Titlebook: Emerging Topics in Modeling Interval-Censored Survival Data; Jianguo Sun,Ding-Geng Chen Book 2022 The Editor(s) (if applicable) and The Au

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https://doi.org/10.1007/978-981-19-9263-6 time-dependent covariates. Simulation results demonstrate the proposed estimator is approximately unbiased and the standard errors are well estimated from the sandwich estimators. The methods are applied to an observational study which examined the association between hormonal contraceptive use and risk of HIV acquisition.

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Book 2022deling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censored survival data can easily be mistaken for typical right-censored survival data, which can result in erroneous statistical inference due to the complexity of

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2199-0980 analyze interval-censored data.Details R/SAS implementations.This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biome

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Example II: The Neumann Probleman model and as such INLA can be used for near real-time Bayesian inference. We provide a brief summary of the INLA methodology and illustrate the approach on real data examples with interval censoring, including a joint model. The analysis is done using the . package . and all code is available for reproducibility.

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Structured Eigenvalue Perturbation Theory obtained using the sieve maximum likelihood method, and the resulting estimators are shown to be consistent and asymptotically normal under mild regularity conditions. The finite sample performance of these models is examined through simulation studies and their practical applications are illustrated by real data examples.

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https://doi.org/10.1007/978-1-4842-5064-8l-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.

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