{"title":"阶段结构开发模型的改进近似贝叶斯计算方法。","authors":"Hoa Pham, Huong T T Pham, Kai Siong Yow","doi":"10.1515/ijb-2025-0065","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An enhanced approximate Bayesian computation method for stage-structured development models.\",\"authors\":\"Hoa Pham, Huong T T Pham, Kai Siong Yow\",\"doi\":\"10.1515/ijb-2025-0065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":50333,\"journal\":{\"name\":\"International Journal of Biostatistics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Biostatistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1515/ijb-2025-0065\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Biostatistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1515/ijb-2025-0065","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An enhanced approximate Bayesian computation method for stage-structured development models.
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.
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.