstract Objective The prevalence and prognostic implications of pre-existing dyslipidaemia in patients infected by the SARS-CoV-2 remain unclear. To perform a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with pre-existing dyslipidaemia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on dyslipidaemia among COVID-19 survivors and non-survivors. The pooled prevalence of dyslipidaemia was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I2 statistic. Results Eighteen studies, enrolling 74.132 COVID-19 patients [mean age 70.6 years], met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 17.5% of cases (95% CI: 12.3-24.3%, p < 0.0001), with high heterogeneity (I2=98.7%). Pre-existing dyslipidaemia was significantly associated with higher risk of short-term death (OR: 1.69, 95% CI: 1.19-2.41, p = 0.003), with high heterogeneity (I2=88.7%). Due to publication bias, according to the Trim-and-Fill method, the corrected random-effect ORs resulted 1.61, 95% CI 1.13-2.28, p < 0.0001 (one studies trimmed). Conclusions Dyslipidaemia represents a major comorbidity in about 18% of COVID-19 patients but it is associated with a 60% increase of short-term mortality risk.

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