Tag: Survival Analysis
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Biomarker Variability in Joint Model
This note is for Wang, C., Shen, J., Charalambous, C., & Pan, J. (2024). Modeling biomarker variability in joint analysis of longitudinal and time-to-event data. Biostatistics, 25(2), 577–596. https://doi.org/10.1093/biostatistics/kxad009 and Wang, C., Shen, J., Charalambous, C., & Pan, J. (2024). Weighted biomarker variability in joint analysis of longitudinal and time-to-event data. The Annals of Applied Statistics, 18(3), 2576–2595. https://doi.org/10.1214/24-AOAS1896
- Integrative Bayesian Analysis of High-dimensional Multiplatform Genomics Data
- Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics.
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Joint Model of Longitudinal and Survival Data
This post is based on Rizopoulos, D. (2017). An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R. 235.
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Additive Bayesian Variable Selection
This post is based on Rossell, D., & Rubio, F. J. (2019). Additive Bayesian variable selection under censoring and misspecification. ArXiv:1907.13563 [Math, Stat].
- Counting Process Based Dimension Reduction Methods for Censored Data
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Essentials of Survival Time Analysis
This post aims to clarify the relationship between rates and probabilities.
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Cox Regression
Survival analysis examines and models the time it takes for events to occur. It focuses on the distribution of survival times. There are many well known methods for estimating unconditional survival distribution, and they examines the relationship between survival and one or more predictors, usually terms covariates in the survival-analysis literature. And Cox Proportional-Hazards regression model is one of the most widely used method of survival analysis.