A computationally efficient nonparametric sampling (NPS) method of time to event for individual-level models.
A computationally efficient nonparametric sampling (NPS) method of time to event for individual-level models.
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一种计算高效的个体级模型事件时间非参数采样 (NPS) 方法。
DOI：
10.1101/2024.04.05.24305356
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Alarid-Escudero,Fernando
中科院分区：
文献类型：
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作者：
Garibay,David;Jalal,Hawre;Alarid-Escudero,Fernando
目的：个体水平的模拟模型通常需要事件的采样时间；然而，许多过程的有效参数分布通常不存在。例如，生命表中的死亡时间不能从现有的参数分布中准确地抽样。我们提出了一种有效的非参数方法来对事件的时间进行采样，该方法不需要任何关于风险的参数假设。方法我们提出了一种非参数抽样方法(NPS)，它同时从分类分布中提取多个事件时间样本。该方法可应用于单变量过程和多变量过程。我们将整个周期离散成等长的时间区间，然后推导出区间特定的概率。然后可以直接在个体级别的模拟模型中使用事件的时间。我们比较了我们的方法在从常见的参数分布(包括指数分布、伽马分布和Gompertz分布)中采样事件间隔时间的准确性。结果3个参数分布、100,000个同质队列、200,000个异质队列、100,000个时变协变量参数分布估计出与事件相似的预期时间(1 ，000,000次抽签)， ，000,000次抽签，100,000次抽签。非参数抽样方法是通用的，可以在不需要任何参数假设的情况下，从任何离散的(或可离散的)危险中采样到事件的时间，而不需要任何参数假设。该方法展示了离散事件模拟模型中常用的5种分布。与分析结果相比，该方法产生了非常相似的事件的期望时间及其概率分布。我们为R和PYTHON编程语言提供了一个多元分类抽样函数，用于同时从具有不同危险的过程中采样事件的时间。
Purpose.Individual-level simulation models often require sampling times to events; however, efficient parametric distributions for many processes may often not exist. For example, time to death from life tables cannot be accurately sampled from existing parametric distributions. We propose an efficient nonparametric method to sample times to events that does not require any parametric assumption on the hazards.Methods.We developed a nonparametric sampling (NPS) approach that simultaneously draws multiple time-to-event samples from a categorical distribution. This approach can be applied to univariate and multivariate processes. We discretize the entire period into equal-length time intervals and then derived the interval-specific probabilities. The times to events can then be used directly in individual-level simulation models. We compared the accuracy of our approach in sampling time-to-events from common parametric distributions, including exponential, gamma, and Gompertz. In addition, we evaluated the method’s performance in sampling age to death from US life tables and sampling times to events from parametric baseline hazards with time-dependent covariates.Results.The NPS method estimated similar expected times to events from 1 million draws for the 3 parametric distributions, 100,000 draws for the homogenous cohort, 200,000 draws from the heterogeneous cohort, and 1 million draws for the parametric distributions with time-varying covariates, all in less than 1 second.Conclusion.Our method produces accurate and computationally efficient samples for time to events from hazards without requiring parametric assumptions.HighlightsThe nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.We provide a multivariate categorical sampling function for R and Python programming languages to sample times to events from processes with different hazards simultaneously.