زیرشاخه پژوهشیآمار زیستی
مقالهها، منابع و پژوهشهای تازه حوزه آمار زیستی
جستوجوی چندمنبعی
PubMedدسترسی آزاد2026
Frontiers in public healthVictor Luckyboy Dzramado, William Wilberforce Amoah, Samuel Antwi, Joana Edem Koto, Doris Hagan
BACKGROUND: Infectious disease outbreaks pose significant threats to global health security, with resource-limited settings in West Africa bearing a disproportionate burden. Despite sustained investments in field epidemiology training and surveillance system strengthening, no comprehensive systematic synthesis exists of Ghana Health Service preparedness for field epidemiology and applied biostatistics. This protocol addresses the primary research question: What is the current level of preparedness of the Ghana Health Service for field epidemiology and applied biostatistics, as assessed across World Health Organization International Health Regulations core capacity domains? Preparedness is operationally defined as the measurable capacity of the Ghana Health Service to detect, investigate, confirm, and respond to infectious disease events across the eight WHO IHR core capacity domains, including surveillance, human resources, laboratory systems, and response mechanisms. METHODS AND ANALYSIS: This systematic review follows PRISMA 2020 guidelines and is registered with PROSPERO (CRD420261299788). Searches will be conducted in PubMed/MEDLINE, African Index Medicus, African Journals Online, and grey literature sources for studies published from January 2010 to present. Eligible studies include those describing field epidemiology capacity, surveillance system performance, outbreak investigation preparedness, biostatistical capacity, training program outcomes, infrastructure, or health workforce within the Ghana Health Service. Two independent reviewers will screen citations, extract data, and assess quality using study-design-appropriate tools including the JBI Critical Appraisal Checklist, CASP Qualitative Checklist, and Mixed Methods Appraisal Tool. Primary outcomes are overall field epidemiology preparedness level measured using Joint External Evaluation and State Party Annual Reporting scores, and surveillance system performance with outbreak response capacity. Secondary outcomes include field epidemiology workforce capacity, statistical modeling and biostatistical capacity, and infrastructure and governance systems. Narrative synthesis is the primary analytic approach. Meta-analysis will be conducted where sufficient comparable studies with acceptable methodological homogeneity are identified. DISCUSSION: This review will provide the first comprehensive assessment of Ghana Health Service field epidemiology preparedness mapped against WHO IHR core capacities, generating actionable evidence-based recommendations applicable to similar resource-limited settings across Africa. ETHICS AND DISSEMINATION: No ethical approval is required. Results will be disseminated through peer-reviewed publication, conference presentations, and policy briefs for the Ghana Health Service, Ministry of Health, and international stakeholders. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261299788, identifier CRD420261299788.
PubMedدسترسی آزاد2026
Biostatistics (Oxford, England)Jincheol Park, Meng Wang, Emerson Webb, Victor Jin, Shili Lin
The three dimensional (3D) spatial organization of the genome is closely linked to biological functions and can be captured by Hi-C assays through interrogating genome-wide chromatin interactions. Methodologies for inferring 3D structures from Hi-C data summarized as a two-dimensional (2D) contact matrix can be broadly placed within the paradigms of optimization-based and sampling-based. Many optimization-based methods are capable of constructing whole genome 3D structures but do not account for spatial dependency in the 2D data matrix nor cell heterogeneity in bulk Hi-C data, which provide an average over millions of cells. Sampling-based methods, on the other hand, are probabilistic model-based and can account for not only dependency, heterogeneity, but also other features inherent in Hi-C data, such as over-dispersion and sparsity. However, whole-genome 3D structure recapitulation is too computationally expensive for sampling-based methods, while chromosome-by-chromosome strategies for sampling-based methods ignore important information on inter-chromosomal contacts. To address these issues, we propose the truncated Random effect EXpression-cut and paste (tREX-cap) method, which applies the tREX model within a divide and conquer strategy. The resulting method inherits the good data-feature-cognizant properties of tREX and, in the meantime, can efficiently infer the whole genome 3D structure. We demonstrate the performance of tREX-cap through an extensive simulation study and analyses of a Hi-C lymphoblastoid dataset and a Hi-C IMR90 dataset.
PubMedدسترسی آزاد2026
Biostatistics (Oxford, England)David McCoy, Wenxin Zhang, Alan Hubbard, Mark van der Laan, Alejandro Schuler
In epidemiology, identifying subpopulations that are particularly vulnerable to exposures and those who may benefit differently from exposure-reducing interventions is essential. Factors such as age, gender-specific vulnerabilities, and physiological states such as pregnancy are critical for policymakers when setting regulatory guidelines. However, current semiparametric methods for estimating heterogeneous treatment effects are often limited to binary exposures and can function as black boxes, lacking clear, interpretable rules for subpopulation-specific policy interventions. This study introduces a novel method that uses cross-validated targeted minimum loss-based estimation (TMLE) paired with a data-adaptive target parameter strategy to identify subpopulations with the most significant differential impact of simulated policy interventions that reduce exposure. Our approach is assumption-lean, allowing for the integration of machine learning while still yielding valid confidence intervals. We demonstrate the robustness of our methodology through simulations and an application to data from the National Health and Nutrition Examination Survey. Our analysis of NHANES data on persistent organic pollutants (POPs) and leukocyte telomere length (LTL) identified age as a significant effect modifier. Specifically, we found that exposure to 3,3',4,4',5-pentachlorobiphenyl (PCB; NHANES analyte LBXPCBLA) consistently had a differential impact on LTL, with a 1-SD reduction in exposure leading to a more pronounced increase in LTL among younger populations than in older ones. We offer our method as an open-source software package, EffectXshift, enabling researchers to investigate the effect modification of continuous exposures. The EffectXshift package provides clear and interpretable results, informing targeted public health interventions and policydecisions.
PubMedدسترسی آزاد2026
Biostatistics (Oxford, England)Lu You, Falastin Salami, Carina Törn, Åke Lernmark, Kendra Vehik, Xiang Liu, Peihua Qiu, Roy Tamura
This paper presents a joint model of multivariate longitudinal data and multistate data with application to modeling and predicting autoantibody development in The Environmental Determinants of Diabetes in the Young (TEDDY) study. The model quantifies the risks of state transitions based on observed time-varying and non-time-varying risk factors. Based on the estimated model, a dynamic prediction approach is suggested to predict future state occupation probabilities using historical data. The proposed method can handle uncertainties in the observed data, due to measurement errors in the observed longitudinal data and interval censoring or missing information in the observed multistate data. For evaluating the predictions by the proposed approach, some performance metrics and their estimation are discussed. The proposed method is evaluated by some simulation studies. It is discussed in detail how this method can be used in analyzing the TEDDY data by properly handling the missing information and predicting future disease status using the proposed dynamic prediction algorithm.