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Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors (49.09%), samples from transgender participants (3.64%) and stem cell or bone marrow transplant patients (7.27%) along with undetermined sample mix-ups (40%) for which sample swaps occurred prior to arrival at genome centers, however the exact cause of the events at the sampling sites resulting in the mix-ups were not able to be determined. [ABSTRACT FROM AUTHOR]

Background: The Behavioral Risk Factor Surveillance System (BRFSS) is an annual survey designed to identify trends in the public's health. In its 2019 field survey, the U.S. state of GA tested a new 3 – item module to measure the numbers of bereaved, resident adults aged 18 years and older. Participants were eligible if they answered 'Yes' to the item 'Have you experienced the death of a family member or close friend in the years 2018 or 2019?'. This analysis explores two research questions. Can estimates for bereavement prevalence be derived without large sampling errors, low precision, and small subsamples? Can multiple imputation techniques be applied to overcome non-response and missing data to support multivariate modeling? Methods: BRFSS is a non-institutionalized sample of adults aged 18 years and older living in the U.S. state of Georgia. Analyses in this study were conducted under two scenarios. Scenario 1 applies the complex sample weights created by the Centers for Disease Control and imputes values for missing responses. Scenario 2 treats the data as a panel – no weighting combined with removal of persons with missing data. Scenario 1 reflects the use of BRFSS data for public health and policy, while Scenario 2 reflects data as it is commonly used in social science research studies. Results: The bereavement screening item has a response rate (RR) of 69.1% (5206 of 7534 persons). Demographic subgroups and categories of health have RR of 55% or more. Under Scenario 1, the estimated prevalence of bereavement is 45.38%, meaning that 3,739,120 adults reported bereaved in 2018 or 2019. The estimated prevalence is 46.02% with Scenario 2 which removes persons with any missing data (4,289 persons). Scenario 2 overestimates the bereavement prevalence by 1.39%. An illustrative logistic model is presented to show the performance of exposure to bereavement under the two data scenarios. Conclusions: Recent bereavement can be ascertained in a surveillance survey accounting for biases in response. Estimating bereavement prevalence is needed for measuring population health. This survey is limited to one US state in a single year and excludes persons aged 17 years and younger. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the value of transrectal ultrasound (TRUS) guided biopsy in endoscopy negative biopsy patients with rectal lesions. Methods: 150 endoscopy negative biopsy result rectal lesions adopted the transrectal ultrasound-guided biopsy. Based on whether the patients received contrast-enhanced ultrasound examination or not before the biopsies, all enrolled cases were divided into TRUS guided group and contrast-enhanced TRUS (CE-TRUS) guided group, and the safety and diagnostic performances were analyzed retrospectively. Results: We obtained adequate specimens in the majority of cases (98.7%,148/150); There were no complications identified in our study. 126 patients received contrast-enhanced TRUS examination before biopsy to evaluate vascular perfusion and necrosis. The sensitivity, specificity, PPV, NPV, and overall accuracy of all biopsies were 89.1%, 100%,100%, 70.4%, and 91.3% respectively; The sensitivity, specificity, PPV, NPV, and accuracy for TRUS-guided biopsy and CE-TRUS guided biopsy were 73.7%, 100%,100%, 50%,79.2% and 92.1%,100%,100%,75%,93.6% separately; The increase in correct diagnoses was significant (p < 0.05) between TRUS guided biopsy and CE-TRUS guided biopsy. Conclusion: TRUS-guided biopsy is a reliable procedure that can be augmented by endoscopic biopsy techniques if the biopsy yields negative results. CE-TRUS might assist in the location of the biopsy and decrease sampling errors. [ABSTRACT FROM AUTHOR]

Background: Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment. Methods: We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d'Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores. Results: Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates. Conclusions: Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error. [ABSTRACT FROM AUTHOR]

Background: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. Result: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. Conclusion: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl. [ABSTRACT FROM AUTHOR]

Background: Accuracy can be improved by taking multiple synchronous samples from each subject in a study to estimate the endpoint of interest if sample values are not highly correlated. If feasible, it is useful to assess the value of this cluster approach when planning studies. Multiple assessments may be the only method to increase power to an acceptable level if the number of subjects is limited.Methods: The main aim is to estimate the difference in outcome between groups of subjects by taking one or more synchronous primary outcome samples or measurements. A summary statistic from multiple samples per subject will typically have a lower sampling error. The number of subjects can be balanced against the number of synchronous samples to minimize the sampling error, subject to design constraints. This approach can include estimating the optimum number of samples given the cost per subject and the cost per sample.Results: The accuracy improvement achieved by taking multiple samples depends on the intra-class correlation (ICC). The lower the ICC, the greater the benefit that can accrue. If the ICC is high, then a second sample will provide little additional information about the subject's true value. If the ICC is very low, adding a sample can be equivalent to adding an extra subject. Benefits of multiple samples include the ability to reduce the number of subjects in a study and increase both the power and the available alpha. If, for example, the ICC is 35%, adding a second measurement can be equivalent to adding 48% more subjects to a single measurement study.Conclusion: A study's design can sometimes be improved by taking multiple synchronous samples. It is useful to evaluate this strategy as an extension of a single sample design. An Excel workbook is provided to allow researchers to explore the most appropriate number of samples to take in a given setting. [ABSTRACT FROM AUTHOR]

Background: Extracellular volume fraction (ECV) is altered in pathological cardiac remodeling and predicts death and arrhythmia. ECV can be quantified using cardiovascular magnetic resonance (CMR) T1 mapping but calculation requires a measured hematocrit (Hct). The longitudinal relaxation of blood has been used in adults to generate a synthetic Hct (estimate of true Hct) but has not been validated in pediatric populations. Methods: One hundred fourteen children and young adults underwent a total of 163 CMRs with T1 mapping. The majority of subjects had a measured Hct the same day (N = 146). Native and post-contrast T1 were determined in blood pool, septum, and free wall of mid-LV, avoiding areas of late gadolinium enhancement. Synthetic Hct and ECV were calculated and intraclass correlation coefficient (ICC) and linear regression were used to compare measured and synthetic values. Results: The mean age was 16.4 ± 6.4 years and mean left ventricular ejection fraction was 59% ± 9%. The mean measured Hct was 41.8 ± 3.0% compared to the mean synthetic Hct of 43.2% ± 2.9% (p < 0.001, ICC 0.46 [0.27, 0.52]) with the previously published model and 41.8% ± 1.4% (p < 0.001, ICC 0.28 [0.13, 0.42]) with the locally-derived model. Mean measured mid-free wall ECV was 30.5% ± 4.8% and mean synthetic mid-free wall ECV of local model was 29. 7% ± 4.6% (p < 0.001, ICC 0.93 [0.91, 0.95]). Correlations were not affected by heart rate and did not significantly differ in subpopulation analysis. While the ICC was strong, differences between measured and synthetic ECV ranged from -8. 4% to 4.3% in the septum and -12.6% to 15.8% in the free wall. Using our laboratory's normal cut-off of 28.5%, 59 patients (37%) were miscategorized (53 false negatives, 6 false positives) with published model ECV. The local model had 37 miscategorizations (20 false negatives, 17 false positives), significantly fewer but still a substantial number (23%). Conclusions: Our data suggest that use of synthetic Hct for the calculation of ECV results in miscategorization of individual patients. This difference may be less significant once synthetic ECV is calculated and averaged over a large research cohort, making it potentially useful as a research tool. However, we recommend formal measurement of Hct in children and young adults for clinical CMRs. [ABSTRACT FROM AUTHOR]

Background: Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (n ⪡ p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data sets from observational studies, e.g., in ecology, where flexible, non-linear models are fitted to high-dimensional data. We assess the recently proposed flexible framework for variable selection called stability selection. By the use of resampling procedures, stability selection adds a finite sample error control to high-dimensional variable selection procedures such as Lasso or boosting. We consider the combination of boosting and stability selection and present results from a detailed simulation study that provide insights into the usefulness of this combination. The interpretation of the used error bounds is elaborated and insights for practical data analysis are given. Results: Stability selection with boosting was able to detect influential predictors in high-dimensional settings while controlling the given error bound in various simulation scenarios. The dependence on various parameters such as the sample size, the number of truly influential variables or tuning parameters of the algorithm was investigated. The results were applied to investigate phenotype measurements in patients with autism spectrum disorders using a log-linear interaction model which was fitted by boosting. Stability selection identified five differentially expressed amino acid pathways. Conclusion: Stability selection is implemented in the freely available R package stabs (http://CRAN.R-project.org/ package=stabs). It proved to work well in high-dimensional settings with more predictors than observations for both, linear and additive models. The original version of stability selection, which controls the per-family error rate, is quite conservative, though, this is much less the case for its improvement, complementary pairs stability selection. Nevertheless, care should be taken to appropriately specify the error bound. [ABSTRACT FROM AUTHOR]

Background: Propensity score methods have become a popular tool for reducing selection bias in making causal inference from observational studies in medical research. Propensity score matching, a key component of propensity score methods, normally matches units based on the distance between point estimates of the propensity scores. The problem with this technique is that it is difficult to establish a sensible criterion to evaluate the closeness of matched units without knowing estimation errors of the propensity scores. Methods: The present study introduces interval matching using bootstrap confidence intervals for accommodating estimation errors of propensity scores. In interval matching, if the confidence interval of a unit in the treatment group overlaps with that of one or more units in the comparison group, they are considered as matched units. Results: The procedure of interval matching is illustrated in an empirical example using a real-life dataset from the Nursing Home Compare, a national survey conducted by the Centers for Medicare and Medicaid Services. The empirical example provided promising evidence that interval matching reduced more selection bias than did commonly used matching methods including the rival method, caliper matching. Interval matching's approach methodologically sounds more meaningful than its competing matching methods because interval matching develop a more "scientific" criterion for matching units using confidence intervals. Conclusions: Interval matching is a promisingly better alternative tool for reducing selection bias in making causal inference from observational studies, especially useful in secondary data analysis on national databases such as the Centers for Medicare and Medicaid Services data. [ABSTRACT FROM AUTHOR]

The article offers information on the study conducted by the authors related to determination of optimal neighborhood size for ecological studies using leave-one-out cross validation. It states that there has been a growing interest in ecological research assuming that the neighborhood where people reside may influence their health status as much as personal risk factors like age and gender.

Background: When the resected specimen is sent for intraoperative margin assessment, all margins are grossly checked, and selected margins undergo a frozen section (FS) examination. Therefore, there is a possibility of sampling error. This study evaluated the effectiveness of using toluidine blue (TB) as an intraoperative triage screening tool to detect positive mucosal margins of the resected specimens of oral squamous cell carcinoma (OSCC) and serve as a guide for FS sampling.Methods: Surgical samples of 30 consecutive patients with biopsy-proven OSCC were included in the study. A total of 140 mucosal margins were analyzed intraoperatively by TB and FS, the results were compared with the final histopathology.Results: Of the 140 examined mucosal tumor margins, 14 stained positives with TB, six were true-positives, eight were false-positives, and there were no false-negatives, as confirmed by final histopathology of the same margins. The diagnostic performance measures were sensitivity 100.0%; specificity 94.0%; positive predictive value (PPV) 42.9%; negative predictive value (NPV) 100.0%; and accuracy 94.3% (95% CI: 89.0-97.5%). For FS, there were three true-positives, three false-negatives, and no false-positives. The diagnostic performance measures were sensitivity 50.0%; specificity 100.0%; PPV 100.0%; NPV 97.8%; and accuracy 97.9% (95% CI: 93.9-99.6%).Conclusion: TB is less specific but more sensitive than FS for detecting positive mucosal margins of resected OSCC. Screening the tumor mucosal margins with TB before FS sampling may help identify more tumor-bearing margins.Trial Registration: This trial was registered at ClinicalTrials.gov. Registration number: NCT03554967 . Registration date: June 13, 2018. [ABSTRACT FROM AUTHOR]

Background: Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood. Results: Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated. Conclusions: We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease. [ABSTRACT FROM AUTHOR]

Background: Herein, an algorithm that can be used in wearable health monitoring devices to estimate metabolic equivalents (METs) based on physical activity intensity data, particularly for certain activities in daily life that make MET estimation difficult.Results: Energy expenditure data were obtained from 42 volunteers using indirect calorimetry, triaxial accelerations and heart rates. The proposed algorithm used the percentage of heart rate reserve (%HRR) and the acceleration signal from the wearable device to divide the data into a middle-intensity group and a high-intensity group (HIG). The two groups were defined in terms of estimated METs. Evaluation results revealed that the classification accuracy for both groups was higher than 91%. To further facilitate MET estimation, five multiple-regression models using different features were evaluated via leave-one-out cross-validation. Using this approach, all models showed significant improvements in mean absolute percentage error (MAPE) of METs in the HIG, which included stair ascent, and the maximum reduction in MAPE for HIG was 24% compared to the previous model (HJA-750), which demonstrated a 70.7% improvement ratio. The most suitable model for our purpose that utilized heart rate and filtered synthetic acceleration was selected and its estimation error trend was confirmed.Conclusion: For HIG, the MAPE recalculated by the most suitable model was 10.5%. The improvement ratio was 71.6% as compared to the previous model (HJA-750C). This result was almost identical to that obtained from leave-one-out cross-validation. This proposed algorithm revealed an improvement in estimation accuracy for activities in daily life; in particular, the results included estimated values associated with stair ascent, which has been a difficult activity to evaluate so far. [ABSTRACT FROM AUTHOR]