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MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.

ABSTRACT: Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose six quantitative quality control measures, covering internal homogeneity of coexpression structure among studies, external consistency of coexpression pattern with pathway database, and accuracy and consistency of differentially expressed gene detection or enriched pathway identification. Each quality control index is defined as the minus log transformed P values from formal hypothesis testing. Principal component analysis biplots and a standardized mean rank are applied to assist visualization and decision. We applied the proposed method to 4 large-scale examples, combining 7 brain cancer, 9 prostate cancer, 8 idiopathic pulmonary fibrosis and 17 major depressive disorder studies, respectively. The identified problematic studies were further scrutinized for potential technical or biological causes of their lower quality to determine their exclusion from meta-analysis. The application and simulation results concluded a systematic quality assessment framework for genomic meta-analysis.

SUBMITTER: Kang DD

PROVIDER: S-EPMC3258120 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.

Kang Dongwan D DD Sibille Etienne E Kaminski Naftali N Tseng George C GC

Nucleic acids research 20111123 2

Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose s ...[more]

PMID: 22116060

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Project description:BackgroundControl-arm mortality varies between acute respiratory distress syndrome (ARDS) RCTs.MethodsWe systematically reviewed ARDS RCTs that commenced recruitment after publication of the American-European Consensus (AECC) definition (MEDLINE, Embase, and Cochrane central register of controlled trials; January 1994 to October 2020). We assessed concordance of RCT inclusion criteria to ARDS consensus definitions and whether exclusion criteria are strongly or poorly justified. We estimated the proportion of between-trial difference in control-arm 28-day mortality explained by the inclusion criteria and RCT design characteristics using meta-regression.ResultsA literature search identified 43 709 records. One hundred and fifty ARDS RCTs were included; 146/150 (97.3%) RCTs defined ARDS inclusion criteria using AECC/Berlin definitions. Deviations from consensus definitions, primarily aimed at improving ARDS diagnostic certainty, frequently related to duration of hypoxaemia (117/146; 80.1%). Exclusion criteria could be grouped by rationale for selection into strongly or poorly justified criteria. Common poorly justified exclusions included pregnancy related, age, and comorbidities (infectious/immunosuppression, hepatic, renal, and human immunodeficiency virus/acquired immunodeficiency syndrome). Control-arm 28-day mortality varied between ARDS RCTs (mean: 29.8% [95% confidence interval: 27.0-32.7%; I2=88.8%; τ2=0.02; P<0.01]), and differed significantly between RCTs with different Pao2:FiO2 ratio inclusion thresholds (26.6-39.9 kPa vs <26.6 kPa; P<0.01). In a meta-regression model, inclusion criteria and RCT design characteristics accounted for 30.6% of between-trial difference (P<0.01).ConclusionsIn most ARDS RCTs, consensus definitions are modified to use as inclusion criteria. Between-RCT mortality differences are mostly explained by the Pao2:FiO2 ratio threshold within the consensus definitions. An exclusion criteria framework can be applied when designing and reporting exclusion criteria in future ARDS RCTs.

Project description:BackgroundDifferent methodological choices such as inclusion/exclusion criteria and analytical models can yield different results and inferences when meta-analyses are performed. We explored the range of such differences, using several methodological choices for indirect comparison meta-analyses to compare nalmefene and naltrexone in the reduction of alcohol consumption as a case study.MethodsAll double-blind randomized controlled trials (RCTs) comparing nalmefene to naltrexone or one of these compounds to a placebo in the treatment of alcohol dependence or alcohol use disorders were considered. Two reviewers searched for published and unpublished studies in MEDLINE (August 2017), the Cochrane Library, Embase, and ClinicalTrials.gov and contacted pharmaceutical companies, the European Medicines Agency, and the Food and Drug Administration. The indirect comparison meta-analyses were performed according to different inclusion/exclusion criteria (based on medical condition, abstinence of patients before inclusion, gender, somatic and psychiatric comorbidity, psychological support, treatment administered and dose, treatment duration, outcome reported, publication status, and risk of bias) and different analytical models (fixed and random effects). The primary outcome was the vibration of effects (VoE), i.e. the range of different results of the indirect comparison between nalmefene and naltrexone. The presence of a "Janus effect" was investigated, i.e. whether the 1st and 99th percentiles in the distribution of effect sizes were in opposite directions.ResultsNine nalmefene and 51 naltrexone RCTs were included. No study provided a direct comparison between the drugs. We performed 9216 meta-analyses for the indirect comparison with a median of 16 RCTs (interquartile range = 12-21) included in each meta-analysis. The standardized effect size was negative at the 1st percentile (- 0.29, favouring nalmefene) and positive at the 99th percentile (0.29, favouring naltrexone). A total of 7.1% (425/5961) of the meta-analyses with a negative effect size and 18.9% (616/3255) of those with a positive effect size were statistically significant (p < 0.05).ConclusionsThe choice of inclusion/exclusion criteria and analytical models for meta-analysis can result in entirely opposite results. VoE evaluations could be performed when overlapping meta-analyses on the same topic yield contradictory result.Trial registrationThis study was registered on October 19, 2016, in the Open Science Framework (OSF, protocol available at https://osf.io/7bq4y/ ).

Project description:Clinical trials are essential parts of a medical study process, but studies are often cancelled due to a lack of participants. Clinical Trial Recruitment Support Systems are systems that help to increase the number of participants by seeking more suitable subjects. The software ATLAS (developed by Observational Health Data Sciences and Informatics) can support the launch of a clinical trial by building cohorts of patients who fulfill certain criteria. The correct use of medical classification systems aiming at clearly defined inclusion and exclusion criteria in the studies is an important pillar of this software. The aim of this investigation was to determine whether ATLAS can be used in a Clinical Trial Recruitment Support System to portray the eligibility criteria of clinical studies. Our analysis considered the number of criteria feasible for integration with ATLAS and identified its strengths and weaknesses. Additionally, we investigated whether nonrepresentable criteria were associated with the utilized terminology systems. We analyzed ATLAS using 223 objective eligibility criteria from 30 randomly selected trials conducted in the last 10 years. In the next step, we selected appropriate ICD, OPS, LOINC, or ATC codes to feed the software. We classified each criterion and study based on its implementation capability in the software, ensuring a clear and logical progression of information. Based on our observations, 51% of the analyzed inclusion criteria were fully implemented in ATLAS. Within our selected example set, 10% of the studies were classified as fully portrayable, and 73% were portrayed to some extent. Additionally, we conducted an evaluation of the software regarding its technical limitations and interaction with medical classification systems. To improve and expand the scope of criteria within a cohort definition in a practical setting, it is recommended to work closely with personnel involved in the study to define the criteria precisely and to carefully select terminology systems. The chosen criteria should be combined according to the specific setting. Additional work is needed to specify the significance and amount of the extracted criteria.

Project description:BACKGROUND: Bacterial inclusion bodies (IBs) are key intermediates for protein production. Their quality affects the refolding yield and further purification. Recent functional and structural studies have revealed that IBs are not dead-end aggregates but undergo dynamic changes, including aggregation, refunctionalization of the protein and proteolysis. Both, aggregation of the folding intermediates and turnover of IBs are influenced by the cellular situation and a number of well-studied chaperones and proteases are included. IBs mostly contain only minor impurities and are relatively homogenous. RESULTS: IBs of alpha-glucosidase of Saccharomyces cerevisiae after overproduction in Escherichia coli contain a large amount of (at least 12 different) major product fragments, as revealed by two-dimensional polyacrylamide gel electrophoresis (2D PAGE). Matrix-Assisted-Laser-Desorption/Ionization-Time-Of-Flight Mass-Spectrometry (MALDI-ToF MS) identification showed that these fragments contain either the N- or the C-terminus of the protein, therefore indicate that these IBs are at least partially created by proteolytic action. Expression of alpha-glucosidase in single knockout mutants for the major proteases ClpP, Lon, OmpT and FtsH which are known to be involved in the heat shock like response to production of recombinant proteins or to the degradation of IB proteins, clpP, lon, ompT, and ftsH did not influence the fragment pattern or the composition of the IBs. The quality of the IBs was also not influenced by the sampling time, cultivation medium (complex and mineral salt medium), production strategy (shake flask, fed-batch fermentation process), production strength (T5-lac or T7 promoter), strain background (K-12 or BL21), or addition of different protease inhibitors during IB preparation. CONCLUSIONS: alpha-glucosidase is fragmented before aggregation, but neither by proteolytic action on the IBs by the common major proteases, nor during downstream IB preparation. Different fragments co-aggregate in the process of IB formation together with the full-length product. Other intracellular proteases than ClpP or Lon must be responsible for fragmentation. Reaggregation of protease-stable alpha-glucosidase fragments during in situ disintegration of the existing IBs does not seem to occur.

Project description:The coronavirus disease 2019 (COVID-19) pandemic that has been ongoing since 2019 is still ongoing and how to control it is one of the international issues to be addressed. Antiviral drugs that reduce the viral load in terms of reducing the risk of secondary infection are important. For the general control of emerging infectious diseases, establishing an efficient method to evaluate candidate therapeutic agents will lead to a rapid response. We evaluated clinical trial designs for viral entry inhibitors that have the potential to be effective pre-exposure prophylactic drugs in addition to reducing viral load after infection. We used a previously developed simulation of clinical trials based on a mathematical model of within-host viral infection dynamics to evaluate sample sizes in clinical trials of viral entry inhibitors against COVID-19. We assumed four measures as outcomes, namely change in log10-transformed viral load from symptom onset, PCR positive ratio, log10-transformed viral load, and cumulative viral load, and then sample sizes were calculated for drugs with 99 % and 95 % antiviral efficacy. Consistent with previous results, we found that sample sizes could be dramatically reduced for all outcomes used in an analysis by adopting inclusion/exclusion criteria such that only patients in the early post-infection period would be included in a clinical trial. A comparison of sample sizes across outcomes demonstrated an optimal measurement schedule associated with the nature of the outcome measured for the evaluation of drug efficacy. In particular, the sample sizes calculated from the change in viral load and from viral load tended to be small when measurements were taken at earlier time points after treatment initiation. For the cumulative viral load, the sample size was lower than that from the other outcomes when the stricter inclusion/exclusion criteria to include patients whose time since onset is earlier than 2 days was used. We concluded that the design of efficient clinical trials should consider the inclusion/exclusion criteria and measurement schedules, as well as outcome selection based on sample size, personnel and budget needed to conduct the trial, and the importance of the outcome regarding the medical and societal requirements. This study provides insights into clinical trial design for a variety of situations, especially addressing infectious disease prevalence and feasible trial sizes. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Project description:BackgroundThe European Association for the Study of the Liver (EASL) criteria and the modified Response Evaluation Criteria in Solid Tumors (mRECIST) are currently adopted to evaluate radiological response in patients affected by HCC and treated with loco-regional procedures. Several studies explored the validity of these measurements in predicting survival but definitive data are still lacking.AimTo conduct a systematic review of studies exploring mRECIST and EASL criteria usefulness in predictive radiological response in HCC undergoing loco-regional therapies and their validity in predicting survival.MethodsA comprehensive search of the literature was performed in electronic databases EMBASE, MEDLINE, COCHRANE LIBRARY, ASCO conferences and EASL conferences up to June 10, 2014. Our overall search strategy included terms for HCC, mRECIST, and EASL. Loco-regional procedures included transarterial embolization (TAE), transarterial chemoembolization (TACE) and cryoablation. Inter-method agreement between EASL and mRECIST was assessed using the k coefficient. For each criteria, overall survival was described in responders vs. non-responders patients, considering all target lesions response.ResultsAmong 18 initially found publications, 7 reports including 1357 patients were considered eligible. All studies were published as full-text articles. Proportion of responders according to mRECIST and EASL criteria was 62.4% and 61.3%, respectively. In the pooled population, 1286 agreements were observed between the two methods (kappa statistics 0.928, 95% confidence interval 0.912-0.944). HR for overall survival (responders versus non responders) according to mRECIST and EASL was 0.39 (95% confidence interval 0.26-0.61, p<0.0001) and 0.38 (95% confidence interval 0.24-0.61, p<0.0001), respectively.ConclusionIn this literature-based meta-analysis, mRECIST and EASL criteria showed very good concordance in HCC patients undergoing loco-regional treatments. Objective response according to both criteria confirms a strong prognostic value in terms of overall survival. This prognostic value appears to be very similar between the two criteria.

Dataset Information

MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.

Publications

MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.

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