Biostatistics Core

摘要：生物统计学核心 生物统计核心(BC)将支持个人的研究设计、数据处理和分析需求 以及加强P20中所有项目之间的互动。经验表明， 概念阶段的生物统计学家和数据科学家的参与可以产生更好的研究 设计，更有可能回答感兴趣的科学问题，最终在他们的 结论。生物统计核心的目标是支持研究人员完成其研究的所有阶段 通过以下具体目标从设计到出版。 具体目标1：研究设计 BC将采用5阶段早期检测生物标记物开发指南以及探针研究 设计标准，以帮助P20团队建立路线图和研究策略，以改进 AI/AN患者的肝癌早期发现。5阶段指导方针的使用将使P20调查员能够 制定战略并制定长期计划，帮助将生物标志物从发现阶段1-2转移到验证阶段 3-5，满足翻译连续体。坚持探测标准将指导由以下驱动的研究设计 预期的临床应用，减少系统偏差，并确保高可能性的可重复性和 具有临床相关性的研究结论。 具体目标2：分析和解释 委员会将在拟议的LI-CADP20项目的统计分析和解释方面发挥领导作用。 BC将确定和实施量化方法，以解决感兴趣的科学问题和 就各种研究假设的证据提供有效的统计推断。例如, 将使用参数经验贝叶斯(PEG)和多元完全贝叶斯(MFB)方法来建模 纵向生物标志物，将用于构建AI/AN中肝细胞癌早期检测的风险预测模型 人口。此外，还将进行数据可视化，以调查乙肝病毒突变和最先进的 将利用机器学习方法来开发和验证AI/AN中的肝细胞癌风险预测模型 人。BC还将与研究调查人员合作，明确传达研究中的方法和结果 并确保所报道的结论是合理的。 具体目标3：促进与刚果民主共和国和其他核心国家的互动 BC将与DRP以及生物质学和病理学核心进行互动，以确保 LI-CAD P20已步入正轨，包括所有拟议的P20项目以及DRP项目。公元前首领将 直接与P20执行委员会沟通，以确保任何具有统计意义的决定 有来自核心的适当和及时的投入。

ABSTRACT: BIOSTATISTICS CORE The Biostatistics Core (BC) will support the study design, data processing, and analysis needs for individual projects as well as enhance interactions among all the projects in the P20. Experience has shown that involvement of biostatisticians and data scientists from the concept phase yields studies that are better designed, more likely to answer the scientific questions of interest, and, ultimately, more compelling in their conclusions. The goal of the Biostatistics Core is to support the investigators through all phases of their studies from design to publication via the following Specific Aims. Specific Aim 1: Study Design The BC will employ the 5-phase early detection biomarker development guideline as well as the PRoBE study design standards in order to help the P20 team establish a road map and research strategies for improving liver cancer early detection in AI/AN patients. The use of the 5-phase guideline will allow P20 investigators to strategize and make long-term plans, helping move biomarkers from discovery phases 1-2 to validation phases 3-5, fulfilling the translational continuum. Adhering to PRoBE standards will guide the study design driven by intended clinical application, reducing systematic bias, and ensuring a high likelihood of reproducible and clinically relevant study conclusions. Specific Aim 2: Analysis and Interpretation The BC will play a leadership role in statistical analysis and interpretation for the proposed Li-CAD P20 projects. The BC will identify and implement quantitative methods to address the scientific questions of interest and provide valid statistical inferences about the evidence addressing the various study hypotheses. For example, Parametric Empirical Bayesian (PEG) and Multivariate Fully Bayesian (mFB) approaches will be used to model longitudinal biomarkers, which will be used to build risk prediction models for the early detection of HCC in AI/AN population. Additionally, data visualization will be conducted to investigate HBV mutations, and state-of-the-art machine learning approaches will be utilized to develop and validate HCC risk prediction models in AI/AN persons. The BC will also work with study investigators to clearly communicate methods and results in study publications and insure that reported conclusions are justified. Specific Aim 3: Promote Interactions with DRP and Other Cores The BC will interact with the DRP as well as the Biospecimen and Pathology Core to ensure efforts within the Li-CAD P20 are on track, including all proposed P20 projects as well as DRP projects. The BC leader will communicate directly with the P20 Executive Committee to ensure any decision that has statistical implications has appropriate and timely inputs from the Core.