FamEvent:用于在家庭设计中生成时间到事件数据并对其进行建模的 R 软件包。

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Statistical Software Pub Date : 2021-03-01 Epub Date: 2021-03-19 DOI:10.18637/jss.v097.i07
Yun-Hee Choi, Laurent Briollais, Wenqing He, Karen Kopciuk
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引用次数: 0

摘要

FamEvent 是一个综合性 R 软件包,用于模拟和建模携带罕见基因突变的家族的发病年龄。该软件包可以模拟复杂的家族数据,在三种常见的家族研究设计(人群、高风险诊所和多阶段)下,根据不同程度的家族成员遗传信息缺失情况,计算不同的时间到事件结果。可通过加入虚弱项或第二个基因来诱导残余家族相关性。疾病基因携带者概率是根据孟德尔传播假设或数据经验进行评估的。如果疾病基因的遗传信息缺失,则采用期望最大化算法计算携带者概率。根据抽样设计进行确定性校正的穿透性模型函数,为模拟数据和真实数据分析提供了按性别、突变状态和其他协变量划分的特定年龄累积疾病风险。这些估计值有稳健的标准误差和 95% 的置信区间。根据拟合模型绘制的系谱图和渗透函数图提供了评估和总结模型的图形显示。
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FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs.

FamEvent is a comprehensive R package for simulating and modelling age-at-disease onset in families carrying a rare gene mutation. The package can simulate complex family data for variable time-to-event outcomes under three common family study designs (population, high-risk clinic and multi-stage) with various levels of missing genetic information among family members. Residual familial correlation can be induced through the inclusion of a frailty term or a second gene. Disease-gene carrier probabilities are evaluated assuming Mendelian transmission or empirically from the data. When genetic information on the disease gene is missing, an Expectation-Maximization algorithm is employed to calculate the carrier probabilities. Penetrance model functions with ascertainment correction adapted to the sampling design provide age-specific cumulative disease risks by sex, mutation status, and other covariates for simulated data as well as real data analysis. Robust standard errors and 95% confidence intervals are available for these estimates. Plots of pedigrees and penetrance functions based on the fitted model provide graphical displays to evaluate and summarize the models.

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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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