论文标题
r套件$ \ texttt {ebmstate} $用于疾病进程分析的经验贝叶斯cox模型
The R package $\texttt{ebmstate}$ for disease progression analysis under empirical Bayes Cox models
论文作者
论文摘要
The software package $\texttt{mstate}$, in articulation with the package $\texttt{survival}$, provides not only a well-established multi-state survival analysis framework in R, but also one of the most complete, as it includes point and interval estimation of relative transition hazards, cumulative transition hazards and state occupation probabilities, both under clock-forward and clock-reset models;个性化的估计值，即具有特定协变量测量值的个体的估计，也可以通过拟合COX回归模型来获得$ \ texttt {mstate} $的估计。我们在当前论文中呈现的新的R软件包$ \ texttt {ebmstate} $是$ \ texttt {mstate} $的扩展，据我们所知，这是第一个用于多状态模型估计的R软件包，适用于高维数据，并在恰好提到的含义上完成。它的$ \ texttt {mstate} $的扩展是三倍：它将Cox模型转换为正规化的经验贝叶斯模型，该模型在高维数据中的性能明显更好；它取代了通过非参数自举置信区间用于低维设置的渐近置信区间。它引入了一个基于分析性的，基于傅立叶变换的状态职业概率的估计值，该概率的速度比相应的，基于仿真的估计器的速度要快得多。本文包括有关如何使用我们的软件包来估计过渡危害和州职业概率的详细教程，以及一项模拟研究，以提高$ \ texttt {mstate} $的性能。
The software package $\texttt{mstate}$, in articulation with the package $\texttt{survival}$, provides not only a well-established multi-state survival analysis framework in R, but also one of the most complete, as it includes point and interval estimation of relative transition hazards, cumulative transition hazards and state occupation probabilities, both under clock-forward and clock-reset models; personalised estimates, i.e. estimates for an individual with specific covariate measurements, can also be obtained with $\texttt{mstate}$ by fitting a Cox regression model. The new R package $\texttt{ebmstate}$, which we present in the current paper, is an extension of $\texttt{mstate}$ and, to our knowledge, the first R package for multi-state model estimation that is suitable for higher-dimensional data and complete in the sense just mentioned. Its extension of $\texttt{mstate}$ is threefold: it transforms the Cox model into a regularised, empirical Bayes model that performs significantly better with higher-dimensional data; it replaces asymptotic confidence intervals meant for the low-dimensional setting by non-parametric bootstrap confidence intervals; and it introduces an analytical, Fourier transform-based estimator of state occupation probabilities for clock-reset models that is substantially faster than the corresponding, simulation-based estimator in $\texttt{mstate}$. The present paper includes a detailed tutorial on how to use our package to estimate transition hazards and state occupation probabilities, as well as a simulation study showing how it improves the performance of $\texttt{mstate}$.
