An enhanced approximate Bayesian computation method for stage-structured development models.

IF 1.2 4区 数学
Hoa Pham, Huong T T Pham, Kai Siong Yow
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引用次数: 0

Abstract

Multi-stage models for cohort data are widely used in various fields, including disease progression, the biological development of plants and animals, and laboratory studies of life cycle development. However, the likelihood functions of these models are often intractable and complex. These complexities in the likelihood functions frequently result in significant biases and high computational costs when estimating parameters using current Bayesian methods. This paper aims to address these challenges by applying the enhanced Sequential Monte Carlo approximate Bayesian computation (ABC-SMC) method, which does not rely on explicit likelihood functions, to stage-structured development models with non-hazard rates and stage-wise constant hazard rates. Instead of using a likelihood function, the proposed method determines parameter estimates based on matching vector summary statistics. It incorporates stage-wise parameter estimations and retains accepted parameters across stages. This approach not only reduces model biases but also improves the computational efficiency of parameter estimations, despite the computational intractability of the likelihood functions. The proposed ABC-SMC method is validated through simulation studies on stage-structured development models and applied to a case study of breast development in New Zealand schoolgirls. The results demonstrate that the proposed methods effectively reduce biases in later-stage estimates for stage-structured models, enhance computational efficiency, and maintain accuracy and reliability in parameter estimations compared to the current methods.

阶段结构开发模型的改进近似贝叶斯计算方法。
队列数据的多阶段模型广泛应用于疾病进展、动植物生物学发育以及生命周期发育的实验室研究等各个领域。然而,这些模型的似然函数通常是难以处理和复杂的。当使用当前的贝叶斯方法估计参数时,这些复杂性在似然函数中经常导致显著的偏差和高计算成本。本文旨在通过应用增强型序列蒙特卡罗近似贝叶斯计算(ABC-SMC)方法来解决这些挑战,该方法不依赖于显式似然函数,用于具有非风险率和阶段恒定风险率的阶段结构开发模型。该方法不使用似然函数,而是基于匹配向量汇总统计来确定参数估计。它结合了分段参数估计,并在各阶段保留可接受的参数。这种方法不仅减少了模型偏差,而且提高了参数估计的计算效率,尽管似然函数的计算困难。提出的ABC-SMC方法通过阶段结构发育模型的模拟研究得到了验证,并应用于新西兰女学生乳房发育的案例研究。结果表明,与现有方法相比,所提出的方法有效地减少了阶段结构模型后期估计中的偏差,提高了计算效率,并保持了参数估计的准确性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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