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BACKGROUND: Since the inception of the Youth Risk Behavior Surveillance System in 1991, all surveys have been conducted in schools, using paper and pencil instruments (PAPI). For the 2019 YRBSS, sites were offered the opportunity to conduct their surveys using electronic data collection. This study aimed to determine whether differences in select metrics existed between students who completed the survey electronically versus using PAPI. METHODS: Thirty risk behaviors were examined in this study. Data completeness, response rates and bivariate comparisons of risk behavior prevalence between administration modes were examined. RESULTS: Twenty‐nine of 30 questions examined had more complete responses among students using electronic surveys. Small differences were found for student and school response rates between modes. Twenty‐five of 30 adolescent risk behaviors showed no mode effect. CONCLUSIONS: Seven of 44 states and DC participated electronically. Because survey data were more complete; school and student response rates were consistent; and minor differences existed in risk behaviors between modes, the acceptability of collecting data electronically was demonstrated. [ABSTRACT FROM AUTHOR]

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research. [ABSTRACT FROM AUTHOR]

Aims and objectives. The aims of this study were to measure skin moisture and transepidermal water loss after application of uncoated paper and to compare skin moisture and transepidermal water loss after use of uncoated paper and disposable underpads. Study design. The study was a cross-over, prospective, open-labeled, randomized trial. Sample and setting. Bedridden patients aged ≥18 years at a medical center in Korea were included. Treatment order was randomly assigned using block randomization, with a block size of 4 and an assignment rate of one-by-one. Methods. Skin moisture was measured using a Corneometer 825 and transepidermal water loss was measured using a Tewameter 300. Results. Skin moisture after application of an uncoated paper was significantly lower than observed after application of a disposable underpad (mean 40·6 and SD 13·1 vs. mean 64·6 and SD 23·7, p < 0·001). Transepidermal water loss also showed greater health scores after using uncoated paper (mean 11·1 and SD 5·7 g/m2/hour) than after applying a disposable underpad (mean 23·2 and SD 11·1 g/m2/hour, p < 0·001). There were no statistical between-group differences in room temperature, relative humidity, and body temperature. Conclusion. We found that uncoated paper was helpful in avoiding excessive moisture without adverse effects. Relevance to clinical practice. As indicated by the results of this study, uncoated paper can be applied to bed-ridden patients who required incontinence care. Nurses may consider using uncoated paper as one of nursing methods in the routine care of bed-ridden patients for moisture control. [ABSTRACT FROM AUTHOR]

Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. We have made a structured presentation on these measures for both trial success and clinical success: first, we have summarized the expressions of CP, PPoS and PoS in a general setting and subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. Many of these expressions are previously published; however, some of the expressions are very new including the expressions for testing median of time-to-event endpoint in a single-arm trial. We have also shown that 1 / (no. of events) consistently underestimates the variance of log(median) and alternative expression for variance was derived. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R and in R shiny app https://ppos.shinyapps.io/public/. [ABSTRACT FROM AUTHOR]

In many statistical analysis, a finite population may contain a large proportion of zero-and-one values that make the population distribution severely skew. Confidence intervals based on a normal approximation (NA) for such data may have low coverage probabilities. In this paper, we apply the methods of jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL) to discuss the confidence intervals for the mean of zero-and-one inflated population. Asymptotic distributions of the likelihood-type statistics are studied. Simulations are conducted to compare coverage probabilities with other methods under different distributions. Real data is given to illustrate the procedure of proposed methods. [ABSTRACT FROM AUTHOR]

This article aims to introduce the inverse new XLindley distribution, a further extension of the new XLindley distribution. The article explores various properties of the proposed model, such as the quantile function, stochastic orders, entropies, fuzzy reliability, moments, and stress–strength estimation. The paper also compares different methods of estimating the parameters of the proposed model and evaluates their performance using a simulation study. Moreover, the paper demonstrates the usefulness of the proposed model by applying it to two real datasets. The article shows that the proposed model fits the data better than seven existing models based on model selection criteria, goodness-of-fit test statistics, and graphical visualizations. The paper concludes that the new model can be a valuable tool for modeling and analyzing hazard functions or survival data in various fields and contributing to probability theory and statistical inferences. [ABSTRACT FROM AUTHOR]

Background: Given an observed test statistic and its degrees of freedom, one may compute the observed P value with most statistical packages. It is unknown to what extent test statistics and P values are congruent in published medical papers. Methods: We checked the congruence of statistical results reported in all the papers of volumes 409-412 of Nature (2001) and a random sample of 63 results from volumes 322-323 of BMJ (2001). We also tested whether the frequencies of the last digit of a sample of 610 test statistics deviated from a uniform distribution (i.e., equally probable digits). Results: 11.6% (21 of 181) and 11.1% (7 of 63) of the statistical results published in Nature and BMJ respectively during 2001 were incongruent, probably mostly due to rounding, transcription, or type-setting errors. At least one such error appeared in 38% and 25% of the papers of Nature and BMJ, respectively. In 12% of the cases, the significance level might change one or more orders of magnitude. The frequencies of the last digit of statistics deviated from the uniform distribution and suggested digit preference in rounding and reporting. Conclusions: This incongruence of test statistics and P values is another example that statistical practice is generally poor, even in the most renowned scientific journals, and that quality of papers should be more controlled and valued. [ABSTRACT FROM AUTHOR]

Williamson G.R. (2003) Journal of Advanced Nursing 44(3), 278–288 Misrepresenting random sampling? A systematic review of research papers in the Journal of Advanced Nursing This paper discusses the theoretical limitations of the use of random sampling and probability theory in the production of a significance level (or P-value) in nursing research. Potential alternatives, in the form of randomization tests, are proposed. Research papers in nursing, medicine and psychology frequently misrepresent their statistical findings, as the P-values reported assume random sampling. In this systematic review of studies published between January 1995 and June 2002 in the Journal of Advanced Nursing, 89 (68%) studies broke this assumption because they used convenience samples or entire populations. As a result, some of the findings may be questionable. The key ideas of random sampling and probability theory for statistical testing (for generating a P-value) are outlined. The result of a systematic review of research papers published in the Journal of Advanced Nursing is then presented, showing how frequently random sampling appears to have been misrepresented. Useful alternative techniques that might overcome these limitations are then discussed. This review is limited in scope because it is applied to one journal, and so the findings cannot be generalized to other nursing journals or to nursing research in general. However, it is possible that other nursing journals are also publishing research articles based on the misrepresentation of random sampling. The review is also limited because in several of the articles the sampling method was not completely clearly stated, and in this circumstance a judgment has been made as to the sampling method employed, based on the indications given by author(s). Quantitative researchers in nursing should be very careful that the statistical techniques they use are appropriate for the design and sampling methods of their studies. If the techniques they employ are not appropriate, they run the risk of misinterpreting findings by using inappropriate, unrepresentative and biased samples. [ABSTRACT FROM AUTHOR]

Objective: To describe the technical performance of nested PCR for identifying hepatitis B viral (HBV) DNA. Methods: Hepatitis B viral DNA was extracted from a dried bloodstain on filter paper by a Chelex-100 method. Then the DNA fragment was amplified by nested polymerase chain reaction (PCR). The sensitivity and specificity of this method were also analyzed. The positive rate of the nested PCR-based method was compared with that of the enzyme-linked immunosorbent assays (ELISA) method. Results: The lowest detection limit of the test was 5 copies of HBV DNA per μL by this method. McNemar's test showed that the difference between the positive rates of these 2 methods was not statistically significant (P=0.289, P>0.05). Hepatitis B viral test results showed a good concordance between these 2 methods (kappa=0.727). Conclusion: A very small amount of the dried blood sample is required for the detection, which could overcome the shortage of the normal ELISA method that requires a relatively large amount of fresh blood samples. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper provides a review of statistical methods for tumor-agnostic clinical trials. In particular, the review focuses on basket trials and provides methodological insights into various Bayesian approaches. The key concept of borrowing information through Bayesian hierarchical models is emphasized, and some novel trial designs are introduced. The review is expected to provide oncology and biostatistics researchers with more exposure to powerful Bayesian methods for the design and analysis of tumor-agnostic clinical trials. Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings. [ABSTRACT FROM AUTHOR]

One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g. within the bid-ask spread) for assets with different queue dynamics and trading activity. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problems of stability and stabilization for discrete‐time switched linear systems with a new class of switching signals. The considered switching signals are of random mode‐dependent sojourn‐time (RMST) property, which is capable of describing the mode‐dependent sojourn‐time period consisting of a fixed part with no switching and a random part with known sojourn‐time expectation. Compared with the previous studies assuming that the switching dynamics of the random part can be described by a Markov chain, the length of the random part is allowed to obey any distribution including geometric distribution. Besides, the mode switching of RMST switching is arbitrary indicating that it requires no statistical information of transition probabilities. The criteria of stability and stabilization are derived for the RMST switched systems. To further reduce the limitation of RMST switching on switching frequency, a concept of random mode‐dependent persistent sojourn‐time (RMPST) is proposed. Then, in the presence of asynchronous switching, the conditions of the existence of controllers are presented for the switched systems with persistent sojourn‐time expectation. A numerical example and a practical example of a space robot manipulator are given to demonstrate the effectiveness and applicability of the developed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper we investigate the following question: if NP is slightly hard on average, is it very hard on average? We give a positive answer: if there is a function in NP which is infinitely often balanced and (1-1/poly(n))-hard for circuits of polynomial size, then there is a function in NP which is infinitely often (1/2+n-1/2+#x03B5;)-hard for circuits of polynomial size. Our proof technique is to generalize the Yao XOR Lemma, allowing us to characterize nearly tightly the hardness of a composite function g( f(x1),…,f(xn)) in terms of: (i) the original hardness of f, and (ii) the expected bias of the function g when subjected to random restrictions. The computational result we prove essentially matches an information-theoretic bound. [Copyright &y& Elsevier]

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for K-class problems (Wu et al., 2010; Wang et al., 2019), where K is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in K. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in K. Though not the most efficient in computation, the OVA is found to have the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate their finite sample performance. [ABSTRACT FROM AUTHOR]

Much statistical teaching and many research reports focus on the 'null hypothesis significance test'. Yet the correct meaning and interpretation of statistical significance tests is elusive. Misinterpretations are both common and persistent, leading many to question whether significance tests should be used at all. While most take aim at the arbitrary declaration of p < 0.05 as a threshold for determining 'significance', others extend the critique to suggest the 'p-value' should be dispensed with entirely. P-values and significance tests are still widely used as if they give a measure of the size and importance of relationships, even though this misunderstanding has been observed and discussed for many years. We argue that p-values and significance tests are intrinsically misleading. Point estimates of relationships and confidence intervals give direct information about the effect and the uncertainty of the estimate without recourse to interpreting how a particular p-value might have arisen or indeed referring to them at all. In this paper we briefly outline some of the problems with significance testing, offer a number of examples selected from a recent issue of the International Journal of Nursing Studies and discuss some proposed responses to these problems. We conclude by offering some guidance to authors reporting statistical tests in journals and present a position statement that has been adopted by the International Journal of Nursing Studies to guide its' authors in reporting the results of statistical analyses. While stopping short of calling for an outright ban on reporting p-values and significance tests we urge authors (and journals) to place more emphasis on measures of effect and estimates of precision/uncertainty and, following the position of the American Statistical Association, emphasise that authors (and readers) should avoid using 0.05 or any other cut off for a p-value as the basis for a decision about the meaningfulness/importance of an effect. If point estimates and confidence intervals are used, then the p-value may be redundant and can be omitted from reports. When authors talk about 'significance' they need to be explicit when referring to statistical significance and we recommend authors adopt the language of 'importance' when talking about effect sizes to avoid any confusion. [ABSTRACT FROM AUTHOR]

This paper is concerned with the asymptotic analysis of sojourn times of random fields with continuous sample paths. Under a very general framework we show that there is an interesting relationship between tail asymptotics of sojourn times and that of supremum. Moreover, we establish the uniform double-sum method to derive the tail asymptotics of sojourn times. In the literature, based on the pioneering research of S. Berman the sojourn times have been utilised to derive the tail asymptotics of supremum of Gaussian processes. In this paper we show that the opposite direction is even more fruitful, namely knowing the asymptotics of supremum of random processes and fields (in particular Gaussian) it is possible to establish the asymptotics of their sojourn times. We illustrate our findings considering i) two dimensional Gaussian random fields, ii) chi-process generated by stationary Gaussian processes and iii) stationary Gaussian queueing processes. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to assess the influence of perceived work environment, empowerment and psychological stress on job burnout among nurses working at the time of the COVID‐19 pandemic. Background: Nurses experienced high levels of job burnout during the pandemic, which impacted their mental health and well‐being. Studies investigating the influence of work environment, empowerment and stress on burnout during the time of COVID‐19 are limited. Design: The study utilized a cross‐sectional design. Methods: Data were collected from 351 nurses in Oman between January and March 2021. The Maslach Burnout Inventory, the Practice Environment Scale of the Nursing Work Index, the Conditions of Work Effectiveness Questionnaire and the Perceived Stress Scale were used to assess study variables. Results: About two‐thirds of the nurses (65.6%) reported high levels of job burnout. Nurse managers' ability, leadership and support; staffing and resources adequacy; and nurses' access to support were significant factors associated with a reduced level of burnout. Conclusion: Supporting nurses during the crisis, ensuring adequate staffing levels and providing sufficient resources are critical to lower job burnout. Creating a positive and empowered work environment is vital to enhance nurses' retention during the pandemic. Summary statement: What is already known about this topic? Nurses' perceived work environment and structural empowerment have been associated with job burnout. However, their impact during the COVID‐19 has not been examined.As nurses' psychological stress increases, their job burnout increases. What this paper adds? Nurses experienced high levels of job burnout during the COVID‐19 pandemic.Working in a favourable environment and having access to support can reduce nurses' job burnout during the crisis. The implications of this paper During pandemics, ensuring adequate staffing levels and providing sufficient resources are critical to lower job burnout and enhance nurse retention. [ABSTRACT FROM AUTHOR]

Owing to probabilistic uncertainties associated with seasonal forecasts, especially over areas such as southern Africa where forecast skill is limited, non-climatologists and users of such forecasts frequently prefer them to be presented or distributed in terms of the likelihood (expressed as a probability) of certain categories occurring or thresholds being exceeded. Probabilistic forecast verification is needed to verify such forecasts. Whilst the resulting verification statistics can provide clear insights into forecast attributes, they are often difficult to understand, which might hinder forecast uptake and use. This problem can be addressed by issuing forecasts with some understandable evidence of skill, with the purpose of reflecting how similar forecasts may have performed in the past. In this paper, we present a range of different probabilistic forecast verification scores, and determine if these statistics can be readily compared to more commonly known and understood ‘ordinary’ correlations between forecasts and their associated observations – assuming that ordinary correlations are more intuitively understood and informative to seasonal forecast users. Of the range of scores considered, the relative operating characteristics (ROC) was found to be the most intrinsically similar to correlation. [ABSTRACT FROM AUTHOR]

Recurrent events—outcomes that an individual can experience repeatedly over the course of follow-up—are common in epidemiologic and health services research. Studies involving recurrent events often focus on time to first occurrence or on event rates, which assume constant hazards over time. In this paper, we contextualize recurrent event parameters of interest using counterfactual theory in a causal inference framework and describe an approach for estimating a target parameter referred to as the mean cumulative count. This approach leverages inverse probability weights to control measured confounding with an existing (and underutilized) nonparametric estimator of recurrent event burden first proposed by Dong et al. in 2015. We use simulations to demonstrate the unbiased estimation of the mean cumulative count using the weighted Dong-Yasui estimator in a variety of scenarios. The weighted Dong-Yasui estimator for the mean cumulative count allows researchers to use observational data to flexibly estimate and contrast the expected number of cumulative events experienced per individual by a given time point under different exposure regimens. We provide code to ease application of this method. [ABSTRACT FROM AUTHOR]

Abstract: Bayesian statistics was applied to a small sample database of the tensile properties of five different brands of writing paper which were perceptibly similar. The measured parameters in the database were found to overlap for the five brands. This posed a limitation to the application of the classical approach for “match” or “no match”. It was found that using Bayesian statistics for source correspondence, a mere 2–3 measurements corresponding to a particular brand raised the probabilities associated with that brand to as high a 72% and eliminating a couple of brands. [Copyright &y& Elsevier]

Aim: This study aimed to explore the level and influencing factors of fatigue in patients with rheumatoid arthritis. Methods: This cross‐sectional study was conducted in 243 patients with rheumatoid arthritis from April 2016 to March 2017. The Bristol Rheumatoid Arthritis Fatigue Multi‐Dimensional Questionnaire, Arthritis Self‐Efficacy Scale‐8, Visual Analogue Scale for pain, physical function subscale of Short Form 36‐Item Health Survey, Hospital Anxiety and Depression Scale, Perceived Social Support Scale, Pittsburgh Sleep Quality Index and a self‐designed demographic and disease‐related information questionnaire were used to collect the data. Stepwise linear multiple regression was used to clarify the impact of statistically significant variables (P < 0.05) in the independent sample t test, one‐way ANOVA and correlation analysis on the level of fatigue. Results: Stepwise linear multiple regression analyses showed that disease activity, self‐efficacy, physical function, pain, depression, duration of morning stiffness and anxiety were major factors influencing fatigue in patients with rheumatoid arthritis, which explained 59.5% of the total variance. Conclusion: Our study demonstrated a moderate level of fatigue in Chinese patients with rheumatoid arthritis. In clinical practice, nurses should explore individualized intervention programmes based on related predictors of fatigue to help patients relieve fatigue. Summary statement: What is already known about this topic? Fatigue is a common problem in patients with rheumatoid arthritis (RA).Little research has investigated what factors are associated with fatigue in patients with RA.Nurses need to understand the factors that potentially influence fatigue in patients with RA, in order to improve the quality of life. What this paper adds? Fatigue is moderate in Chinese patients with RA, which is lower than the results of western countries.Disease activity, self‐efficacy, physical function, pain, depression, duration of morning stiffness and anxiety are important factors affecting fatigue in patients with RA. The implications of this paper: Special attention should be paid to the RA patients with high level of fatigue.The findings of this study highlight the need to develop effective strategies to alleviate fatigue and eventually improve the quality of life in patients with RA. [ABSTRACT FROM AUTHOR]

The use of technology in teaching and learning the topic of probability and statistics in the educational system in our country, Albania, has the potential to be encouraged in different level of classes in the educational system. Lee and Hollebrands (2011) have done better characterization of teaching probability and statistics with the help of technology, through the integration of Statistical Knowledge, Technological Statistical Knowledge, and Technological Pedagogical Statistical Knowledge. Also, we have integrated this framework with Pedagogical Knowledge as a very important factor. In this paper is given as a case study, the process of mastering the concepts of probability and statistics, with students of a lower secondary school in our country to understand the role of technology in the process of teaching and learning, realized concretely during the lessons, specifically throughout a project-based learning by demonstrating the results of the project by the students themselves. Our students recognize and analyze the data, then collect and represent it, to provide graphical presentation Thus, we focus on the potential advantages of using technology in teaching and learning the topics of probability and statistics in school mathematics during different levels of educational process in our country and beyond. [ABSTRACT FROM AUTHOR]

Given a probability distribution p := {pk}k=1∞ on the positive integers, there are two natural ways to construct a random permutation in Sn or a random permutation of N from IID samples from p. One is called the p-biased construction and the other the p-shifted construction. In the first part of the paper we consider the case that the distribution p is the geometric distribution with parameter 1 - q ∈ (0, 1). In this case, the p-shifted random permutation has the Mallows distribution with parameter q. Let Pnb;Geo(1-q) and Pns;Geo(1-q) denote the biased and the shifted distributions on Sn. The expected number of inversions of a permutation under Pns;Geo(1-q) is greater than under Pnb;Geo(1-q), and under either of these distributions, a permutation tends to have many fewer inversions than it would have under the uniform distribution. For fixed n, both Pnb;Geo(1-q) and Pns;Geo(1-q) converge weakly as q → 1 to the uniform distribution on Sn. We compare the biased and the shifted distributions by studying the inversion statistic under ... and ... for various rates of convergence of qn to 1. In the second part of the paper we consider p-biased and p-shifted permutations for the case that the distribution p is itself random and distributed as a GEM(θ)-distribution. In particular, in both the GEM(θ)-biased and the GEM(θ)-shifted cases, the expected number of inversions behaves asymptotically as it does under the Geo(1 - q)-shifted distribution with θ = q / 1-q . This allows one to consider the GEM(θ)-shifted case as the random counterpart of the Geo(q)-shifted case. We also consider another p-biased distribution with random p for which the expected number of inversions behaves asymptotically as it does under the Geo(1 - q)-biased case with θ and q as above, and with θ → ∞ and q → 1 [ABSTRACT FROM AUTHOR]

Simple Summary: In the modern era, characterized by parenchymal-sparing procedures, in some cases pneumonectomy remains the only therapeutic approach to achieving oncological radicality. One of the most feared complications is undoubtedly respiratory failure and ARDS. Its cause after pneumonectomy is still unclear, and the study of risk factors is a subject of debate. In this paper, we evaluate the main risk factors for ARDS of a large cohort of patients and we classify them in four classes of growing risk in order to quantify their postoperative risk of ARDS and facilitate their global management. (1) Background: The cause of ARDS after pneumonectomy is still unclear, and the study of risk factors is a subject of debate. (2) Methods: We reviewed a large panel of pre-, peri- and postoperative data of 211 patients who underwent pneumonectomy during the period 2014–2021. Univariable and multivariable logistic regression was used to quantify the association between preoperative parameters and the risk of developing ARDS, in addition to odds ratios and their respective 95% confidence intervals. A backward stepwise selection approach was used to limit the number of variables in the final multivariable model to significant independent predictors of ARDS. A nomogram was constructed based on the results of the final multivariable model, making it possible to estimate the probability of developing ARDS. Statistical significance was defined by a two-tailed p-value < 0.05. (3) Results: Out of 211 patients (13.3%), 28 developed ARDS. In the univariate analysis, increasing age, Charlson Comorbidity Index and ASA scores, DLCO < 75% predicted, preoperative C-reactive protein (CRP), lung perfusion and duration of surgery were associated with ARDS; a significant increase in ARDS was also observed with decreasing VO2max level. Multivariable analysis confirmed the role of ASA score, DLCO < 75% predicted, preoperative C-reactive protein and lung perfusion. Using the nomogram, we classified patients into four classes with rates of ARDS ranking from 2.0% to 34.0%. (4) Conclusions: Classification in four classes of growing risk allows a correct preoperative stratification of these patients in order to quantify the postoperative risk of ARDS and facilitate their global management. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer-related mortality among males in the US. Definitive radiation therapy (RT) plays an important role in curative-intent treatment for localized PCa and can be delivered with several different techniques, depending on the availability of resources and patient-specific criteria. With an analysis of the extensive National Cancer Database, this paper investigates trends in utilization, survival probability, and factors associated with overall survival of six common RT modalities utilized for the treatment of PCa patients—stratified by the three risk groups. Background: In this study, the utilization rates and survival outcomes of different radiotherapy techniques are compared in prostate cancer (PCa) patients stratified by risk group. Methods: We analyzed an extensive data set of N0, M0, non-surgical PCa patients diagnosed between 2004 and 2015 from the National Cancer Database (NCDB). Patients were grouped into six categories based on RT modality: an intensity-modulated radiation therapy (IMRT) group with brachytherapy (BT) boost, IMRT with/without IMRT boost, proton therapy, stereotactic body radiation therapy (SBRT), low-dose-rate brachytherapy (BT LDR), and high-dose-rate brachytherapy (BT HDR). Patients were also stratified by the National Comprehensive Cancer Network (NCCN) guidelines: low-risk (clinical stage T1–T2a, Gleason Score (GS) ≤ 6, and Prostate-Specific Antigen (PSA) < 10), intermediate-risk (clinical stage T2b or T2c, GS of 7, or PSA of 10–20), and high-risk (clinical stage T3–T4, or GS of 8–10, or PSA > 20). Overall survival (OS) probability was determined using a Kaplan–Meier estimator. Univariate and multivariate analyses were performed by risk group for the six treatment modalities. Results: The most utilized treatment modality for all PCa patients was IMRT (53.1%). Over the years, a steady increase in SBRT utilization was observed, whereas BT HDR usage declined. IMRT-treated patient groups exhibited relatively lower survival probability in all risk categories. A slightly better survival probability was observed for the proton therapy group. Hormonal therapy was used for a large number of patients in all risk groups. Conclusion: This study revealed that IMRT was the most common treatment modality for PCa patients. Brachytherapy, SBRT, and IMRT+BT exhibited similar survival rates, whereas proton showed slightly better overall survival across the three risk groups. However, analysis of the demographics indicates that these differences are at least in part due to selection bias. [ABSTRACT FROM AUTHOR]

This paper adopts hierarchical regression and ANOVA techniques to analyse the data from 'Child Well‐being Study in Greater Taipei' in view of examining the association between the practice of family routines and different work schedules for parents with children in primary school. The findings conclude that family routines are relatively unstable when parents work atypical hours, with this association more significant for mothers than for fathers. Moreover, the practice of family routines becomes more unstable when the number of parents working atypical hours increases, while there is no significant change when more parents take jobs with flexible working hours. [ABSTRACT FROM AUTHOR]

Bayesian methods are becoming increasingly used in applied psychological research. Previous researchers have thoroughly written about much of the details already, including the philosophy underlying Bayesian methods, computational issues associated with Bayesian model estimation, Bayesian model development and summary, and the role of Bayesian methods in the so‐called replication crisis. In this paper, we seek to provide case studies comparing the use of frequentist methods to the use of Bayesian methods in applied psychological research. These case studies are intended to 'illustrate by example' the ways that Bayesian modelling differs from frequentist modelling and the differing conclusions that one may arrive at using the two methods. The intended audience is applied psychological researchers who have been trained in the traditional frequentist framework, who are familiar with mixed‐effects models and who are curious about how statistical results might look in a Bayesian context. Along with our case studies, we provide general opinions and guidance on the use of Bayesian methods in applied psychological research. [ABSTRACT FROM AUTHOR]

This paper outlines Binomial and Poisson distributions which are both used to measure the occurrence of a number of random events within a certain period. [ABSTRACT FROM AUTHOR]

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insures from 2001 to 2016. [ABSTRACT FROM AUTHOR]

The strong limit theory is one of the most important problems in the probability theory. Some results on the convergence of ρ ˜ -mixing random sequences have been presented. In this paper, we study the almost sure convergence for ρ ˜ -mixing random sequences. As a result, we generalize partial results of Wu and Jiang [Wu, Q. Y., and Y. Y. Jiang. 2008. Some strong limit theorems for q ˜ -mixing sequences of random variables. Statistics & Probability Letters 78:1017–23.]. We obtain the mainstream and some corresponding conclusions by using truncation methods and the generalized three-series theorem. A known result is generalized in this paper. [ABSTRACT FROM AUTHOR]

A central issue in assessing the airborne risk of COVID-19 infections in indoor spaces pertains to linking the viral load in infected subjects to the lung deposition probability in exposed individuals through comprehensive aerosol dynamics modelling. In this paper, we achieve this by combining aerosol processes (evaporation, dispersion, settling, lung deposition) with a novel double Poisson model to estimate the probability that at least one carrier particle containing at least one virion will be deposited in the lungs and infect a susceptible individual. Multiple emission scenarios are considered. Unlike the hitherto used single Poisson models, the double Poisson model accounts for fluctuations in the number of carrier particles deposited in the lung in addition to the fluctuations in the virion number per carrier particle. The model demonstrates that the risk of infection for 10-min indoor exposure increases from 1 to 50% as the viral load in the droplets ejected from the infected subject increases from 2 × 108 to 2 × 1010 RNA copies/mL. Being based on well-established aerosol science and statistical principles, the present approach puts airborne risk assessment methodology on a sound formalistic footing, thereby reducing avoidable epistemic uncertainties in estimating relative transmissibilities of different coronavirus variants quantified by different viral loads. [ABSTRACT FROM AUTHOR]

Objective: A point prevalence survey was conducted across Western Australia to monitor adherence to evidence-based practices to prevent falls in hospitals. Study design and methods: A state-wide point prevalence survey of patients and their medical records was conducted across 20 hospitals, over 17 days during May 2014. The survey determined rates of: provision of verbal information to patients; completion of a falls risk screening tool and age based cognitive testing. Univariate and multivariate logistic regression was utilised to determine key risks and opportunities to improve. Results: Information was collected from 2,720 patients. The provision of verbal information to prevent falls, as recalled by patients was 60% (hospital range 35--88%). This was significantly higher for patients with a stay of six or more days or involved in rehabilitation care. Perinatal women were three times less likely to be provided with verbal falls prevention information. A falls risk screening tool was completed for 82% of patients (range 28-98%). Perinatal women, and both adult and paediatric patients compared to older adults, were significantly less likely to have a complete falls risk screening tool. Thirty seven percent of patients within the recommended age ranges had cognitive testing (range 0--87%). Short-term patients and those not involved in rehabilitation, were significantly less likely to have been tested. Discussion: The survey identified differences in patient care and supporting processes across all hospitals. The results have highlighted areas for improvement. Conclusion: There were wide variations across all the hospitals in the provision of falls information, completion of falls risk screening tools and cognitive testing. At significant risk of missing out on falls prevention strategies were short stay patients and perinatal women. Five hospitals had significantly low rates of cognitive testing, indicating a hospital- wide issue rather than specific patient cohorts. Subsequently, the importance of ensuring that falls prevention strategies are conducted is vital to reduce preventable inpatient falls in all care settings. Implications for research, policy and practice: * This was the first state-wide point prevalence study in WA and it has informed the need for further research into the implication of falls risk inpatients. * It was found that falls risk assessment was not conducted for each patient who met the screening criteria. A review of the criteria, and practicability to carry out the assessment may need to be further investigated to determine if the practice should be refined. What is already known about the topic? * Falls in hospitals are a frequent and largely considered preventable health concern. * Falls that occur in hospitals are associated with an increased length of stay and use of health resources. What this paper adds: * This paper offers a comprehensive insight into the variation in hospital falls prevention strategies, from a state-wide perspective. It also identifies perinatal women as a high-risk group who are missing out on falls prevention strategies despite having the potential to fall. * It also gives an insight to health services that not all at risk patients are being screened and those screened are not screened early in their inpatient stay which can be a risk to both patient and staff. [ABSTRACT FROM AUTHOR]

Background: A total of 68% of pre‐school children with cleft palate have speech problems requiring speech therapy. There is a lack of access to regular targeted therapy. Parent training leads to positive outcomes in early communication skills in cleft palate and non‐cleft speech disorders. Connected health has been used to address inadequate access to therapy, providing intervention to those who would not otherwise receive therapy. Aims: To evaluate the speech, activity and participation outcomes of Parent Led, Therapist Supervised, Articulation Therapy (PLAT) compared with routine speech therapy intervention in parent–child dyads. Methods & Procedures: A total of 44 children, aged 2.9–7.5 years, were included in a two‐centre, two‐phase randomized controlled trial. Informed consent and assent were obtained. Participants and speech and language therapists (SLTs) were unblinded to the groups. Parents, in the parent‐trained group (n = 23), attended 2 days' training, received a detailed speech therapy programme, and undertook intervention over 12 weeks supported by the cleft specialist SLT using FaceTime and one face‐to‐face session. In the control arm (n = 21), parent–child dyads received six therapy sessions over 12 weeks with a research SLT, comparable with usual care. Speech recordings were undertaken pre‐ and post‐intervention. Percent consonant correct (PCC) was analysed by external SLTs blinded to the time and group. Activity and participation were measured using the Intelligibility in Context Scale (ICS) and Focus on Outcomes for Children Under Six (FOCUS) questionnaire. Outcomes & Results: There was no evidence of an interaction between Time and Group or an overall statistical difference between groups for PCC scores. There was a statistically significant difference over time for both groups (words: p < 0.002; confidence interval (CI) = 9.38–16.27; d = 0.57; sentences: p < 0.002; CI = 16.04–25.97; d = 0.23). Effect sizes were medium for words and small for sentences. For intelligibility and participation, there was no evidence of an interaction between Time and Group or an overall statistical difference between groups. A statistically significant difference over time was found for intelligibility (F = 29.97, d.f. = 1, 42, p < 0.001, 95 % CI = 1.45–3.15 d = 0.46) and for participation (F = 14.19, d.f. = 1, 41, p < 0.001 95% CI = 7.63–25.03; d = 0.36) with FOCUS results indicating clinically meaningful (parent‐led group) and significant (control group) change in participation. Conclusions & Implications: PLAT can be as effective as routine care in changing speech, activity and participation outcomes for children with cleft palate, when supported by a specialist cleft SLT using connected health. What this paper addsWhat is already known on this subjectOver 50% of children with cleft palate require speech therapy. However, there is a lack of timely, accessible speech therapy services in the UK and Ireland. Previous studies have shown that parents can deliver therapy effectively, and that connected health can support the delivery of speech therapy. This study aims to provide evidence that parent‐led therapy with the supervision of a specialist cleft therapist using FaceTime is effective.What this paper adds to existing knowledgeThis randomized controlled trial indicates that parents can be trained to deliver therapy for children with cleft palate speech disorders, under the supervision of an SLT. This approach results in improved speech, activity and participation outcomes similar to routine care.What are the potential or actual clinical implications of this work?This study indicates that both parent‐led articulation therapy and routine care showed meaningful gains in speech, activity and participation, and that parent‐led articulation therapy when supported by a cleft SLT using connected health could be an additional service delivery model for children with cleft palate speech disorders. [ABSTRACT FROM AUTHOR]

The article discusses the use of statistical analysis in research papers and highlights the potential for incorrect interpretations and misleading statements, particularly in studies with small sample sizes. The article emphasizes that p-values, which are often used to determine statistical significance, are not as reliable or objective as commonly believed. The importance of considering sample size and accurately interpreting p-values is illustrated through an example. The article concludes by suggesting that using larger studies, acknowledging bias, and calculating pre-study probabilities can improve the statistics within current literature. It also encourages the use of multiple statistical elements and reflection on desired significance levels and power. [Extracted from the article]

Having grandchildren is known to reduce individuals' labour supply. However, it is unclear whether there is a negative association between grandchild care provision and employment among grandparents. Moreover, we do not know how the magnitude of any association between the two activities may vary across countries characterised by different child-care policy regimes. Using data from the Survey of Health, Ageing and Retirement in Europe, this paper investigates the association between daily grandchild care provision and two employment outcomes for grandmothers and grandfathers aged 50–69: the probability of being employed and the average weekly working hours. Recursive bivariate models are used to account for the potential selection of grandparents with different unobserved traits into work and family care. Estimates are compared across four country groups characterised by different child-care policy orientations: optional de-familisation, service de-familisation, supported familism and familism by default. On average, across 20 European countries, grandparents looking after grandchildren daily are no less likely to work than grandparents who do not; however, employed grandfathers work eight hours less per week if providing daily child care. Evidence of a negative association between daily grandchild care and employment is strongest in countries with familistic approaches to child care, with no association in countries characterised by optional de-familisation. This suggests that public support to child care may help retain grandparents in the labour force. [ABSTRACT FROM AUTHOR]

Multiple epistemological programs make use of intuitive judgments pertaining to an individual's ability to gain knowledge from exclusively probabilistic/statistical information. This paper argues that these judgments likely form without deference to such information, instead being a function of the degree to which having knowledge is representative of an agent. Thus, these judgments fit the pattern of formation via a representativeness heuristic, like that famously described by Kahneman and Tversky to explain similar probabilistic judgments. Given this broad insensitivity to probabilistic/statistical information, it directly follows that these epistemic judgments are insensitive to a given agent's epistemic status. From this, the paper concludes that, breaking with common epistemological practice, we cannot assume that such judgments are reliable. [ABSTRACT FROM AUTHOR]

Two-tailed significance testing for 2 × 2 contingency tables has remained controversial. Within the medical literature, different tests are used in different papers and that choice may decide whether findings are adjudged to be significant or nonsignificant; a state of affairs that is clearly undesirable. In this paper, it is argued that a part of the controversy is due to a failure to recognise that there are two possible alternative hypotheses to the Null. It is further argued that, while one alternative hypothesis can lead to tests with greater power, the other choice is more applicable in medical research. That leads to the recommendation that, within medical research, 2 × 2 tables should be tested using double the one-tailed exact probability from Fisher's exact test or, as an approximation, the chi-squared test with Yates' correction for continuity. [ABSTRACT FROM AUTHOR]

Several lifetime distributions have played an important role to fit survival data. However, for some of these models, the computation of maximum likelihood estimators is quite difficult due to presence of flat regions in the search space, among other factors. Several well-known derivative-based optimization tools are unsuitable for obtaining such estimates. To circumvent this problem, we introduce the AdequacyModel computational library version 2.0.0 for the R statistical environment with two major contributions: a general optimization technique based on the Particle Swarm Optimization (PSO) method (with a minor modification of the original algorithm) and a set of statistical measures for assessment of the adequacy of the fitted model. This library is very useful for researchers in probability and statistics and has been cited in various papers in these areas. It serves as the basis for the Newdistns library (version 2.1) published in an impact journal in the area of computational statistics, see . It is also the basis of the Wrapped library (version 2.0), see . A third package making use of the AdequacyModel library can be found in . In addition, the proposed library has proved to be very useful for maximizing log-likelihood functions with complex search regions. The library provides a greater control of the optimization process by introducing a stop criterion based on a minimum number of iterations and the variance of a given proportion of optimal values. We emphasize that the new library can be used not only in statistics but in physics and mathematics as proved in several examples throughout the paper. [ABSTRACT FROM AUTHOR]

This paper presents two data-driven virtual sensors to estimate the time-series of the probability of high-concentration occurrence of naturally occurring radioactive materials (NORMs; 238U and 222Rn) in groundwater based on the in-situ groundwater quality monitoring data and geological information. The random forest was applied to estimate the NORM concentration based on the actual in-situ groundwater quality data, rock type, and the aquifer depth. Additionally, this study proposes three data sampling techniques (i.e., under-sampling, synthetic minority over-sampling, and a complex sampling) to improve the model applicability and accuracy. The developed models were validated using the actual data acquired from 201 locations in South Korea. The models for 238U and 222Rn showed estimation accuracies of 85% and 80%, respectively; the models with over-sampling showed better performance. All the results verified the usefulness of the developed models as virtual sensors for providing immediate information on the in-situ presence of NORMs in groundwater. [Display omitted] • Naturally occurring radioactive material (NORMs) in groundwater can be problematic. • Virtual sensor for in-situ estimating the probability of high-concentration of NORMs. • Major geochemical factors for NORMs were evaluated using statistical analyses. • Methods for solving data imbalance problem in training virtual sensor were proposed. • Major factors for the estimation were consistent with the statistical analysis result. [ABSTRACT FROM AUTHOR]

In Siotani and Fujikoshi (1984), a precise local limit theorem for the multinomial distribution is derived by inverting the Fourier transform, where the error terms are explicit up to order N − 1. In this paper, we give an alternative (conceptually simpler) proof based on Stirling's formula and a careful handling of Taylor expansions, and we show how the result can be used to approximate multinomial probabilities on most subsets of R d. Furthermore, we discuss a recent application of the result to obtain asymptotic properties of Bernstein estimators on the simplex, we improve the main result in Carter (2002) on the Le Cam distance bound between multinomial and multivariate normal experiments while simultaneously simplifying the proof, and we mention another potential application related to finely tuned continuity corrections. • Local limit theorem for the multinomial distribution up to an N − 3 ∕ 2 error. • Simple proof using Stirling's formula and Taylor expansions. • Application to obtain asymptotic properties of Bernstein estimators. • Improves Le Cam distance between multinomial and multivariate normal experiments. • Potential applications to continuity corrections and power divergence statistics. [ABSTRACT FROM AUTHOR]

The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the 'frequentist' tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p-values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequentist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of probability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribution. These examples show how the principles can be applied, in order to develop better scientific practice. "La théorie des probabilités n'est que le bon sens reduit au calcul." (Pierre-Simon Laplace, Essai Philosophiques sur les Probabilités, 1819). "It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude." (Harold Jeffreys, Theory of Probability, 1st edition, 1939). [ABSTRACT FROM AUTHOR]

The paper is concerned with the problem of construction of characterizing statistics for some types of distributions within certain classes. A characterizing statistic for multivariate normal distribution is constructed with the use of the theory of random matrices. Properties and characteristics of spherically symmetric distributions are studied. Examples of statistics that can be used for characterization of symmetric distributions are given. Applications of characterizing statistics to the theory of hypothesis testing are given. The problem of discrimination between the uniform and normal types of multivariate distributions within the group of spherically symmetric distributions from small samples is studied on the basis of the results obtained. A lower estimate is obtained for the number of observations that can serve as a basis for discrimination between these distributions. [ABSTRACT FROM AUTHOR]

In view of the fact that uncertain data stream had the characteristics of non-convex distribution and contained a lot of noise, this paper proposed an algorithm Clu_Ustream for clustering uncertain data stream which solved the problem of real time and efficient clustering evolution for recent data. Firstly, in the online part, Clu_Ustream used the sub window sampling mechanism to collect the uncertain stream data in the sliding window. Moreover, it used a double-layer summary statistical structure linked list to store the statistical information of the probability density grids to improve the processing efficiency. Secondly, in the off-line part, it used the damped window mechanism to weaken the influence of old data and deleted regularly the expired sub windows to ensure the effectiveness of clustering. In addition, it developed a dynamic abnormal grids deletion mechanism to filter most of outliers in order to dramatically improve the space and time efficiency. The experimental results on the synthetic and real datasets show that Clu_Ustream has superior clustering quality and efficiency than other similar algorithms. [ABSTRACT FROM AUTHOR]

Statistical power analysis involves designing and interpreting research with attention to the statistical power (probability) of the study to detect an effect of a specific size. Statistical power analysis. which is based on the interdependence of sample size, alpha, effect size, and power, is acknowledged by scholars of various disciplines as an indispensable component of high quality research. This paper reviews basic principles associated with power analysis and demonstrates its importance by comparing the meaningfulness of significant findings in two studies of job satisfaction. The perspective advanced in this paper is that the use of statistical power analysis will strengthen sport management research and will enable researchers to expand the body of knowledge in a systematic, coherent fashion. [ABSTRACT FROM AUTHOR]

In this paper we exemplify the types of Plithogenic Probability and respectively Plithogenic Statistics. Several applications are given. The Plithogenic Probability of an event to occur is composed from the chances that the event occurs with respect to all random variables (parameters) that determine it. Each such a variable is described by a Probability Distribution (Density) Function, which may be a classical, (T,I,F)-neutrosophic, I-neutrosophic, (T,F)-intuitionistic fuzzy, (T,N,F)-picture fuzzy, (T,N,F)-spherical fuzzy, or (other fuzzy extension) distribution function. The Plithogenic Probability is a generalization of the classical MultiVariate Probability. The analysis of the events described by the plithogenic probability is the Plithogenic Statistics. [ABSTRACT FROM AUTHOR]

Clinical studies on periodontal disease (PD) often lead to data collected which are clustered in nature (viz. clinical attachment level, or CAL, measured at tooth-sites and clustered within subjects) that are routinely analyzed under a linear mixed model framework, with underlying normality assumptions of the random effects and random errors. However, a careful look reveals that these data might exhibit skewness and tail behavior, and hence the usual normality assumptions might be questionable. Besides, PD progression is often hypothesized to be spatially associated, that is, a diseased tooth-site may influence the disease status of a set of neighboring sites. Also, the presence/absence of a tooth is informative, as the number and location of missing teeth informs about the periodontal health in that region. In this paper, we develop a (shared) random effects model for site-level CAL and binary presence/absence status of a tooth under a Bayesian paradigm. The random effects are modeled using a spatial skew-normal/independent (S-SNI) distribution, whose dependence structure is conditionally autoregressive (CAR). Our S-SNI density presents an attractive parametric tool to model spatially referenced asymmetric thick-tailed structures. Both simulation studies and application to a clinical dataset recording PD status reveal the advantages of our proposition in providing a significantly improved fit, over models that do not consider these features in a unified way. [ABSTRACT FROM AUTHOR]

We provide an integrated capture–recapture–recovery framework for partially monitored populations. In these studies, live resightings are only observable at a set of monitored locations, so that if an individual leaves these specific locations, they become unavailable for capture. Additional ring-recovery data reduce the corresponding bias obtained in the survival probability estimates from capture–recapture data due to the confounding with colony dispersal. We derive an explicit efficient likelihood expression for the integrated capture–recapture–recovery data, and state the associated sufficient statistics. We demonstrate the significant improvements in the estimation of the survival probabilities using the integrated approach for a colony of guillemots (Uria aalge), where we additionally specify a hierarchical approach to deal with low sample size over the early period of the study. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study. [ABSTRACT FROM AUTHOR]

Statistical paradigms limit the perspective and tools social work researchers use to study the world and answer questions impacting people and policy. Currently, quantitative social work researchers overwhelmingly rely on the frequentist paradigm of statistics. This paper discusses foundational differences between the frequentist and Bayesian statistical paradigms, describes basic concepts of Bayesian analysis, compares Bayesian and frequentist statistical analysis for a sample social work problem, and introduces two types of causal analyses built on Bayesian statistical thinking: counterfactual causality, and causality based on work by computer scientist Judea Pearl. Implications for social work research are discussed. [ABSTRACT FROM AUTHOR]

Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models to test fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches (Bayesian imputation-based association mapping, Bayesian sparse linear mixed model, and the J-test), assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals (n = 1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase 3 (1000G) reference panel (n = 2504 from 26 populations) giving a mean nonreference discordance rate between imputed and sequenced genotypes of 3.2% compared to 1.6% when using the Haplotype Reference Consortium (HRC) reference panel (n = 32,470 Europeans). These imputation errors had an impact on whether the CpG-SNP was included in the 95% credible set, with a difference of ~23% and ~7% between the WGS and the 1000G and HRC imputed datasets, respectively. All of the fine-mapping methods failed to reach the expected 95% coverage of the CpG-SNP. This is attributed to secondary cis genetic effects that are unable to be statistically separated from the CpG-SNP, and through a masking mechanism where the effect of the methylation disrupting allele at the CpG-SNP is hidden by the effect of a nearby SNP that has strong linkage disequilibrium with the CpG-SNP. The reduced accuracy in fine-mapping a known causal variant in a low-level biological trait with imputed genetic data has implications for the study of higher-order complex traits and disease. [ABSTRACT FROM AUTHOR]