ArticleThe annals of applied statistics
Fitting Birth-Death Processes to Panel Data with Applications to Bacterial DNA Fingerprinting.
Article in The annals of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Birth/birth-death processes and their computable transition probabilities with biological applications.Journal of mathematical biology · 2018Article
- Likelihood-based inference for discretely observed birth-death-shift processes, with applications to evolution of mobile genetic elements.Biometrics · 2015Article
- Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2015Article
- Estimation for general birth-death processes.Journal of the American Statistical Association · 2014Article
- Fitting Birth-Death Processes to Panel Data with Applications to Bacterial DNA Fingerprinting.The annals of applied statisticsArticle
- Computational methods for birth-death processes.Wiley interdisciplinary reviews. Computational statisticsArticle
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Authors and funding
5 authors.
Funding
Abstract
Continuous-time linear birth-death-immigration (BDI) processes are frequently used in ecology and epidemiology to model stochastic dynamics of the population of interest. In clinical settings, multiple birth-death processes can describe disease trajectories of individual patients, allowing for estimation of the effects of individual covariates on the birth and death rates of the process. Such estimation is usually accomplished by analyzing patient data collected at unevenly spaced time points, referred to as panel data in the biostatistics literature. Fitting linear BDI processes to panel data is a nontrivial optimization problem because birth and death rates can be functions of many parameters related to the covariates of interest. We propose a novel expectation-maximization (EM) algorithm for fitting linear BDI models with covariates to panel data. We derive a closed-form expression for the joint generating function of some of the BDI process statistics and use this generating function to reduce the E-step of the EM algorithm, as well as calculation of the Fisher information, to one-dimensional integration. This analytical technique yields a computationally efficient and robust optimization algorithm that we implemented in an open-source R package. We apply our method to DNA fingerprinting of
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.