Incorrect Testing Research Articles

Экспресс-диагностика профессиональной психологической пригодности военнослужащих по призыву

Summary. The purpose of the study is to develop a model for express diagnostics of the professional psychological suitability of conscript military personnel. Materials and methods of research. The object of the study was randomly selected conscript military personnel (n=60), whose average age was (23.78±1.27) years. Criteria for inclusion in the study were conscription age and written consent of the subject to participate in the study; exclusion criteria from the study – lack of written consent to participate in the study, incorrect testing, severe illness – high fever, chills, etc. When processing the research results, descriptive statistics methods and cluster analysis using the K-means method were used. The significance of differences between groups was assessed using Student’s t-test; to develop decision rules, the multiple regression method and forward discriminant analysis stepwise with inclusion were used (F-enter = 2.0); (F-remove=1.9) and p<0.05. The critical value of the significance level was considered to be 0.05. Mathematical data processing was carried out using the STATISTICA v.10.0 software package. Research results and their analysis. Using the example of conscripted military personnel, the possibility of predicting professional psychological suitability using express testing based on vibraimage technology is shown. The results of testing aimed at assessing personality accentuations, abilities (types of multiple intelligences according to G. Gardner), as well as the moral qualities of military personnel were analyzed. Decisive rules have been constructed that make it possible to predict the professional psychological suitability of a serviceman with an accuracy of more than 91%.

Epidemiological impact of a large number of false negative SARS-CoV-2 test results in South West England during September and October 2021

During September and October 2021, a substantial number of Polymerase Chain Reaction (PCR) tests in England processed at a single laboratory were incorrectly reported as negative. We estimate the number of false negative test results issued and investigate the epidemiological impact of this incident. We estimate the number of COVID-19 cases that would have been reported had the sensitivity of the laboratory test procedure not dropped for the period 2 September to 12 October. In addition, by making comparisons between the most affected local areas and comparator populations, we estimate the number of additional infections, cases, hospitalisations and deaths that could have occurred as a result of increased transmission due to false negative test results.We estimate that around 39,000 tests may have been false negatives during this period and, as a direct result of this incident, the most affected areas in the South-West of England could have experienced between 6000 and 34,000 additional reportable cases, with a central estimate of around 24,000 additional reportable cases. Using modelled relationships between key variables, we estimate that this central estimate could have translated to approximately 55,000 additional infections.Each false negative likely led to around 1.5 additional infections. The incident is likely to have had a measurable impact on cases and infections in the affected areas in the South-West of England. Impact statementThese results indicate the significant negative impact of incorrect testing on COVID outcomes; and make a substantial contribution to understanding the impact of testing systems and the need to ensure high accuracy in testing and reporting of results.

Empirically adjusted fixed-effects meta-analysis methods in genomic studies.

In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the z-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.

An optimized neural network for prediction of security threats on software testing

Software testing involves evaluating and confirming a software program or product to ensure it operates according to its intended functionality. Testing offers advantages like bug prevention, reduced development expenses, and improved performance. The problems are dialogue gap, ecological danger, creation of software quickly, cost of operation and upkeep, inadequate assessment, and incorrect testing estimates. The structure was initially educated using internet presentation data that included intrusion information. A novel Dove Swarm-based Deep Neural Method (DSbDNM) with the required traits and stages of processing has been developed. Moving forward, feature extraction and malicious behaviour forecast have both been completed. Also, the different types of assaults and negative behaviours were categorized. The developed prediction model is also examined by initiating and detecting an unidentified assault. Finally, the performance measures' accuracy, error rate, Precision, Recall and f-measure were computed. Moreover, the proposed system implementation is done in Python. Therefore, the proposed work performance can be enhanced and attain high accuracy in low computational time. For the DSbDNM dataset, the designed prototypical achieved 94.65 accuracy, 94.95 precision, 90.16 Recall and 92.02 F-measure for the NF-UQ-NIDS-v2 Dataset. Moreover, the Intrusion Detection Dataset attained an accuracy of 98, Precision of 98.8, Recall of 94.2, and F-score of 96 in the developed model. Subsequently, the Network Intrusion Detection Dataset attained an accuracy of 99, a precision of 99.2, a Recall of 95.8 and an F-measure of 97.1

Public-Key Encryption With Tester Verifiable Equality Test for Cloud Computing

Public-key encryption with equality test (PKEET) provides cloud servers with an effective way to check the equality of outsourced encrypted data without decryption. This enables PKEET to attract much attention and be widely researched in cloud computing. However, we claim that the existing PKEET schemes suffer from an inherited problem, called message-consistency unverifiability of testers (MCUT). Applying the MCUT problem, outsourcers can fool cloud servers into outputting incorrect testing results of encrypted data, which negates the practicability of PKEET in cloud computing. We investigate the PKEET literature and find the main reason for the MCUT problem is the independence between the messages inserted in the decryption and testing modules in their ciphertexts. To bridge the technical gap between PKEET and its practical applications, we present a new notion, called PKE with tester verifiable equality test (PKE-TVET), which solves the MCUT problem by allowing testers to verify the message consistency in two modules. We then instantiate the PKE-TVET and give a specific construction in the standard model. In our PKE-TVET scheme, the testing module is integrated into the decryption module so that there is only one message inserted in the ciphertext for both decryption and testing. This special setting lets our scheme directly get rid of the MCUT problem. For better applications in actual scenarios, we further extend the scheme to support authorization and tester designation. Finally, we analyze the tradeoff of parameter sizes and computation costs for the security against MCUT attacks in our PKE-TVET scheme.

An empirically adjusted weighted ordered p-values meta-analysis method for large-scale simultaneous significance testing in genomic experiments

Background Meta-analysis is a popular approach for combining results from multiple studies investigating the same questions. Meta-analysis has gained wide popularity in genomic analysis due to the availability of large volumes of genomic study results from public databases. In genomic meta-analysis, researchers, often, tend to combine p-values related to significance testing of a gene from multiple studies where thousands of genes are tested simultaneously. The traditional p-value combination approaches aim to find genes which are differentially expressed in at least one of studies. An alternative form of meta-analysis has, recently, gained popularity where the aim is to find genes that are consistently differentially expressed in a large number, possibly a majority, of studies. An approach based on weighted ordered p-values (WOP) has been developed, in the recent past, to perform the latter type of meta-analysis. Methods In this article, we discuss the limitations of the WOP meta-analysis method due to its adherence to the standard null distributional assumptions of classical meta-analysis that can lead to incorrect significance testing results. Moreover, we propose a robust meta-analysis method for simultaneous significance testing of multitude of genes that improves the WOP approach using an empirical modification. Results Through simulation studies, we demonstrate the superiority of our proposed method over the existing WOP meta-analysis by substantially reducing false discoveries of significant genes and controlling type-I error rates especially in the presence of unobserved confounding variables. We illustrate the utility of our proposed method through a variety of meta-analysis of genomic studies in different diseases.

Cytopathological evaluation of thyroid by fine needle aspiration cytology and its association with thyroid function

Introduction: Fine needle aspiration cytology is a reliable method for thyroid nodule evaluation (FNAC). Thyroid nodules often cause abnormal thyroid function tests, which are used for diagnosis and therapy. Thyroid cancer and incorrect thyroid function testing are still debated. Objective: We aimed to evaluate the cytopathological features of thyroid nodules by FNAC and their association with thyroid function tests. Methods: This 12-month cross-sectional study was conducted in Peshawar, Pakistan. 350 thyroid nodule patients were referred to endocrinology. All research participants had a physical checkup and thyroid function testing. Thyroid function testing measured TSH and FT4 using conventional laboratory procedures. All individuals had 23-gauge ultrasound-guided thyroid nodule FNAC. Pathologists used the Bethesda System for Reporting Thyroid Cytopathology to evaluate FNAC samples. SPSS 25 analyzed the data. Descriptive statistics detailed the study population's clinical and demographic characteristics. Logistic regression was utilized to examine the relationship between thyroid function tests and thyroid cancer risk and FNAC's thyroid cancer detection effectiveness. Results: For this research, a total of 350 thyroid nodule patients were included having the median age 39.8 (20-60) years and the ratio of female to male participants was 3:1.

EXPERIENCE IN THE APPLICATION OF NON-DESTRUCTIVE TESTING METHODS CONTROL IN THE OPERATION OF PRODUCTS

The article summarizes the practical experience accumulated by the author in the application of non-destructive testing methods in the operation of aviation equipment, main gas pipelines, railway rolling stock. The main criteria for the selection of non-destructive testing methods, their applicability depending on the depth of the defect location and the maximum achievable sensitivity of the method are considered. The main advantages and disadvantages of non-destructive testing methods are indicated specific examples are given: –the influence of manufacturing defects in the material of products (main gas pipelines, railway rolling stock) on the detection of operational cracks; –an erroneous approach to ultrasonic inspection of zones of “tight” connection of two smoothly processed parts of the product and ways to eliminate such errors; –incorrect testing of the control technology, without taking into account: statistics on rejection of parts during their repair, the causes of the destruction of the part, the influence of the operating conditions of the product; the influence of ambient temperature. Recommendations are given on the search zones for operational defects of technological gas pipelines. Recommendations have been worked out to eliminate the identified shortcomings. The author gives examples of the application of non-destructive testing methods for 30 years of his practical activity.

Observations from the Field Concerning the Use of 1 mEq/L Potassium Dialysate, Equipment Monitoring, Staffing, and Total Chlorine Testing

Patient safety and survey readiness should be top priorities for dialysis facilities. Hemodialysis is a complex process that redisposes staff and patients to possible errors and patient harm. Some observations seen by surveyors in dialysis facilities include an increased use in 1 mEq/L potassium dialysate, incomplete testing of hemodialysis machines, incorrect total chlorine testing, and staffing shortages. This article discusses the implications for the nephrology nurse in each of these situations.

Clinical and epidemiological investigation of a child with asymptomatic COVID-19 infection following reoccurrence

ObjectiveTo investigate the case of a child infected with coronavirus disease 2019 (COVID-19) who had subsequent viral reactivation.MethodsWe retrospectively analyzed the clinical manifestations, epidemiological data, laboratory and imaging examinations, treatment, and follow-up of the child. And then, we searched related literature using PubMed.ResultsThe 9-year-old boy was exposed to COVID-19 in Malawi and tested positive for NAT in Haikou, China. He was asymptomatic and admitted to our hospital. After six negative NATs, he was discharged from the hospital and quarantined in a hotel. His infection was reactivated again after 22 days (interval between first and last positive NATs). The cycle threshold (Ct) values of positive tests were 25 and 31, and the gene sequencing viral loads were very low. The viral strain Kenya/P2601/2020, a variant of the hCoV-19/Wuhan/IVDC-HB-01/2019 genome (GISAID accession IL: EPI_ISL_402119), was found when polymerase chain reaction enrichment was used to sequence the virus. However, people around him tested negative for COVID-19.ConclusionFirst, we confirmed the reactivation of COVID-19 in a child. The risk of recurrent infection with SARS-CoV-2 was low, and the policy of strictly isolating patients carrying long-term viral ribonucleic acid should be reconsidered. The interval positivity was most likely due to incorrect sampling and/or testing methods. SGS and aB testing are recommended for children with viral reactivation. Second, SARS-CoV-2 viral reactivation cannot be ruled out. The possible mechanisms, such as prolonged infection and viral latent reactivation, need further investigation.

COMPUTATIONAL ANALYSIS OF FRACTIONAL ORDER IMPERFECT TESTING INFECTION DISEASE MODEL

These days the whole world is facing a serious problem of infectious maladies and how to control the endemic of these diseases. Testing correctly is one of the most important procedures in preventing the spread of infectious diseases, as incorrect testing can turn a susceptible person into an infected person. In this paper, we study the dynamics of imperfect testing and diagnostics of infectious diseases model by replacing the integer order derivative in the sense of fractional order Atangana–Baleanu operator coupled with Caputo operator. This AB-fractional operator is the generalization of classical derivative and gives more data of the factors of the nonlocal dynamical frameworks. Fixed point theorems have been used for the verification of existence results and Picard’s stability technique utilized for the stability study of a fractional order imperfect testing infectious disease (ITID) model. Finally, numerical computations are implemented for the fractional order ITID model to illustrate the results graphically.

Food safety knowledge and behaviour among food handlers in catering establishments: a case study

PurposeThe main objective of this study is to find out how food handlers in catering establishments perceive ensuring food safety and which problems they meet along the way.Design/methodology/approachUsing a qualitative approach, ten food handlers in Slovenian catering facilities were included in the case study. A semi-structured approach was applied to provide a deeper insight into food safety barriers perceived by respondents. Participants first read short fictitious newspaper news about a foodborne disease at a tourist farm, which served as a starting point.FindingsThe results demonstrate barriers which most often originate in a lack of knowledge (e.g. improper food safety training, incorrect food safety knowledge testing, knowledge and maintaining of CCPs), shortage of food hygiene skills (e.g. handwashing, food defrosting) and weak work satisfaction (e.g. insufficient payment, poor interpersonal relationships and weak motivation). Food safety knowledge and consequently training methods were found to be the biggest barrier for the efficiency of the HACCP system in practice.Research limitations/implicationsDue to the small sample, the results cannot be generalised to the entire population of food handlers in Slovenia.Practical implicationsThe results indicate weaknesses in food safety knowledge among professional food handlers.Originality/valueThe study provides a deeper insight into implicit opinions of ten food handlers in catering facilities regarding barriers in providing food safety, their knowledge and behaviour in their work with food.

Challenges and Errors in Genetic Testing: The Fifth Case Series.

In this ongoing case series, 33 genetic testing cases are documented in which tests were recommended, ordered, interpreted, or used incorrectly and/or in which clinicians faced challenges related to history/reports provided by patients or laboratories. An invitation to submit cases of challenges or errors in genetic testing was issued to the general National Society of Genetic Counselors Listserv, the National Society of Genetic Counselors Cancer Special Interest Group members, as part of a case series with Precision Oncology News, and via social media (i.e., Facebook, Twitter, LinkedIn). Deidentified clinical documentation was requested and reviewed when available. Thirty-three cases were submitted, reviewed, and accepted. A thematic analysis was performed. Submitters were asked to approve cases before submission. All cases took place in the United States, involved hereditary cancer testing and/or findings in cancer predisposition genes, and involved medical-grade genetic testing, direct-to-consumer testing, or research genetic testing. In 9 cases, test results were misinterpreted, leading to incorrect screening or risk-reducing procedures being performed/recommended. In 5 cases, incorrect or unnecessary testing was ordered/recommended. In 3 cases, incorrect clinical diagnoses were made, or opportunities for diagnoses were delayed. In 3 cases, errors or challenges arose related to medical intervention after testing or reported genetic diagnosis. In 2 cases, physicians provided incorrect information related to the inheritance pattern of a syndrome. In 2 cases, there were challenges related to the interpretation of genetic variants. In 2 cases, challenges arose after direct-to-consumer testing. One case involved test results that should never have been reported based on sample quality. In 1 case, a patient presented a falsified test result. In 5 cases, multiple errors were made. As genetic testing continues to become more complicated and common, it is critical that patients and nongenetics providers have access to accurate and timely genetic counseling information. Even as multiple medical bodies highlight the value of genetic counselors (GCs), tension exists in the genomics community as GCs work toward licensure and Medicare provider status. It is critical that health care communities leverage, rather than restrict, the expertise and experience of GCs so that patients can benefit from, and not be harmed by, genetic testing. In order to responsibly democratize genomics, it will be important for genetics and nongenetic health care providers to collaborate and use alternative service delivery models and technology solutions at point of care. To deliver on the promise of precision medicine, accurate resources and tools must be utilized.

Flexible omnibus test in 1:M matched case-crossover study with measurement error in covariate.

The matched case-crossover study design is used in public health, biomedical, and epidemiological research with clustered binary outcomes. Conditional logistic regression is commonly used for analysis because any effects associated with the matching covariates by stratum can be removed. However, some matching covariates often play an important role as effect modifications, causing incorrect statistical testing. The covariates in such studies are often measured with error, so that not accounting for this error can also lead to incorrect inferences for all covariates in the model. However, the methods available for simultaneously evaluating effect modification by matching covariates as well as assessing and characterizing error-in-covariates are limited. In this paper, we propose a flexible omnibus test for testing (1) the significance of a functional association between the clustered binary outcomes and covariates with the measurement error, (2) the existence of effect modifications by matching covariates, and (3) the significance of an interaction effect between the measurement error covariate and other covariates, without specifying the functional forms for these testings. The proposed omnibus test has the flexibility to allow inferences on various hypothesis settings. The advantages of the proposed flexible omnibus test are demonstrated through simulation studies and 1:4 bidirectional matched data analyses from an epidemiology study.

Household Water Security: An Analysis of Water Affect in the Context of Hydraulic Fracturing in West Virginia, Appalachia

Hydraulic fracturing has been booming in the last decade in the United States. While natural gas extraction and production has improved the national energy security, it has raised questions around the water security of those communities where extraction is taking place. Both scientists and residents are concerned about hydraulic fracturing’s impacts on surface- and groundwater, especially regarding how hydraulic fracturing impacts residents’ access to safe household well water. In the past decade, the Marcellus Shale has been developed in Northwestern West Virginia, yet the human geography dimensions of oil and gas extraction in West Virginia remain to be investigated. This article, based on 30 in-depth interviews, explores household groundwater insecurity due to hydraulic fracturing experienced by residents (i.e., mineral owners, surface owners, and concerned citizens) in Northwestern West Virginia. The concept of water affect is used to attend to the emotional and subjective dimensions of water security by unveiling the power, emotional struggles, and mental stress inherent in water testing practices and environmental regulation around hydraulic fracturing. Water testing is typically conducted by contractors hired by oil and gas companies, but it is mired in delayed test results and incorrect testing procedures, triggering residents’ negative feelings toward oil and gas companies. This article furthers the understanding of water security, commonly defined in terms of individual access to adequate water quality and quantity, by studying Appalachian residents’ anxieties about well water contamination and uncertainty around the long-term water impacts of hydraulic fracturing. By investigating the uneven power relations around groundwater in West Virginia, the emotional experiences and responses are articulated to further the notion of water affect as impacting household groundwater security.

A re-examination of the empirical evidence concerning colonial Virginia's paper money, 1755-1774: a comment on Grubb

Farley Grubb's recent article in the Financial History Review contains econometric results designed to support his theoretical propositions concerning the paper money of the American colonies. This comment demonstrates that some of his results are spurious and the rest are based on using incorrect testing procedures and incorrect critical values of test statistics.

A prospective quality improvement initiative in adult hemophagocytic lymphohistiocytosis to improve testing and a framework to facilitate trigger identification and mitigate hemorrhage from retrospective analysis.

Hemophagocytic lymphohistiocytosis (HLH) is a highly fatal, hyperinflammatory syndrome in adults triggered by an underlying illness in most cases. As such, suspicion of HLH dictates further investigation to identify the HLH trigger and determine treatment. HLH is clinically challenging due to diverse presentations and underlying triggers, provider unfamiliarity, and bleeding complications. Clinically, we observed diagnostic error from incorrect testing and cognitive biases (interleukin-2 confused with soluble interleukin-2 receptor and natural killer cell quantification confused with functional assays).This study reports our single institutional experience with adult HLH with the aim to reduce erroneous testing with a quality improvement (QI) project, and to facilitate trigger discovery and mitigate hemorrhage. Provider education on HLH testing was the prospective intervention, followed by mistaken test removal. HLH triggers and diagnostic utility were determined by retrospective chart review. Risk factors for hemorrhage were determined by multivariable analysis.Erroneous HLH testing was reduced from 74% to 24% of patients (P < .001) by the QI intervention. These changes were projected to save $11,700 yearly. The majority (64%) of patients evaluated for HLH were on non-hematology/oncology services, highlighting the need for vigilance in hematology consultation. Sixty-three patients met classic HLH-2004 criteria for HLH. Malignancy (38%), infection (27%), Epstein-Barr virus (EBV) (14%), or autoimmune disease (8%) triggered most HLH cases. HLH triggers were most commonly identified by serologic testing (27%) and bone marrow biopsy (19%). Biopsy of other affected organs based on PET-CT imaging after unsuccessful initial diagnostic measures was helpful, and focal fluorodeoxyglucose uptake was predictive of an underlying malignancy (likelihood ratio 8.3, P = .004). Major hemorrhage occurred in 41% of patients. On multivariable analysis the odds ratios (OR) for major hemorrhage were increased for patients with intensive care unit level care (OR 10.47, P = .005), and disseminated intravascular coagulation in the first week of admission (OR 10.53, P = .04).These data are incorporated into a framework to encourage early HLH recognition with the HScore, facilitate trigger identification, identify those at risk for hemorrhage, and minimize low-yield or erroneous testing.

"Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data": Correction to Liu et al. (2016).

Reports an error in "Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data" by Siwei Liu, Yang Zhou, Richard Palumbo and Jane-Ling Wang (Psychological Methods, 2016[Sep], Vol 21[3], 291-308). In the article, there were errors in the R script of Appendix B which could lead to incorrect significance testing results for dynamical correlation. We created an updated R script with corrections. In the updated R script, argument "na" from function "ind_DC" and argument "ms" from function "boot_test_DC" were removed. Codes to check if there is any missing value in the data, and to compute proportion of missing values in the data were added. A warning was added when too many missing values are present. In addition, argument 't' is now correctly labeled "a vector of time points where x,y are observed". The updated R script with corrections can be downloaded from the first author's personal website: https://siweiliu.weebly.com/publications.html. (The following abstract of the original article appeared in record 2016-07276-001.) In this article, we introduce dynamical correlation, a new method for quantifying synchrony between 2 variables with intensive longitudinal data. Dynamical correlation is a functional data analysis technique developed to measure the similarity of 2 curves. It has advantages over existing methods for studying synchrony, such as multilevel modeling. In particular, it is a nonparametric approach that does not require a prespecified functional form, and it places no assumption on homogeneity of the sample. Dynamical correlation can be easily estimated with irregularly spaced observations and tested to draw population-level inferences. We illustrate this flexible statistical technique with a simulation example and empirical data from an experiment examining interpersonal physiological synchrony between romantic partners. We discuss the advantages and limitations of the method, and how it can be extended and applied in psychological research. We also provide a set of R code for other researchers to estimate and test for dynamical correlation. (PsycINFO Database Record

Beware the F test (or, how to compare variances)

Biologists commonly compare variances among samples, to test whether underlying populations have equal spread. However, despite warnings from statisticians, incorrect testing is rife. Here we show that one of the most commonly employed of these tests, the F test, is extremely sensitive to deviations from normality. The F test suffers greatly elevated false positive errors when the underlying distributions are heavy tailed, a distribution feature that is very hard to detect using standard normality tests. We highlight and assess a selection of parametric, jackknife and permutation tests, consider their performance in terms of false positives, and power to detect signal when it exists, then show correct methods to compare measures of variation among samples. Based on these assessments, we recommend using Levene's test, Box–Anderson test, jackknifing or permutation tests to compare variances when normality is in doubt. Levene's and Box–Anderson tests are the most powerful at small sample sizes, but the Box–Anderson test may not control type I error for extremely heavy-tailed distributions. As noted previously, do not use F tests to compare variances.

EAMA: Empirically adjusted meta-analysis for large-scale simultaneous hypothesis testing in genomic experiments.

Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. However, adhering to the regular statistical assumptions regarding the null distributions of test statistics in such large-scale multiple testing frameworks has the potential of leading to incorrect significance testing results and biased inference. This problem gets worse when one combines results from different independent genomic experiments with a possibility of ending up with gross false discoveries of significant genes. In this article, we develop a meta-analysis method of combining p-values from different independent experiments involving large-scale multiple testing frameworks, through empirical adjustments of the individual test statistics and p-values. Even though, it is based on various existing ideas, this specific combination is novel and potentially useful. Through simulation studies and real genomic datasets we show that our method outperforms the standard meta-analysis approach of significance testing in terms of accurately identifying the truly significant set of genes.