Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

The incremental risk of fragility fractures in aging men.

INTRODUCTION: While the United States Preventative Services Task Force recommends osteoporosis screening for women 65 years and older, there is no definitive recommendation for routine osteoporosis screening in men. The purpose of this study was to determine the age at which the odds of fragility fractures (FFx) increase in men to help guide future policy discussions evaluating an optimal screening strategy in this population. METHODS: Men older than 49 years were identified in the PearlDiver Patient Records Database. Patients were excluded if they had a prior fragility fracture, if they were at high risk for osteoporosis due to comorbidities, or if they carried a diagnosis of and/or were on treatment for osteoporosis. The prevalence of FFx was trended for each age group. A stratum-specific likelihood ratio (SSLR) analysis was conducted to identify data-driven strata that maximize the incremental FFx risk by age for men. Logistic regression analyses controlling for potential confounders were conducted to test these identified strata. RESULTS: The incidence of FFx started to increase after the age of 64 years for men. Further, the identified data-driven age strata associated with a significant and incremental difference in fragility fractures were the following: 50-64, 65-69, 70-72, 73-75, 76-78, 79-80, and 81+. When compared to the youngest age stratum (50-64 years), multivariable regression showed the risk of fragility fracture incrementally increased starting in those aged 70-72 (RR, 1.31; 95% CI. 1.21-1.46; p < 0.001) with the highest risk in those aged 81+ (RR, 5.35; 95% CI, 5.10-5.62; p < 0.001). CONCLUSION: In men without a pre-existing history of osteoporosis, the risk of fragility fractures starts to increase after the age of 70. Further work building upon these data may help to identify a specific age at which routine bone health screening in males can help to minimize fractures and their associated morbidity and mortality.

Plasma soluble fms-like tyrosine kinase 1 to placental growth factor ratio of 11.5 multiples of median predicts preeclampsia with severe features within 2 weeks of testing.

BACKGROUND: Angiogenic imbalances, characterized by an excess of antiangiogenic factors (soluble fms-like tyrosine kinase 1) and reduced angiogenic factors (vascular endothelial growth factor and placental growth factor), contribute to the mechanisms of disease in preeclampsia. The ratio of soluble fms-like tyrosine kinase 1 to placental growth factor has been used as a biomarker for preeclampsia, but the cutoff values may vary with gestational age and assay platform. OBJECTIVE: This study aimed to compare multiples of the median of the maternal plasma soluble fms-like tyrosine kinase 1 to placental growth factor ratio, soluble fms-like tyrosine kinase 1, placental growth factor, and conventional clinical and laboratory values in their ability to predict preeclampsia with severe features. STUDY DESIGN: We conducted a cohort study across 18 United States centers involving hospitalized individuals with hypertension between 23 and 35 weeks' gestation. Receiver operating characteristic curve analyses of maternal plasma biomarkers, highest systolic or diastolic blood pressures, and laboratory values at enrollment were performed for the prediction of preeclampsia with severe features. The areas under the curve were compared, and quasi-Poisson regression models were fitted to estimate relative risks. The primary outcome was preeclampsia with severe features within 2 weeks of enrollment. Secondary outcomes were a composite of severe adverse maternal outcomes (elevated liver enzymes, low platelets count, placental abruption, eclampsia, disseminated intravascular coagulation, and pulmonary edema) and a composite of severe adverse perinatal outcomes (birth weight below the third percentile, very preterm birth [<32 weeks' gestation], and fetal or neonatal death). RESULTS: Of the 543 individuals included in the study, preeclampsia with severe features within 2 weeks was observed in 33.1% (n=180) of them. A receiver operating characteristic curve-derived cutoff of 11.5 multiples of the median for the soluble fms-like tyrosine kinase 1 to placental growth factor plasma ratio provided good sensitivity (90.6%), specificity (76.9%), positive predictive value (66.0%), negative predictive value (94.3%), positive likelihood ratio (3.91), negative likelihood ratio (0.12), and accuracy (81.4%) for preeclampsia with severe features within 2 weeks. This cutoff was used to compare test positive cases (≥ cutoff) and test negative cases (< cutoff). Preeclampsia with severe features (66.0% vs 5.7%; P<.001) and composites of severe adverse maternal (8.11% vs 2.7%; P=.006) or perinatal (41.3% vs 10.14%; P=.001) outcomes within 2 weeks were more frequent in test positive cases than in test negative cases. A soluble fms-like tyrosine kinase 1 to placental growth factor plasma ratio ≥11.5 multiples of the median was independently associated with preeclampsia with severe features (adjusted incidence rate ratio, 9.08; 95% confidence interval, 6.11-14.06; P<.001) and a composite of severe adverse perinatal outcomes (adjusted incidence rate ratio, 9.42; 95% confidence interval, 6.36-14.53; P<.001) but not with a composite of severe adverse maternal outcomes (adjusted incidence rate ratio, 2.20; 95% confidence interval, 0.95-5.54; P=.08). The area under the curve for the soluble fms-like tyrosine kinase 1 to placental growth factor plasma ratio in multiples of the median (0.91; 95% confidence interval, 0.89-0.94) for preeclampsia with severe features within 2 weeks was significantly higher (P<.001 for all comparisons) than either plasma biomarker alone or any other parameter with the exception of absolute soluble fms-like tyrosine kinase 1 to placental growth factor plasma ratio values. CONCLUSION: A soluble fms-like tyrosine kinase 1 to placental growth factor plasma ratio ≥11.5 multiples of the mean among hospitalized patients with hypertension between 23 and 35 week's gestation predicts progression to preeclampsia with severe features and severe adverse perinatal outcomes within 2 weeks.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.

Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies.

The agreement intra-class correlation coefficient (ICCa) is a suitable statistical index for inter-rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter-rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior-power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter-rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter-rater reliability studies.

Utility of self-report antiretroviral adherence for predicting HIV viral load among persons who inject drugs in Hai Phong Vietnam: assessing differences by methamphetamine use.

ABSTRACTIn resource-limited settings, alternatives to HIV viral load testing may be necessary to monitor the health of people living with HIV. We assessed the utility of self-report antiretroviral therapy (ART) to screen for HIV viral load among persons who inject drugs in Hai Phong Vietnam, and consider differences by recent methamphetamine use. From 2016 to 2018 we recruited PWID through cross sectional surveys and collected self-report ART adherence and HIV viral load to estimate sensitivity, specificity, positive and negative predictive values (PPV, NPV) and likelihood ratios (LR+, LR-) for self-reported ART adherence as a screening test for HIV viral load. We used three HIV viral load thresholds: < 1000, 500 and 250 copies/mL; laboratory-confirmed HIV viral load was the gold standard. Among 792 PWID recruited, PPV remained above 90% regardless of recent methamphetamine use with slightly higher PPV among those not reporting recent methamphetamine use. The results remained consistent across all three HIV viral load thresholds. Our findings suggest that when HIV viral load testing is not possible, self-reported ART adherence may inform decisions about how to prioritize HIV viral load testing among PWID. The high PPV values suggest self-reported high ART adherence indicates likely HIV viral suppression, irrespective of methamphetamine use.

Nonlinear Regression Modelling: A Primer with Applications and Caveats.

Use of nonlinear statistical methods and models are ubiquitous in scientific research. However, these methods may not be fully understood, and as demonstrated here, commonly-reported parameter p-values and confidence intervals may be inaccurate. The gentle introduction to nonlinear regression modelling and comprehensive illustrations given here provides applied researchers with the needed overview and tools to appreciate the nuances and breadth of these important methods. Since these methods build upon topics covered in first and second courses in applied statistics and predictive modelling, the target audience includes practitioners and students alike. To guide practitioners, we summarize, illustrate, develop, and extend nonlinear modelling methods, and underscore caveats of Wald statistics using basic illustrations and give key reasons for preferring likelihood methods. Parameter profiling in multiparameter models and exact or near-exact versus approximate likelihood methods are discussed and curvature measures are connected with the failure of the Wald approximations regularly used in statistical software. The discussion in the main paper has been kept at an introductory level and it can be covered on a first reading; additional details given in the Appendices can be worked through upon further study. The associated online Supplementary Information also provides the data and R computer code which can be easily adapted to aid researchers to fit nonlinear models to their data.

The diagnostic accuracy of quality control rules.

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfr and only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules.

Regional consistency assessment in multiregional clinical trials.

Multiregional clinical trials (MRCTs) have become a favored strategy for new drug development. The accurate evaluation of treatment effects across different regions is crucial for interpreting the results of MRCTs. Consistency between regional and overall results ensures the extrapolability of the overall conclusions to individual regions. While numerous statistical methods have been proposed for consistency assessment, a notable proportion necessitate a substantial escalation in sample size, particularly in scenarios involving more than four regions within MRCTs. This, paradoxically, undermines the fundamental intent of MRCTs. In addition, standardized statistical criteria for concluding consistency are yet to be established. In this paper, we develop further consistency assessment approaches in the framework of two multivariate likelihood ratio test-based methods, namely mLRTa and mLRTb, wherein consistency is cast as the alternative and null hypotheses. Notably, our exploration unveils that qualitative methods such as the funnel approach and PMDA methods are special instances of mLRTa. Furthermore, our work underscores that these three qualitative methodologies roughly share the same level of assurance probability (AP). Intriguingly, when the number of regions in an MRCT surpasses five, even when the overall sample size guarantees a power of 90% or more and the true treatment effects remain uniform across regions, the AP remains below the 70% mark. Drawing from our meticulous examination of operational attributes, we recommend mLRTa with positive treatment effects in all regions in the alternative hypothesis with significance level 0.5 or mLRTb with all regional treatment effects being equal in the null and significance level of 0.2.

Bayesian spatial cluster signal learning with application to adverse event (AE).

There is growing interest in understanding geographic patterns of medical device-related adverse events (AEs). A spatial scan method combined with the likelihood ratio test (LRT) for spatial-cluster signal detection over the geographical region is universally used. The spatial scan method used a moving window to scan the entire study region and collected some candidate sub-regions from which the spatial-cluster signal(s) will be found. However, it has some challenges, especially in computation. First, the computational cost increased when the number of sub-regions increased. Second, the computational cost may increase if a large spatial-cluster pattern is present and a flexible-shaped window is used. To reduce the computational cost, we propose a Bayesian nonparametric method that combines the ideas of Markov random field (MRF) to leverage geographical information to find potential signal clusters. Then, the LRT is applied for the detection of spatial cluster signals. The proposed method provides an ability to capture both locally spatially contiguous clusters and globally discontiguous clusters, and is manifested to be effective and tractable using hypothetical Left Ventricular Assist Device (LVAD) data as an illustration.

The clinical utility of cystatin C based eGFR in assessing renal function among HIV/AIDs patients on ART at Mildmay Uganda.

BACKGROUND: In clinical practice, Measurement of estimated glomerular filtration rates (eGFR) is the gold standard assessing renal function the glomerular filtration rate often estimated from plasma creatinine. Several studies have shown Cystatin C based eGFR (Cys C) to be a better parameter for the diagnosis of impaired renal function. Cystatin C based eGFR has been proposed as a potential renal function marker but its use in HIV&AIDS patients has not been well evaluated. METHODS: A cross sectional study was carried out on 914 HIV&AIDS patients on antiretroviral therapy (ART) attending Mildmay Uganda for care and treatment between January to March 2015. Serum Cystatin C based eGFR was measured using the particle enhanced immunoturbidimetric assay. Creatinine was analyzed using enzymatic Creatinine PAP method and creatinine clearance was calculated according to C&G. RESULTS: The sensitivity of Cystatin C based eGFR was 15.1% (95% CI = 8.4, 24) with specificity 99.3% (95% CI = 98- 99.7). The positive and negative predictive values were 70.0% (95% CI 45.7-88.1) and 91.2% (95% CI 98.11-92.94) respectively. The positive likelihood ratio was 18.81 and negative likelihood ratio was 0.85. Cystatin C based eGFR had diagnostic accuracy of 90.7 and area under curve was 0.768. CONCLUSION: Cystatin C based eGFR exhibited a high specificity and a high positive likelihood ratio in diagnosis of kidney disease among HIV&AIDS patients. Cystatin C based eGFR can be used as a confirmatory test.

The Creation of Data-Driven Preoperative Hemoglobin A1c and Same-Day Glucose Strata to Stratify Complication Risk Following Total Hip Arthroplasty.

BACKGROUND: This study identifies data-driven strata for preoperative Hemoglobin A1c (HbA1c) and same-day glucose levels that maximize differences in the likelihood of complications following total hip arthroplasty (THA). METHODS: Patients who underwent THA from 2013 to 2022 were identified using a national database. In total, 18,728 patients were identified with a mean age of 67 years (range, 18 to 80). Stratum specific likelihood ratio (SSLR) analysis determined separate strata for HbA1c and same-day glucose levels that minimized the likelihood of 90-day complications following THA. Each stratum was propensity-score matched based on age, sex, hypertension, heart failure, chronic obstructive pulmonary disease, and obesity to the lowest respective stratum. The risk ratio (RR) with respect to the lowest matched stratum was observed. RESULTS: Our SSLR analysis identified 3 data-driven HbA1c strata (4.5 to 5.9, 6.0 to 6.9, and 7.0+) and two same-day glucose strata (60 to 189 and 190+) that predicted 90-day major complications. For HbA1c, when compared to the lowest strata (4.5 to 5.9), the risk of 90-day major complications sequentially increased as the HbA1c strata increased: 6.0 to 6.9 (RR: 1.21; P = .041), 7+ (RR: 1.82; P < .001). For same-day glucose, when compared to the matched lowest strata (60 to 189), the risk of 90-day major complications was higher for the 190+ strata (RR: 1.5; P < .001). CONCLUSIONS: Our results support the use of multiple HbA1c strata that can be incorporated into preoperative risk-stratification models. Additionally, we identified a single cut-off level of 190 as a maximum target blood glucose level perioperatively.

Estimation of additive and maternal covariance of production traits in Murrah buffalo.

The study was done to determine additive, maternal and common permanent environmental effects and best-suited model for some production traits using six univariate animal models that differed in the (co)variance components fitted to assess the importance of maternal effect using likelihood ratio test in Murrah buffaloes. Data from 614 Murrah buffaloes related to production traits were collected from history pedigree sheets maintained at the buffalo farm, Department of Livestock Production and Management (LPM), LUVAS, Hisar. The production traits under this study were 305 days milk yield (305DMY), peak yield (PY), lactation length (LL), dry period (DP), lactation milk yield (LMY) and wet average (WA). The heritability estimates were in the range of 0.33-0.44 for 305DMY, 0.25-0.51 for PY, 0.05-0.13 for LL, 0.03-0.23 for DP, 0.17-0.40 for LMY and 0.37-0.66 for WA. Model 1 was considered best for most of the traits, viz., 305DMY, PY, LL, LMY and WA followed by model 2 for DP. Covariance and correlated values within the traits caused inflation of heritability in model 3 and model 6. The maximum covariance between the additive and maternal effect was found in trait LMY, which was 14,183.90 in model 3 and the minimum value was also reported in the same trait for model 6, valued at -3522.37. Multivariate analysis showed that all production traits were moderate to high and positively correlated with each other except for DP, which was low and negative genetic and phenotypic correlated. Spearman's rank correlation coefficients of breeding value among all six models were high and significant, ranged from 0.78 to 1.00 for all the traits except DP, therefore any of the models could be taken into account depending upon the availability of data.

Permutation Tests for Assessing Potential Non-Linear Associations between Treatment Use and Multivariate Clinical Outcomes.

In many psychometric applications, the relationship between the mean of an outcome and a quantitative covariate is too complex to be described by simple parametric functions; instead, flexible nonlinear relationships can be incorporated using penalized splines. Penalized splines can be conveniently represented as a linear mixed effects model (LMM), where the coefficients of the spline basis functions are random effects. The LMM representation of penalized splines makes the extension to multivariate outcomes relatively straightforward. In the LMM, no effect of the quantitative covariate on the outcome corresponds to the null hypothesis that a fixed effect and a variance component are both zero. Under the null, the usual asymptotic chi-square distribution of the likelihood ratio test for the variance component does not hold. Therefore, we propose three permutation tests for the likelihood ratio test statistic: one based on permuting the quantitative covariate, the other two based on permuting residuals. We compare via simulation the Type I error rate and power of the three permutation tests obtained from joint models for multiple outcomes, as well as a commonly used parametric test. The tests are illustrated using data from a stimulant use disorder psychosocial clinical trial.

Bootstrap tests for simultaneous monotone ordering of effects in a two-way ANOVA.

In a two-way additive analysis of variance (ANOVA) model, we consider the problem of testing for homogeneity of both row and column effects against their simultaneous ordering. The error variances are assumed to be heterogeneous with unbalanced samples in each cell. Two simultaneous test procedures are developed-the first one using the likelihood ratio test (LRT) statistics of two independent hypotheses and another based on the consecutive pairwise differences of estimators of effects. The parametric bootstrap (PB) approach is used to find critical points of both the tests and the asymptotic accuracy of the bootstrap is established. An extensive simulation study shows that the proposed tests achieve the nominal size and have very good power performance. The robustness of the tests is also analyzed under deviation from normality. An "R" package is developed and shared on "GitHub" for ease of implementation of users. The proposed tests are illustrated using a real data set on the mortality due to alcoholic liver disease and it is shown that age and gender have a significant impact on the increasing incidence of mortality.

Likelihood Ratio Test and the Evidential Approach for 2 × 2 Tables.

Categorical data analysis of 2 × 2 contingency tables is extremely common, not least because they provide risk difference, risk ratio, odds ratio, and log odds statistics in medical research. A χ2 test analysis is most often used, although some researchers use likelihood ratio test (LRT) analysis. Does it matter which test is used? A review of the literature, examination of the theoretical foundations, and analyses of simulations and empirical data are used by this paper to argue that only the LRT should be used when we are interested in testing whether the binomial proportions are equal. This so-called test of independence is by far the most popular, meaning the χ2 test is widely misused. By contrast, the χ2 test should be reserved for where the data appear to match too closely a particular hypothesis (e.g., the null hypothesis), where the variance is of interest, and is less than expected. Low variance can be of interest in various scenarios, particularly in investigations of data integrity. Finally, it is argued that the evidential approach provides a consistent and coherent method that avoids the difficulties posed by significance testing. The approach facilitates the calculation of appropriate log likelihood ratios to suit our research aims, whether this is to test the proportions or to test the variance. The conclusions from this paper apply to larger contingency tables, including multi-way tables.

A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2.

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.

CGR-CUSUM: a continuous time generalized rapid response cumulative sum chart.

Rapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative sum (CUSUM) charts have proven to be useful for quality control, yet available methodology for survival outcomes is limited. The few available continuous time inspection charts usually require the researcher to specify an expected increase in the failure rate in advance, thereby requiring prior knowledge about the problem at hand. Misspecifying parameters can lead to false positive alerts and large detection delays. To solve this problem, we take a more general approach to derive the new Continuous time Generalized Rapid response CUSUM (CGR-CUSUM) chart. We find an expression for the approximate average run length (average time to detection) and illustrate the possible gain in detection speed by using the CGR-CUSUM over other commonly used monitoring schemes on a real-life data set from the Dutch Arthroplasty Register as well as in simulation studies. Besides the inspection of medical procedures, the CGR-CUSUM can also be used for other real-time inspection schemes such as industrial production lines and quality control of services.

Integrated variant allele frequency analysis pipeline and R package: easyVAF.

Somatic sequence variants are associated with cancer diagnosis, prognostic stratification, and treatment response. Variant allele frequency (VAF), the percentage of sequence reads with a specific DNA variant over the read depth at that locus, has been used as a metric to quantify mutation rates in these applications. VAF has the potential for feature detection by reflecting changes in tumor clonal composition across treatments or time points. Although there are several packages, including Genome Analysis Toolkit and VarScan, designed for variant calling and rare mutation identification, there is no readily available package for comparing VAFs among and between groups to identify loci of interest. To this end, we have developed the R package easyVAF, which includes parametric and nonparametric tests to compare VAFs among multiple groups. It is accompanied by an interactive R Shiny app. With easyVAF, the investigator has the option between three statistical tests to maximize power while maintaining an acceptable type I error rate. This paper presents our proposed pipeline for VAF analysis, from quality checking to group comparison. We evaluate our method in a wide range of simulated scenarios and show that choosing the appropriate test to limit the type I error rate is critical. For situations where data is sparse, we recommend comparing VAFs with the beta-binomial likelihood ratio test over Fisher's exact test and Pearson's χ2 test.