DELFMUT: duplex sequencing-oriented depth estimation model for stable detection of low-frequency mutations.

Duplex sequencing technology has been widely used in the detection of low-frequency mutations in circulating tumor deoxyribonucleic acid (DNA), but how to determine the sequencing depth and other experimental parameters to ensure the stable detection of low-frequency mutations is still an urgent problem to be solved. The mutation detection rules of duplex sequencing constrain not only the number of mutated templates but also the number of mutation-supportive reads corresponding to each forward and reverse strand of the mutated templates. To tackle this problem, we proposed a Depth Estimation model for stable detection of Low-Frequency MUTations in duplex sequencing (DELFMUT), which models the identity correspondence and quantitative relationships between templates and reads using the zero-truncated negative binomial distribution without considering the sequences composed of bases. The results of DELFMUT were verified by real duplex sequencing data. In the case of known mutation frequency and mutation detection rule, DELFMUT can recommend the combinations of DNA input and sequencing depth to guarantee the stable detection of mutations, and it has a great application value in guiding the experimental parameter setting of duplex sequencing technology.

Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data.

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

Impact assessment of mandatory safety device installation for gas stoves.

Numerous fire accidents have occurred in Japan owing to food overheating when cooking on gas stoves because users left the cooking area unattended. In response, authorities enacted a legal revision mandating temperature sensor installation on gas stove burners. Nevertheless, the actual effectiveness of this measure remains uncertain. Furthermore, prior studies have raised concerns about the efficacy of specific safety standards for consumer products owing to insufficient evidence of reduction in accidents. Consequently, this study seeks to assess the impact of the standards revision implemented in 2008, requiring the placement of safety devices on all gas stove burners. Through a comprehensive analysis, it was aimed to ascertain how these measures have precisely influenced safety outcomes in cooking-related incidents. The data in this study were subdivided to mitigate potential bias from user attributes during data collection. The analysis employed the difference-in-differences method, specifically utilizing one-burner gas stoves unaffected by the standards revision. The data used in this study include accidents associated with gas stoves, spanning the period 2007-2019. To estimate the likelihood of overheating accidents, a binomial distribution model that utilized the Markov chain Monte Carlo methods was applied. For gas stoves with two or more burners, which were affected by the standards revision, the estimated probability of overheating accidents decreased by approximately 80%. In contrast, no decreasing trend was observed for one-burner gas stoves. The analysis suggests that the mandatory installation of safety devices on gas stoves has indeed resulted in a significant reduction in overheating accidents.

Analysis of two binomial proportions in noninferiority confirmatory trials.

In this article, we propose considering an approximate exact score (AES) test for noninferiority comparisons and we derive its test-based confidence interval for the difference between two independent binomial proportions. This test was published in the literature, but not its associated confidence interval. The p-value for this test is obtained by using exact binomial probabilities with the nuisance parameter being replaced by its restricted maximum likelihood estimate. Calculated type I errors revealed that the AES method has important advantages for noninferiority comparisons over popular asymptotic methods for adequately powered confirmatory clinical trials, at 80% or 90% statistical power. For unbalanced sample sizes of the compared groups, type I errors for the asymptotic score method were shown to be higher than the nominal level in a systematic pattern over a range of true proportions, but the AES method did not suffer from such a problem. On average, the true type I error of the AES method was closer to the nominal level than all considered methods in the empirical comparisons. In rare cases, type I errors of the AES test exceeded the nominal level, but only by a small amount. Presented examples showed that the AES method can be more attractive in practice than practical exact methods. In addition, p-value and confidence interval of the AES method can be obtained in <30 s of computer time for most confirmatory trials. Theoretical arguments, combined with empirical evidence and fast computation time should make the AES method attractive in statistical practice.

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.

Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system.

Many statistical methods have been applied to VAERS (vaccine adverse event reporting system) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. The AE ontology contains important information about biological similarities between AEs. In this paper, we develop a model to estimate vaccine-AE associations while incorporating the AE ontology. We model a group of AEs using the zero-inflated negative binomial model and then estimate the vaccine-AE association using the empirical Bayes approach. This model handles the AE count data with excess zeros and allows borrowing information from related AEs. The proposed approach was evaluated by simulation studies and was further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS) dataset. The proposed method is implemented in an R package available at https://github.com/umich-biostatistics/zGPS.AO.

Optimal confidence intervals for the relative risk and odds ratio.

The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the two parameters through approximate or exact confidence intervals. However, these intervals may be liberal or conservative. A natural question is whether the intervals can be further improved in maintaining the correct confidence coefficient of an approximate interval or shortening an exact but conservative interval. In this article, when two independent binomials are observed we offer an effort to improve any of the existing intervals by applying the h $$ h $$ -function method. In particular, if the given interval is approximate, then the improved interval is exact; if the given interval is exact, then the improved interval is a subset of the given interval. This method is also applied multiple times to the improved intervals until the final resultant interval cannot be shortened any further. To demonstrate the effectiveness of the method, we use three real datasets to illustrate in detail how several good intervals in practice are improved. Two exact intervals are then recommended for estimating each of the two parameters in different scenarios.

Negative log-binomial model with optimal robust variance to estimate the prevalence ratio, in cross-sectional population studies.

BACKGROUND: Cross-sectional studies are useful for the estimation of prevalence of a particular event with concerns in specific populations, as in the case of diseases or other public health interests. Most of these studies have been carried out with binary binomial logistic regression model which estimates OR values that could be overestimated due to the adjustment of the model. Thus, the selection of the best multivariate model for cross-sectional studies is a priority to control the overestimation of the associations. METHODS: We compared the precision of the estimates of the prevalence ratio (PR) of the negative Log-binomial model (NLB) with Mantel-Haenszel (MH) and the regression models Cox, Log-Poisson, Log-binomial, and the OR of the binary logistic regression in population-based cross-sectional studies. The prevalence from a previous cross-sectional study carried out in Colombia about the association of mental health disorders with the consumption of psychoactive substances (e.g., cocaine, marijuana, cigarette, alcohol and risk of consumption of psychoactive substances) were used. The precision of the point estimates of the PR was evaluated for the NLB model with robust variance estimates, controlled with confounding variables, and confidence interval of 95%. RESULTS: The NLB model adjusted with robust variance showed accuracy in the measurements of crude PRs, standard errors of estimate and its corresponding confidence intervals (95%CI) as well as a high precision of the PR estimate and standard errors of estimate after the adjustment of the model by grouped age compared with the MH PR estimate. Obtained PRs and 95%CI entre NLB y MH were: cocaine consumption (2.931,IC95%: 0.723-11.889 vs. 2.913, IC95%: 0.786-12.845), marijuana consumption (3.444, IC95%: 1.856-6.391 vs. 3.407, IC95%: 1.848, 6.281), cigarette smoking (2.175,IC95%: 1.493, 3.167 vs. 2.209, IC95%: 1.518-3.214), alcohol consumption (1.243,IC95%: 1.158-1.334 vs. 1.241, IC95%: 1.157-1.332), and risk of consumption of psychoactive substances (1.086, IC95%: 1.047-1.127 vs. 1.086, IC95%: 1.047, 1.126). The NLB model adjusted with robust variance showed mayor precision when increasing the prevalence, then the other models with robust variance with respect to MH. CONCLUSIONS: The NLB model with robust variance was shown as a powerful strategy for the estimation of PRs for cross-sectional population-based studies, as high precision levels were identified for point estimators, standard errors of estimate and its corresponding confidence intervals, after the adjustment of confounding variables. In addition, it does not represent convergence issues for high prevalence cases (as it occur with the Log-binomial model) and could be considered in cases of overdispersion and with greater precision and goodness of fit than the other models with robust variance, as it was shown with the data set of the cross-sectional study used in here.

A discrete, stochastic model of colonial phytoplankton population size structure: Development and application to in situ imaging-in-flow cytometer observations of Dinobryon.

The scientific community lacks models for the dynamic changes in population size structure that occur in colonial phytoplankton. This is surprising, as size is a key trait affecting many aspects of phytoplankton ecology, and colonial forms are very common. We aim to fill this gap with a new discrete, stochastic model of dynamic changes in phytoplankton colonies' population size structure. We use the colonial phytoplankton Dinobryon as a proof-of-concept organism. The model includes four stochastic functions-division, stomatocyst production, colony breakage, and colony loss-to determine Dinobryon population size structure and populations counts. Although the functions presented here are tailored to Dinobryon, the model is readily adaptable to represent other colonial taxa. We demonstrate how fitting our model to in situ observations of colony population size structure can provide a powerful approach to explore colony size dynamics. Here, we have (1) collected high-frequency in situ observations of Dinobryon in Lac (Lake) Montjoie (Quebec, Canada) in 2013 with a moored Imaging FlowCytobot (IFCB) and (2) fit the model to those observations with a genetic algorithm solver that extracts parameter estimates for each of the four stochastic functions. As an example of the power of this model-data integration, we also highlight ecological insights into Dinobryon colony size and stomatocyst production. The Dinobryon population was enriched in larger, flagellate-rich colonies near bloom initiation and shifted to smaller and emptier colonies toward bloom decline.

Does illicit amphetamine seizures quantity associated with amphetamine use disorder related admissions in Saudi Arabia?

BACKGROUND: Illicit amphetamine-type stimulants (ATS) trafficking activities have increased substantially in Saudi Arabia over the last 10 years. In the period 2013-2017 Saudi Arabia seized the largest quantities of amphetamine at the global level. The current study examines whether the increased quantity of ATS seizures has an impact on amphetamine use disorder admissions. METHOD: This is an ecological study combining two datasets, the first dataset was obtained from United Nations Office on Drugs and Crime (UNODC), and the Al-Amal Hospital Electronic Health Record System in the city of Dammam, Eastern region of Saudi Arabia from 2005 to 2018. The annual incidence of patients diagnosed with amphetamine use was the dependent variable. The independent variable was the annual reported count of seized quantities of ATS in Saudi Arabia. We used a random intercept Negative Binomial model to predict the yearly count of amphetamine use disorder admission rates. RESULTS: A total of 910 amphetamine disorder admission patients in Al-Amal rehabilitation and addiction center, and the quantity equivalent to 200 tons of ATS was seized from 2005 to 2018. The amphetamine disorder admission rate has increased from 1.33% in 2005 to 18.27% in 2018. For each one-unit increase in the amphetamine confiscated quantities, the amphetamine use disorder admission rate increased by 49 to 88%. CONCLUSION: The current study found that reported amphetamine seized quantities were significantly and positively associated with the increase of amphetamine use disorder-related admission rates. In 2018, both ATS seized quantities and admission rates significantly increased, nearly doubling from the previous year. Rigorous, and multidisciplinary interventional studies to evaluate factors associated with increasing abuse of ATS should be a priority for policymakers and researchers in Saudi.

Multiple paternity and the number of offspring: A model reveals two major groups of species.

Parentage analyses via microsatellite markers have revealed multiple paternity within the broods of polytocous species of mammals, reptiles, amphibians, fishes and invertebrates. The widespread phenomenon of multiple paternity may have attending relationships with such evolutionary processes as sexual selection and kin selection. However, just how much multiple paternity should a species exhibit? We developed Bayesian null models of how multiple paternity relates to brood sizes. For each of 114 species with published data on brood sizes and numbers of sires, we compared our null model estimates to published frequencies of multiple paternity. The majority of species fell close to our null model, especially among fish and invertebrate species. Some species, however, had low probabilities of multiple paternity, far from the predictions of the null model, likely due to sexual selection and environmental constraints. We suggest a major division among species' mating systems between those with close to random mating and high levels of multiple paternity, and those with constraints that produce low levels of multiple paternity.

From "below chance" to "a single error is one too many": Evaluating various thresholds for invalid performance on two forced choice recognition tests.

This study was designed to empirically evaluate the classification accuracy of various definitions of invalid performance in two forced-choice recognition performance validity tests (PVTs; FCRCVLT-II and Test of Memory Malingering [TOMM-2]). The proportion of at and below chance level responding defined by the binomial theory and making any errors was computed across two mixed clinical samples from the United States and Canada (N = 470) and two sets of criterion PVTs. There was virtually no overlap between the binomial and empirical distributions. Over 95% of patients who passed all PVTs obtained a perfect score. At chance level responding was limited to patients who failed ≥2 PVTs (91% of them failed 3 PVTs). No one scored below chance level on FCRCVLT-II or TOMM-2. All 40 patients with dementia scored above chance. Although at or below chance level performance provides very strong evidence of non-credible responding, scores above chance level have no negative predictive value. Even at chance level scores on PVTs provide compelling evidence for non-credible presentation. A single error on the FCRCVLT-II or TOMM-2 is highly specific (0.95) to psychometrically defined invalid performance. Defining non-credible responding as below chance level scores is an unnecessarily restrictive threshold that gives most examinees with invalid profiles a Pass.

[The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples].

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

Determination of the number of observers needed to evaluate a subjective test and its application in two PD-L1 studies.

In pathological studies, subjective assays, especially companion diagnostic tests, can dramatically affect treatment of cancer. Binary diagnostic test results (ie, positive vs negative) may vary between pathologists or observers who read the tumor slides. Some tests have clearly defined criteria resulting in highly concordant outcomes, even with minimal training. Other tests are more challenging. Observers may achieve poor concordance even with training. While there are many statistically rigorous methods for measuring concordance between observers, we are unaware of a method that can identify how many observers are needed to determine whether a test can reach an acceptable concordance, if at all. Here we introduce a statistical approach to the assessment of test performance when the test is read by multiple observers, as would occur in the real world. By plotting the number of observers against the estimated overall agreement proportion, we can obtain a curve that plateaus to the average observer concordance. Diagnostic tests that are well-defined and easily judged show high concordance and plateau with few interobserver comparisons. More challenging tests do not plateau until many interobserver comparisons are made, and typically reach a lower plateau or even 0. We further propose a statistical test of whether the overall agreement proportion will drop to 0 with a large number of pathologists. The proposed analytical framework can be used to evaluate the difficulty in the interpretation of pathological test criteria and platforms, and to determine how pathology-based subjective tests will perform in the real world. The method could also be used outside of pathology, where concordance of a diagnosis or decision point relies on the subjective application of multiple criteria. We apply this method in two recent PD-L1 studies to test whether the curve of overall agreement proportion will converge to 0 and determine the minimal sufficient number of observers required to estimate the concordance plateau of their reads.

Multiple imputation confidence intervals for the mean of the discrete distributions for incomplete data.

Confidence intervals for the mean of discrete exponential families are widely used in many applications. Since missing data are commonly encountered, the interval estimation for incomplete data is an important problem. The performances of the existing multiple imputation confidence intervals are unsatisfactory. We propose modified multiple imputation confidence intervals to improve the existing confidence intervals for the mean of the discrete exponential families with quadratic variance functions. A simulation study shows that the coverage probabilities of the modified confidence intervals are closer to the nominal level than the existing confidence intervals when the true mean is near the boundaries of the parameter space. These confidence intervals are also illustrated with real data examples.

Improving large-scale estimation and inference for profiling health care providers.

Provider profiling has been recognized as a useful tool in monitoring health care quality, facilitating inter-provider care coordination, and improving medical cost-effectiveness. Existing methods often use generalized linear models with fixed provider effects, especially when profiling dialysis facilities. As the number of providers under evaluation escalates, the computational burden becomes formidable even for specially designed workstations. To address this challenge, we introduce a serial blockwise inversion Newton algorithm exploiting the block structure of the information matrix. A shared-memory divide-and-conquer algorithm is proposed to further boost computational efficiency. In addition to the computational challenge, the current literature lacks an appropriate inferential approach to detecting providers with outlying performance especially when small providers with extreme outcomes are present. In this context, traditional score and Wald tests relying on large-sample distributions of the test statistics lead to inaccurate approximations of the small-sample properties. In light of the inferential issue, we develop an exact test of provider effects using exact finite-sample distributions, with the Poisson-binomial distribution as a special case when the outcome is binary. Simulation analyses demonstrate improved estimation and inference over existing methods. The proposed methods are applied to profiling dialysis facilities based on emergency department encounters using a dialysis patient database from the Centers for Medicare & Medicaid Services.

Statistical tests for two-stage adaptive seamless design using short- and long-term binary outcomes.

The adaptive seamless design combining phases II and III into a single trial has been shown growing interest for improving the efficiency of drug development, becoming the most frequent adaptive design type. It typically consists of two stages, the trial objectives being often different in each stage. The primary objectives are to select optimal experimental treatment group(s) in the first stage and compare the efficacy between the selected treatment and control groups in the second stage. In this article, we focus on a two-stage adaptive seamless design, for which treatment selection is based on the short-term binary endpoint and treatment comparison is based on the long-term binary endpoint. We thus propose an exact conditional test as a final analysis, based on the bivariate binomial distribution and given the selected treatment with the most promising short-term endpoint response rate from an interim analysis. Additionally, the mid- p $$ p $$ approach is incorporated to improve conservativeness for an exact test. Simulation studies were conducted to compare the proposed methods with a method based on the combination test. The proposed exact method controlled for type I error rate at the nominal level, regardless of the number of initial treatments or the correlation between short- and long-term endpoints. In terms of the treatment comparison power, the proposed methods are more powerful than that based on the combination test in the scenarios, with only one treatment being effective.

Comparisons of statistical distributions for cluster sizes in a developing pandemic.

BACKGROUND: We consider cluster size data of SARS-CoV-2 transmissions for a number of different settings from recently published data. The statistical characteristics of superspreading events are commonly described by fitting a negative binomial distribution to secondary infection and cluster size data as an alternative to the Poisson distribution as it is a longer tailed distribution, with emphasis given to the value of the extra parameter which allows the variance to be greater than the mean. Here we investigate whether other long tailed distributions from more general extended Poisson process modelling can better describe the distribution of cluster sizes for SARS-CoV-2 transmissions. METHODS: We use the extended Poisson process modelling (EPPM) approach with nested sets of models that include the Poisson and negative binomial distributions to assess the adequacy of models based on these standard distributions for the data considered. RESULTS: We confirm the inadequacy of the Poisson distribution in most cases, and demonstrate the inadequacy of the negative binomial distribution in some cases. CONCLUSIONS: The probability of a superspreading event may be underestimated by use of the negative binomial distribution as much larger tail probabilities are indicated by EPPM distributions than negative binomial alternatives. We show that the large shared accommodation, meal and work settings, of the settings considered, have the potential for more severe superspreading events than would be predicted by a negative binomial distribution. Therefore public health efforts to prevent transmission in such settings should be prioritised.

Entanglement and Fisher Information for Atoms-Field System in the Presence of Negative Binomial States.

We developed a quantum scheme of two atoms (TAs) and field initially in a negative binomial state (NBS). We displayed and discussed the physical implications of the obtained results in terms of the physical parameters of the model. By considering that the TAs were initially prepared in a maximally entangled state, and that the single-mode field was in the NBS, the dynamics of quantum phenomena such TAs-field entanglement, TAs entanglement, and parameter estimation were examined. We found that the quantum quantifiers exhibited randomly quasi-periodic and periodic oscillations that depended on the success probability, photon number transition, and the intensity-dependent coupling effect. Furthermore, we analyzed the connection between the dynamical behavior of the quantifiers. This system can be compared with some other ones that are being discussed in the literature, in order to realize the quantum entanglement, and to control the precision of the parameter estimation.

Logistic Regression Model for a Bivariate Binomial Distribution with Applications in Baseball Data Analysis.

There has been a considerable amount of literature on binomial regression models that utilize well-known link functions, such as logistic, probit, and complementary log-log functions. The conventional binomial model is focused only on a single parameter representing one probability of success. However, we often encounter data for which two different success probabilities are of interest simultaneously. For instance, there are several offensive measures in baseball to predict the future performance of batters. Under these circumstances, it would be meaningful to consider more than one success probability. In this article, we employ a bivariate binomial distribution that possesses two success probabilities to conduct a regression analysis with random effects being incorporated under a Bayesian framework. Major League Baseball data are analyzed to demonstrate our methodologies. Extensive simulation studies are conducted to investigate model performances.