Inference Methods Research Articles

Leveraging probability and statistical algorithms for enhanced financial risk management

This paper explores the foundations and applications of quantitative analysis in financial risk management. It examines the pivotal role of probability theory, statistical inference, and advanced algorithms in identifying, quantifying, and mitigating financial risks. Key concepts such as the Normal, Poisson, and Binomial distributions are discussed in the context of risk analysis, alongside statistical inference methods like hypothesis testing and confidence intervals. Furthermore, the paper investigates the application of portfolio optimization models, credit risk evaluation techniques, and market risk assessment methodologies in practical risk management scenarios. Additionally, it addresses the challenges posed by model risk, data quality, and regulatory compliance, emphasizing the need for rigorous validation, robust data governance, and ethical considerations in risk management practices. By integrating sophisticated quantitative techniques with real-world applications, financial institutions can enhance their ability to navigate the complexities of modern financial markets and achieve more effective risk management strategies.

Exercise may delay cognitive decline in Chinese older adults: a causal inference for ordered multi-categorical exposures with a Mendelian randomization approach

The cognitive problems are prominent in the context of global aging, and the traditional Mendelian randomization method is not applicable to ordered multi-categorical exposures. Therefore, we aimed to address this issue through the development of a method and to investigate the causal inference of cognitive-related lifestyle factors. The study sample was derived from the Chinese Longitudinal Healthy Longevity Survey, which included 897 older adults aged 65 + . This study used genome-wide association analysis to screen genetic loci as instrumental variables and innovatively combined maximum likelihood estimation to infer causal associations between ordered multi-categorical exposures (diet, exercise, etc.) and continuous outcomes (cognitive level). The causal inference method for ordered multi-categorical exposures developed in this study was simple, easy to implement, and able to effectively and reliably discover the potential causal associations between variables. Through this method, we found a potential positive causal association between exercise status and cognitive level in Chinese older adults (β^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widehat{\\beta }$$\\end{document} = 1.883, 95%CI 0.182–3.512), in which there was no horizontal pleiotropy (p = 0.370). The study provided a causal inference method applicable to ordered multi-categorical exposures, that addressed the limitations of the traditional Mendelian randomization method.

Recent advances in exploring transcriptional regulatory landscape of crops.

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Safety and effectiveness of SGLT2 inhibitors in a UK population with type 2 diabetes and aged over 70 years: an instrumental variable approach.

Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013-2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (-0.3 mmol/mol [-1.6, 1.1], -0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (-4 mmol/mol [4.8, -3.1], -0.4% [-0.4, -0.3]). Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes.

The Role of Risk Factors in System Performance: A Comprehensive Study with Adaptive Progressive Type-II Censoring

The quality performance of many vital systems depends on how long the units are performing; hence, research works started focusing on increasing the reliability of systems while taking into consideration that many factors may cause the failures of operating systems. In this study, the combination of a parametric generalized linear failure rate distribution model and an adaptive progressive Type-II censoring scheme for practical purposes is explored. A comprehensive investigation is performed on the risk factors that cause failure and determines which of the factors has a more harmful effect on the units. A lifetime experiment is performed under the condition of an adaptive progressive Type-II censoring scheme to obtain observations as a result of the competing factors of failures. The obtained observations are assumed to follow a three-parameter generalized linear failure rate distribution and are assumed to be competing to cause failure. Two statistical inference methods are employed for estimating this model’s parameters: the frequentist maximum likelihood method and the Bayesian approach. Our model’s validity is demonstrated through extensive simulations and real data applications in the medical and electrical engineering fields.

Circulating interleukin-27 is associated with the risk of chronic periodontitis and allergic rhinitis: A Mendelian randomization analysis

Background Chronic periodontitis (CP) and allergic rhinitis (AR) have attracted wide attention as global public health problems with high incidence. Recent studies have shown that circulating interleukin-27 (IL-27) is associated with the risk of CP and AR. The aim of this study is to analyze the causal effect between them using Mendelian randomization (MR) Methods Bidirectional MR analyses were performed with the use of publicly available genome-wide association study (GWAS) data. Summary data on circulating IL-27, CP, and AR published in genome-wide association studies were collected. Instrumental variables (IV) were extracted using assumptions of correlation, independence and exclusivity as criteria. Inverse variance weighting (IVW) was used as the main method, combined with weighted median method (WM) and MR-Egger and other MR Analysis methods for causal inference of exposure and outcome. Cochran’s Q and MR-Egger intercept were used for sensitivity analysis Results The IVW study showed a causal effect between increased circulating IL-27 levels and increased risk of CP (OR = 1.14, 95%CI = 1.02–1.26, p = .020). Similarly, the increase of circulating IL-27 level had a causal effect on the decreased risk of AR (OR = 0.88, 95%CI = 0.80–0.97, p = .012). In addition, IVW study found that there was a causal between the increased risk of CP and circulating IL-27 level (OR = 1.05, 95%CI = 1.01–1.10, p = .016). However, there was no significant causal relationship between the risk of AR and circulating IL-27 levels (OR = 0.97, 95%CI = 0.91–1.02, p = .209). no significant heterogeneity or horizontal pleiotropy was found in sensitivity analysis. Conclusions There is a causal effect between circulating IL-27 level and CP, AR, which will help to find new ideas and methods for the diagnosis and treatment of CP and AR.

Control of quadrotor robot via optimized nonlinear type-2 fuzzy fractional PID with fractional filter: Theory and experiment

This paper presents a novel approach for controlling quadrotor robots. It introduces an optimized nonlinear fractional order type-2 fuzzy system integrated with a Fractional Order Proportional-Integral-Derivative controller, supplemented by a fractional filter in the derivative section. The input scaling factors, which represent the scaling coefficients associated with the input variables of the fuzzy system, are adjusted using nonlinear gains. The proposed controller combines type-2 fuzzy logic, fractional calculus, and nonlinear gains to enhance the control performance of the system. Its purpose is to provide superior control accuracy, robustness, and disturbance rejection capabilities. In order to enhance the performance of the quadrotor, the Bat Optimization Algorithm is utilized to finely adjust the controller parameters and membership function parameters of the fuzzy system. The fuzzy system incorporates Gaussian membership functions and applies the Mamdani min-max method for inference, utilizing the centroid method for defuzzification. The performance of the controller is evaluated in two scenarios: tracking aerial maneuvers in the presence of wind disturbance, and achieving angular stabilization by tracking a pulse function in the Hardware-In-The-Loop real-time test. The findings demonstrate significant improvements compared to traditional PID controllers. These improvements surpass 34 % without a filter and 53 % with a filter, in all directions and scenarios.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China

BackgroundEvidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. AimsWe aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. MethodsWe used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. ResultsWith an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025–1.041), 1.032 (95% CI: 1.027–1.038), and 1.020 (95% CI: 1.016–1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009–1.035) and 1.053 (1.038–1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031–1.053), 1.041 (1.032–1.050), and 1.0473 (1.025–1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. ConclusionThis study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

OneSC: A computational platform for recapitulating cell state transitions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Medication use evaluation of tocilizumab implementation in COVID-19 treatment guidelines: A causal inference approach.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Introduction of new medications to health-system formularies is often not accompanied by assessments of their clinical impact on the local patient population. The growing availability of electronic health record (EHR) data and advancements in pharmacoepidemiology methods offer institutions the opportunity to monitor the medication implementation process and assess clinical effectiveness in the local clinical context. In this study, we applied novel causal inference methods to evaluate the effects of a formulary policy introducing tocilizumab therapy for critically ill patients with coronavirus disease 2019 (COVID-19). We conducted a medication use evaluation utilizing EHR data from patients admitted to a large medical center during the 6 months before and after implementation of a formulary policy endorsing the use of tocilizumab for treatment of COVID-19. The impact of tocilizumab on 28-day all-cause mortality was assessed using a difference-in-differences analysis, with ineligible patients serving as a nonequivalent control group, and a matched analysis guided by a target trial emulation framework. Safety endpoints assessed included the incidence of secondary infections and liver enzyme elevations. Our findings were benchmarked against clinical trials, an observational study, and a meta-analysis. Following guideline modification, tocilizumab was administered to 69% of eligible patients. This implementation was associated with a 3.1% absolute risk reduction in 28-day mortality (odds ratio, 0.86; number needed to treat to prevent one death, 32) attributable to the inclusion of tocilizumab in the guidelines and an additional 8.6% absolute risk reduction (odds ratio, 0.65; number needed to treat to prevent one death, 12) linked to its administration. These findings were consistent with estimates from published literature, although the effect estimates from the difference-in-differences analysis exhibited imprecision. Evaluating formulary management decisions through novel causal inference approaches offers valuable estimates of clinical effectiveness and the potential to optimize the impact of new medications on population outcomes.

Phylogeny and species delimitation of ciliates in the genus Spirostomum (Class, Heterotrichea) using single-cell transcriptomes.

Ciliates are single-celled microbial eukaryotes that diverged from other eukaryotic lineages over a billion years ago. The extensive evolutionary timespan of ciliate has led to enormous genetic and phenotypic changes, contributing significantly to their high level of diversity. Recent analyses based on molecular data have revealed numerous cases of cryptic species complexes in different ciliate lineages, demonstrating the need for a robust approach to delimit species boundaries and elucidate phylogenetic relationships. Heterotrich ciliate species of the genus Spirostomum are abundant in freshwater and brackish environments and are commonly used as biological indicators for assessing water quality. However, some Spirostomum species are difficult to identify due to a lack of distinguishable morphological characteristics, and the existence of cryptic species in this genus remains largely unexplored. Previous phylogenetic studies have focused on only a few loci, namely the ribosomal RNA genes, alpha-tubulin, and mitochondrial CO1. In this study, we obtained single-cell transcriptome of 25 Spirostomum species populations (representing six morphospecies) sampled from South Korea and the USA, and used concatenation- and coalescent-based methods for species tree inference and delimitation. Phylogenomic analysis of 37 Spirostomum populations and 265 protein-coding genes provided a robustious insight into the evolutionary relationships among Spirostomum species and confirmed that species with moniliform and compact macronucleus each form a distinct monophyletic lineage. Furthermore, the multispecies coalescent (MSC) model suggests that there are at least nine cryptic species in the Spirostomum genus, three in S. minus , two in S. ambiguum, S. subtilis , and S. teres each. Overall, our fine sampling of closely related Spirostomum populations and wide scRNA-seq allowed us to demonstrate the hidden crypticity of species within the genus Spirostomum , and to resolve and provide much stronger support than hitherto to the phylogeny of this important ciliate genus.

Complete mitochondrial genome of Devario shanensis (Cypriniformes: Danionidae: Danioninae): genome characterization and phylogenetic consideration

In this study, a comprehensive analysis is presented on the complete mitochondrial genome and phylogenetic relationships of Devario shanensis, an endemic species to the Irrawaddy drainage in southwestern China. The complete mitogenome sequence of D. shanensis was sequenced to be 16,860 bp long and encompassed 13 protein-coding genes, 22 tRNA genes, two rRNA genes and a non-coding control region. The overall AT content (61.1%) was much higher than GC content (38.9%). Phylogenetic analyses employing maximum-likelihood and Bayesian inference methods on the complete mitogenomes, including D. shanensis and 13 other species, unveiled a close genomic relationship between D. shanensis and Devario interruptus. This work will contribute to the genetic resource enrichment and phylogenetic researches on genus Devario.

Method of sequential intention inference for a space target based on meta-fuzzy decision tree

For an operational satellite in orbit, potential risks may arise when an unknown target approaches. Therefore, it is essential for the satellite to infer the intentions of the target in space. To achieve this objective, this paper proposes a method of sequential intention inference based on meta-fuzzy decision tree. The proposed method initially defines three situational functions: flyaround, collision, and observation. These functions help organize the inherent characteristics found in the trace and relative attitude of the target. Subsequently, by combining fuzzy decision tree (FDT), spatio-temporal modeling based on Long Short-Term Memory (LSTM), and task migration using meta-learning techniques, we can realize sequential intention inference for the target while ensuring quick adaptation to new tasks. Additionally, our proposed method takes into account the impact of fuzzy elements within FDT. Through quantum genetic algorithm (QGA) optimization, these fuzzy elements can be fine-tuned to enhance intention inference accuracy.

Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models.

Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to . The directions of associations were less consistent for walkability and share of single residents. Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.

Transcriptional mechanisms of phenotypic plasticity in glioblastoma.

e14027 Background: Primary glioblastoma (GBM) tumor cells exhibit plasticity and can adopt different cellular states that exhibit dynamic transitions between states. This could be one of the mechanisms for treatment escape and leads to poor prognosis in GBM. In this regard, we sought to uncover transcriptional regulatory mechanisms responsible for this phenotypic plasticity in GBM. Methods: Standard of care (SOC) treatment (radiation and temozolomide) modulates transcriptional regulatory networks (TRNs) of the tumor cells and drives treatment resistance. Since TRNs could be key mechanistic drivers for state transitions, integrated single-cell RNA sequencing (scRNAseq) data of primary and relapse patient samples (11) were analyzed with scMINER. Our method uses a systems biology framework to identify cell states (St_1 to St_n) in terms of regulatory networks and their activities. Clustering of co-regulated gene modules (regulons), enabled the identification of transcriptional programs and regulatory mechanisms that specify tumor cell states. Results: Activity patterns of 38 transcriptional programs clustered tumor cells from the patients into 21 states. Preliminarily, we observed common regulatory network cell states (St_1, St_2, St_5, St_6) across patient tumors. Additionally, primary tumor cells of a patient (UW14) transitioned from initial cell state(s) observed across multiple patients (e.g., St_1) to a recurrent state (e.g., St_9) through a trajectory driven by transcription factors (TFs). Herein, scMINER regulatory network inference method also defines master TFs (TFs that regulate the majority of the other TFs in the network) that were used to characterize initial (St_1) or recurrent (St_9) cell states. Moreover, certain transcription factors (22) and their co-expressed target genes (regulons) also exhibit variation in expression across the pseudotime trajectory (from St_1 to St_9) including masterTFs such as SOX9 and MECOM. Conclusions: Therefore, we observed that shared cell states may exist across patients (primary and relapse) although in different proportions. Our analysis suggests a de-differentiation trajectory from neuronal cells to developing GBM stem cells and predicts transcription factors that could be playing an important role therein. Moreover, similar transcriptional regulatory mechanisms may promote transitions between primary and recurrent tumor cell states that exhibit phenotypic plasticity.

Advancing Bayesian Inference: Fitting Type I Half-logistic Nadarajah-Haghighi Distribution and its Comparison with Related Distributions for Censored Survival Data

In recent years, the field of Bayesian modeling of generalized distributions has garnered substantial attention. This surge in interest can be attributed to the remarkable strides made in computational capabilities and the enhanced accessibility of sophisticated software tools. This study aims to apply Bayesian inference methods to the Type I Half-logistic Nadarajah- Haghighi model and compares it with the Exponential model and the Type I Half-logistic Exponential model. These distributions are analyzed and fitted to censored survival data using the probabilistic programming language STAN. To incorporate censored mechanisms within the STAN framework, customized codes are developed. The models are then compared using fully Bayesian model selection methods.. KEYWORDS :Censored data, Nadarajah-Haghighi distribution, Type I half-logistic family, LOOIC, WAIC, STAN.

Genetic diversity and spread dynamics of SARS-CoV-2 variants present in African populations.

The dynamics of coronavirus disease-19 (COVID-19) have been extensively researched in many settings around the world, but little is known about these patterns in Africa. A total of 7540 complete nucleotide genomes from 51 African nations were obtained and analysed using the National Center for Biotechnology Information and Global Initiative on Sharing Influenza Data databases to examine the genetic diversity and spread dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineages circulating in Africa. Using various clade and lineage nomenclature schemes, we examined their diversity and used maximum parsimony inference methods to reconstruct the evolutionary hypotheses about the spread of the virus in Africa. According to this study, only 465 of the 2610 Pango lineages found to have existed in the world circulated in Africa three years after the COVID-19 pandemic, with five different lineages dominating at various points during the outbreak. We identified South Africa, Kenya and Nigeria as key sources of viral transmission among sub-Saharan African nations. These findings provide insights into the viral strains that circulate in Africa and their evolutionary patterns.

Statistical analysis of double stress accelerated life testing under adaptive type-Ⅱ progressive censoring

Aiming at the traditional accelerated life test only considering the single stress and single failure cause, the inference method of double stress accelerated competing risks data is studied under the adaptive type-Ⅱ progressively censored and the life of test units follow Weibull distribution. The maximum likelihood estimations and the asymptotic confidence intervals of unknown parameters are obtained by using the Newton-Raphson iteration and asymptotic likelihood theory. A Gibbs sampling combining with Metropolis-Hasting algorithm method is developed to obtain Bayes estimations, and the Monte Carlo method is employed to construct the HPD credible intervals. The simulated results show that the present method has good statistical inference performance, meanwhile the failure sample size and the expected experimentation time have significant effect on the estimations.

Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges in clarifying the estimand and underlying assumptions. This article proposes expressing causal structures using graphical tools-directed acyclic graphs (DAGs) and single-world intervention graphs (SWIGs)-in the planning phase of a clinical trial. It presents five rules for selecting a sufficient set of adjustment variables on the basis of a diagram representing the clinical trial, along with three case studies of randomized and single-arm trials and a brief tutorial on DAG and SWIG. Through the case studies, DAGs appear effective in clarifying assumptions for identifying causal effects, although SWIGs should complement DAGs due to their limitations in the presence of intercurrent events in oncology research.