Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration
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Publication:6077587
DOI10.1080/01621459.2021.2023552OpenAlexW4206463393MaRDI QIDQ6077587
Wensheng Guo, Tianhao Wang, Sarah J. Ratcliffe
Publication date: 18 October 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://figshare.com/articles/dataset/Time-to-Event_Analysis_with_Unknown_Time_Origins_via_Longitudinal_Biomarker_Registration/17839324
Cites Work
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- A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data
- Modeling the dynamics of biomarkers during primary HIV infection taking into account the uncertainty of infection date
- Modeling left-truncated and right-censored survival data with longitudinal covariates
- Extensions of estimation methods using the EM algorithm
- Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time
- Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence
- Parameter Estimation in Longitudinal Studies with Outcome-Dependent Follow-Up
- Biases in Prevalent Cohorts
- Self Modeling with Flexible, Random Time Transformations
- A stochastic model for the distribution of HIV latency time based on T4 counts
- Joint modelling of accelerated failure time and longitudinal data
- Length-Biased Sampling With Right Censoring
- Mixed-Effects Models in S and S-PLUS
- Self-Modelling Warping Functions
- An extended hazard model with longitudinal covariates
- Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models
- Statistical Models for Prevalent Cohort Data
- A practical guide to splines.
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