Quality Assurance and Quality Control in the Global Trachoma Mapping Project.
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ABSTRACT:
SUBMITTER: Solomon AW
PROVIDER: S-EPMC6159583 | biostudies-literature | 2018 Oct
REPOSITORIES: biostudies-literature
Publications
Quality Assurance and Quality Control in the Global Trachoma Mapping Project.
Solomon Anthony W AW Willis Rebecca R Pavluck Alexandre L AL Alemayehu Wondu W Bakhtiari Ana A Bovill Sarah S Chu Brian K BK Courtright Paul P Dejene Michael M Downs Philip P Flueckiger Rebecca M RM Haddad Danny D Hooper P J PJ Kalua Khumbo K Kebede Biruck B Kello Amir Bedri AB Macleod Colin K CK McCullagh Siobhain S Millar Tom T Mpyet Caleb C Ngondi Jeremiah J Nwobi Benjamin B Olobio Nicholas N Onyebuchi Uwazoeke U Rotondo Lisa A LA Sarr Boubacar B Shafi Oumer O Sokana Oliver O West Sheila K SK Foster Allen A For The Global Trachoma Mapping Project
The American journal of tropical medicine and hygiene 20181001 4
In collaboration with the health ministries that we serve and other partners, we set out to complete the multiple-country Global Trachoma Mapping Project. To maximize the accuracy and reliability of its outputs, we needed in-built, practical mechanisms for quality assurance and quality control. This article describes how those mechanisms were created and deployed. Using expert opinion, computer simulation, working groups, field trials, progressively accumulated in-project experience, and externa ...[more]
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Project description:BackgroundThe Global Trachoma Mapping Project (GTMP) was implemented with the aim of completing the baseline map of trachoma globally. Over 2.6 million people were examined in 1,546 districts across 29 countries between December 2012 and January 2016. The aim of the analysis was to estimate the unit cost and to identify the key cost drivers of trachoma prevalence surveys conducted as part of GTMP.Methodology and principal findingsIn-country and global support costs were obtained using GTMP financial records. In-country expenditure was analysed for 1,164 districts across 17 countries. The mean survey cost was $13,113 per district [median: $11,675; IQR = $8,365-$14,618], $17,566 per evaluation unit [median: $15,839; IQR = $10,773-$19,915], $692 per cluster [median: $625; IQR = $452-$847] and $6.0 per person screened [median: $4.9; IQR = $3.7-$7.9]. Survey unit costs varied substantially across settings, and were driven by parameters such as geographic location, demographic characteristics, seasonal effects, and local operational constraints. Analysis by activities showed that fieldwork constituted the largest share of in-country survey costs (74%), followed by training of survey teams (11%). The main drivers of in-country survey costs were personnel (49%) and transportation (44%). Global support expenditure for all surveyed districts amounted to $5.1m, which included grant management, epidemiological support, and data stewardship.ConclusionThis study provides the most extensive analysis of the cost of conducting trachoma prevalence surveys to date. The findings can aid planning and budgeting for future trachoma surveys required to measure the impact of trachoma elimination activities. Furthermore, the results of this study can also be used as a cost basis for other disease mapping programmes, where disease or context-specific survey cost data are not available.
| S-EPMC5675456 | biostudies-literature
Project description:Microarray technology has had a profound impact on gene expression research. Some studies have questioned whether similar expression results are obtained when the same RNA samples are analyzed on different platforms. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and analysis issues. We demonstrate the consistency of results within a platform across test sites as well as the high level of cross-platform concordance in terms of genes identified as differentially expressed. The MAQC study provides a rich resource that will help build consensus on the use of microarrays in research, clinical and regulatory settings. Manuscripts related to the MAQC project have been published in Nature Biotechnology, 24(9), September, 2006. More information about the MAQC project can be found at http://edkb.fda.gov/MAQC/. Keywords: Cross-platform comparison
2006-09-08 | GSE5350 | GEO
Project description:BackgroundDeaths attributable to fentanyl (FEN, a synthetic opioid) are high in Appalachia and highest in West Virginia. The goal of the study was to determine FEN prevalence among specimens submitted for definitive opioid testing and monitor responses to provider notifications of unexpected FEN findings during Q1 2020.MethodsAll definitive opioid test data were reviewed daily for FEN signatures in Q1 2020. Unexpected FEN results were communicated to providers and monitored for 10 days to record actions taken. Prevalence data were categorized. Behavioral Medicine (BMED) leaders analyzed January data and implemented FEN screening in the clinic. BMED Q1 clinic visits and order volumes for drug screens were reviewed after Q1.ResultsFEN positivity was 11% in Q1; >60% of findings were unexpected. Actions were taken for 54% of notifications in January but only 18% in March. Notifications required 70 hours of combined laboratory effort each month. BMED providers ordered 44% of definitive opioid tests and 69% of definitive FEN tests. Data prompted the addition of FEN to routine drug screen panels in the laboratory, and a 10% random FEN screening rate in the BMED opioid use disorder clinics (COAT).ConclusionsPrevalence of FEN positivity was higher than initially expected, even for this region in Appalachia. Expanded presence of FEN screening should assist BMED providers with clinical efforts and help identify patients in need of intervention/therapy.
| S-EPMC8415574 | biostudies-literature
Project description:MicroArray Quality Control (MAQC) Project
| PRJNA96213 | ENA
Project description:Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies (GWAS). This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium test P-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis to SNP selection. The methods are illustrated with examples from the "Gene Environment Association Studies" (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of GWAS.
| S-EPMC3061487 | biostudies-literature
Project description:The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models The second phase of the MicroArray Quality Control (MAQC-II) project evaluated common practices for developing and validating microarray-based models aimed at predicting toxicological and clinical endpoints. The purposes of the MAQC-II project were to survey approaches in genomic model development in an attempt to understand sources of variability in prediction performance, and to assess the influences of endpoint signal strength in data. Thirty-six teams developed classifiers for 13 diverse endpoints -- some easy, some difficult to predict, from six relatively large training data sets -- three preclinical (toxicogenomics) and three clinical. By providing the same data sets to many organizations for analysis, but not restricting their data analysis protocols (DAPs), the project made it possible to evaluate to what extent, if any, results depend on the team that performs the analysis. These analyses collectively produced >18,000 models that were challenged by independent and blinded validation sets generated for MAQC-II. The cross-validated performance estimates for models developed under good practices are predictive of the blinded validation performance. The achievable prediction performance is largely determined by the intrinsic predictability of the endpoint, and simple data analysis methods often perform as well as more complicated approaches. Multiple models of comparable performance can be developed for a given endpoint and the stability of gene lists correlates with endpoint predictability. Importantly, similar conclusions were reached when >12,000 new models were generated by swapping the original training and validation sets. Description of six data sets including 13 prediction endpoints: (Summarized in GSE16716_MAQC-II_Datasets_Overview.pdf attached as supplementary file. For more details, see the MAQC-II main paper and its references for individual dataset.) The MAQC-II predictive modeling was limited to binary classification problems; therefore, continuous endpoint values such as overall survival (OS) and event-free survival (EFS) times were dichotomized using a "milestone" cutoff of censor data. Prediction endpoints were chosen to span a wide range of prediction difficulty. Two endpoints, H (CPS1) and L (NEP_S), representing the gender of the patients, were used as positive control endpoints, since they are easily predictable by microarrays. Two other endpoints, I (CPS1) and M (NEP_R), representing randomly assigned class labels, were designed to serve as negative control endpoints, since they are not supposed to be predictable. Data analysis teams were not aware of the characteristics of endpoints H, I, L, and M until their swap prediction results had been submitted. If a data analysis protocol did not yield models to accurately predict endpoints H and L, or if a data analysis protocol claims to be able to yield models to accurately predict endpoints I and M, something must have gone wrong. The Hamner data set (endpoint A) was provided by The Hamner Institutes for Health Sciences (Research Triangle Park, NC, USA). The study objective was to apply microarray gene expression data from the lung of female B6C3F1 mice exposed to a 13-week treatment of chemicals to predict increased lung tumor incidence in the 2-year rodent cancer bioassays of the National Toxicology Program. If successful, the results may form the basis of a more efficient and economical approach for evaluating the carcinogenic activity of chemicals. Microarray analysis was performed using Affymetrix Mouse Genome 430 2.0 arrays on three to four mice per treatment group, and a total of 70 mice were analyzed and used as the MAQC-II's training set. Additional data from another set of 88 mice were collected later and provided as the MAQC-II's external validation set. The Iconix data set (endpoint B) was provided by Iconix Biosciences, Inc. (Mountain View, CA, USA). The study objective was to assess, upon short term exposure, hepatic tumor induction by non-genotoxic chemicals, since there are currently no accurate and well-validated short-term tests to identify non-genotoxic hepatic tumorigens, thus necessitating an expensive 2-year rodent bioassay before a risk assessment can begin. The training set consists of hepatic gene expression data from 216 male Sprague-Dawley rats treated for 5 days with one of 76 structurally and mechanistically diverse nongenotoxic hepatocarcinogens and non-hepatocarcinogens. The validation set consists of 201 male Sprague-Dawley rats treated for 5 days with one of 68 structurally and mechanistically diverse non-genotoxic hepatocarcinogens and non- hepatocarcinogens. Gene expression data were generated using the Amersham Codelink Uniset Rat 1 Bioarray (GE HealthCare, Piscataway, NJ). The separation of the training set and validation set was based on the time when the microarray data were collected; i.e., microarrays processed earlier in the study were used as training and those processed later were used as validation. The NIEHS data set (endpoint C) was provided by the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (Research Triangle Park, NC, USA). The study objective was to use microarray gene expression data acquired from the liver of rats exposed to hepatotoxicants to build classifiers for prediction of liver necrosis. The gene expression "compendium" data set was collected from 418 rats exposed to one of eight compounds (1,2- dichlorobenzene, 1,4-dichlorobenzene, bromobenzene, monocrotaline, N-nitrosomorpholine, thioacetamide, galactosamine, and diquat dibromide). All eight compounds were studied using standardized procedures, i.e. a common array platform (Affymetrix Rat 230 2.0 microarray), experimental procedures and data retrieving and analysis processes. Briefly, for each compound, four to six male, 12 week old F344 rats were exposed to a low dose, mid dose(s) and a high dose of the toxicant and sacrificed at 6, 24 and 48 hrs later. At necropsy, liver was harvested for RNA extraction, histopathology, and clinical chemistry assessments. 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Endpoint E is the clinical estrogen-receptor status as established by immunohistochemistry. RNA extraction and gene expression profiling were performed in multiple batches over time using Affymetrix U133A microarrays. Genomic analysis of a subset of this sequentially accrued patient population were reported previously. For each endpoint, the first 130 cases were used as a training set and the next 100 cases were used as an independent validation set. The multiple myeloma (MM) data set (endpoints F, G, H, and I) was contributed by the Myeloma Institute for Research and Therapy at the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA). Gene expression profiling of highly purified bone marrow plasma cells was performed in newly diagnosed patients with MM. The training set consisted of 340 cases enrolled on total therapy 2 (TT2) and the validation set comprised 214 patients enrolled in total therapy 3 (TT3). Plasma cells were enriched by anti-CD138 immunomagnetic bead selection of mononuclear cell fractions of bone marrow aspirates in a central laboratory. All samples applied to the microarray contained more than 85% plasma cells as determined by 2-color flow cytometry (CD38+ and CD45-/dim) performed after selection. Dichotomized overall survival (OS) and eventfree survival (EFS) were determined based on a two-year milestone cutoff. A gene expression model of high-risk multiple myeloma was developed and validated by the data provider and later on validated in three additional independent data sets. The neuroblastoma (NB) data set (endpoints J, K, L, and M) was contributed by the Children's Hospital of the University of Cologne, Germany. Tumor samples were checked by a pathologist prior to RNA isolation; only samples with =60% tumor content were utilized and total RNA was isolated from ~50mg of snap-frozen neuroblastoma tissue obtained before chemotherapeutic treatment. First, 502 pre-existing 11K Agilent dye-flipped, dual-color replicate profiles for 251 patients were provided. Of these, profiles of 246 neuroblastoma samples passed an independent MAQC-II quality assessment by majority decision and formed the MAQC-II training data set. Subsequently, 514 dyeflipped dual-color 11K replicate profiles for 256 independent neuroblastoma tumor samples were generated and profiles for 253 samples were selected to form the MAQC-II validation set. Of note, for one patient of the validation set, two different tumor samples were analyzed utilizing both versions of the 2x11K microarray (see below). All dual-color gene-expression of the MAQC-II training set were generated using a customized 2x11K neuroblastoma-related microarray. Furthermore, 20 patients of the MAQC-II validation set were also profiled utilizing this microarray. Dual-color profiles of the remaining patients of the MAQC-II validation set were performed using a slightly revised version of the 2x11K microarray. This version V2.0 of the array comprised 200 novel oligonucleotide probes whereas 100 oligonucleotide probes of the original design were removed due to consistent low expression values (near background) observed in the training set profiles. These minor modifications of the microarray design resulted in a total of 9,986 probes present on both versions of the 2x11K microarray. The experimental protocol did not differ between both sets and gene-expression profiles were performed as described. Furthermore, single-color geneexpression profiles were generated for 478/499 neuroblastoma samples of the MAQC-II dual-color training and validation sets (training set 244/246; validation set 234/253). For the remaining 21 samples no single-color data were available, due to either shortage of tumor material of these patients (n=15), poor experimental quality of the generated single-color profiles (n=5), or correlation of one single-color profile to two different dual-color profiles for the one patient profiled with both versions of the 2x11K microarrays (n=1). Single-color gene-expression profiles were generated using customized 4x44K oligonucleotide microarrays produced by Agilent Technologies (Palo Alto, CA, USA). These 4x44K microarrays included all probes represented by Agilent's Whole Human Genome Oligo Microarray and all probes of the version V2.0 of the 2x11K customized microarray that were not present in the former probe set. Labeling and hybridization was performed following the manufacturer's protocol as described. This SuperSeries is composed of the SubSeries listed below.
2010-02-04 | GSE16716 | GEO
Project description:To determine the conclusive integrity in the Shiraz Cohort Heart Study (SCHS) project, management began quality assurance (QA) and quality control (QC) of the collected data throughout the study end-points. The QA is a focused process that prevents and detects data collection errors and verification of intended requirements in the SCHS. The QC is a subset of QA intended to capture errors in processing data through testing and preventive processes to identify problems, defects, or intended requirements. SCHS involved 10,000 males and females aged 40-70 over a 10-year follow-up period with cardiovascular diseases (CVDs) in the city of Shiraz, Iran. The study measured events and access to preventive care in Shiraz city. The SCHS identified unique barriers to select national study models in developing standardized measures related to variations in ethnicity, religion, cross-cultural considerations, and others. A suggested response to this problem was to develop a mechanism to standardize elements of the questionnaire, study design, and method of administration. This action was based on the geographically normal distribution of the Family Physician Health and Medical Services in Shiraz. Important QA and QC decisions were developed and adopted in the construction of the SCHS and follow-up to ensure conclusive integrity.
| S-EPMC7441427 | biostudies-literature
Project description:This study aimed to measure dose in a scanning carbon beam-irradiation field with high sampling rate that is sufficient for identifying spots and verifying the characteristics of the scanning beam that cannot generally be derived from the dose. To identify the spot, which is the smallest control unit of beam information during irradiation, effecting measurements with a sampling time of 10 μs or shorter is necessary. The provided dose within a specific time is referred to as time-resolved dose (TRD). We designed a circuit for time-resolved dosimetry using a fast-data acquisition unit (SL1000, Yokogawa Electric Co.), which can measure 100 000 samples per second. Moreover, we used converters to enable a connection between an ionization chamber (IC) and the SL1000. TRD was measured successfully using point irradiation and two-dimensional irradiation patterns in a scanned carbon beam. Based on the moving time of the spot obtained from the position monitor, the dose delivered to the IC from each spot position (spot dose) was interpreted. The spot dose, displacement of the chamber from the beam's center axis, and beam size were derived using TRD and position monitor outputs, which were measured concurrent with TRD. Spot dose up to a radius of 8 mm area from the IC's center were observed. Using the spot-dose equations and simulation, we show that the spot dose of each position varies depending on the beam size and displacement of the IC's center from the beam's center axis. We devise an interpretation method for the characteristics that may apply to quality assurance, such as the verification of the trend for the beam axis and isocenter to coincide, as well as beam-size verification.
| S-EPMC8598136 | biostudies-literature
Project description:Meiotic chromosome segregation relies on homologous chromosomes being linked by at least one crossover, the obligate crossover. Homolog pairing, synapsis and meiosis specific DNA repair mechanisms are required for crossovers but how they are coordinated to promote the obligate crossover is not well understood. PCH-2 is a highly conserved meiotic AAA+-ATPase that has been assigned a variety of functions; whether these functions reflect its conserved role has been difficult to determine. We show that PCH-2 restrains pairing, synapsis and recombination in C. elegans. Loss of pch-2 results in the acceleration of synapsis and homolog-dependent meiotic DNA repair, producing a subtle increase in meiotic defects, and suppresses pairing, synapsis and recombination defects in some mutant backgrounds. Some defects in pch-2 mutants can be suppressed by incubation at lower temperature and these defects increase in frequency in wildtype worms grown at higher temperature, suggesting that PCH-2 introduces a kinetic barrier to the formation of intermediates that support pairing, synapsis or crossover recombination. We hypothesize that this kinetic barrier contributes to quality control during meiotic prophase. Consistent with this possibility, defects in pch-2 mutants become more severe when another quality control mechanism, germline apoptosis, is abrogated or meiotic DNA repair is mildly disrupted. PCH-2 is expressed in germline nuclei immediately preceding the onset of stable homolog pairing and synapsis. Once chromosomes are synapsed, PCH-2 localizes to the SC and is removed in late pachytene, prior to SC disassembly, correlating with when homolog-dependent DNA repair mechanisms predominate in the germline. Indeed, loss of pch-2 results in premature loss of homolog access. Altogether, our data indicate that PCH-2 coordinates pairing, synapsis and recombination to promote crossover assurance. Specifically, we propose that the conserved function of PCH-2 is to destabilize pairing and/or recombination intermediates to slow their progression and ensure their fidelity during meiotic prophase.
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