Tag: Survival Analysis
Integrative Bayesian Analysis of High-dimensional Multiplatform Genomics Data
This note is for Wang, W., Baladandayuthapani, V., Morris, J. S., Broom, B. M., Manyam, G., & Do, K.-A. (2013). iBAG: Integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics, 29(2), 149–159.
Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics.
This note is for Ni, Y., Stingo, F. C., Ha, M. J., Akbani, R., & Baladandayuthapani, V. (2019). Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics. Journal of the American Statistical Association, 114(525), 48–60.
- Joint Model of Longitudinal and Survival Data
- Additive Bayesian Variable Selection
- Counting Process Based Dimension Reduction Methods for Censored Data
Essentials of Survival Time Analysis
This post aims to clarify the relationship between rates and probabilities.
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.