Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response‐190 is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non‐negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression parameter is estimated through a procedure that does not require estimating the baseline probability. Unlike the majority of process regression methods, the proposed methods accommodate multiple sources of censoring. In particular, we derive a computationally convenient variant of inverse probability of censoring weighting based on the additive hazards model. We show that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulations demonstrate that our estimators have good finite sample performance. We apply our method to national end‐stage liver disease data. The Canadian Journal of Statistics 48: 222–237; 2020 © 2019 Statistical Society of Canada

中文翻译：

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受多个审查来源约束的时间过程的半参数回归方法

相对于相关数据出现的频率，过程回归方法尚不完善。在本文中，response-190是一个二进制指示符过程，表示活着并保持在特定状态下的联合事件。通过时间（例如，自诊断以来的时间）对过程进行索引并连续观察。在慢性疾病的研究中经常出现这种数据。一般的应用领域包括持续时间不可忽略的复发事件（例如，住院和相关的住院时间），并有终止事件（例如，死亡）。我们为过程版本的存在概率和处于（瞬态）感兴趣状态的过程版本提出了半参数乘法模型。根据建议的方法，回归参数是通过不需要估计基线概率的过程来估计的。与大多数过程回归方法不同，建议的方法可容纳多种检查来源。特别是，我们基于累加危害模型得出了一种计算方便的加权加权逆概率的变体。我们表明回归参数估计量是渐近正态的，并且基线概率函数估计量收敛到高斯过程。仿真表明，我们的估计量具有良好的有限样本性能。我们将我们的方法应用于全国晚期肝病数据。我们根据累加危害模型得出了一种计算方便的加权加权逆概率的变体。我们表明回归参数估计量是渐近正态的，并且基线概率函数估计量收敛到高斯过程。仿真表明，我们的估计器具有良好的有限样本性能。我们将我们的方法应用于全国晚期肝病数据。我们根据累加危害模型得出了一种计算方便的加权加权逆概率的变体。我们表明回归参数估计量是渐近正态的，并且基线概率函数估计量收敛到高斯过程。仿真表明，我们的估计量具有良好的有限样本性能。我们将我们的方法应用于全国晚期肝病数据。《加拿大统计杂志》 48：222–237；2020©2019加拿大统计学会