SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2019-06-01 Epub Date: 2019-08-20 DOI:10.32614/rj-2019-038
Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee
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引用次数: 15

Abstract

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

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一个半竞争风险:一个独立的和聚类相关的半竞争风险数据分析的R包。
半竞争风险是指主要的科学兴趣在于对非终结事件的估计和推断,而非终结事件的发生取决于终结事件。在本文中,我们提出了一个R包semiomprisks,它提供了在疾病-死亡多状态模型下进行独立/聚类半竞争风险数据分析的功能。该软件包允许用户从一系列选项中选择模型组件的规格,为用户提供了很大的灵活性,包括:加速故障时间或比例风险回归模型;基线生存函数的参数或非参数说明;当数据簇相关时,随机效应分布的参数或非参数规范;非终端事件之后的终端事件的马尔可夫或半马尔可夫规范。虽然估计主要是在贝叶斯范式中进行的,但该软件包还为选择的参数模型提供了最大似然估计。该软件包还包括单变量生存分析功能,作为补充分析工具。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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