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http://dx.doi.org/10.5351/KJAS.2015.28.2.309

Survival Analysis using SRC-Stat Statistical Package  

Ha, Il Do (Department of Statistics, Pukyong National University)
Noh, Maengseok (Department of Statistics, Pukyong National University)
Lee, Youngjo (Data Science for Knowledge Creation Research Center, Seoul National University)
Lim, Johan (Data Science for Knowledge Creation Research Center, Seoul National University)
Lee, Jaeyong (Data Science for Knowledge Creation Research Center, Seoul National University)
Oh, Heeseok (Data Science for Knowledge Creation Research Center, Seoul National University)
Shin, Dongwan (Data Science for Knowledge Creation Research Center, Seoul National University)
Lee, Sanggoo (Data Science for Knowledge Creation Research Center, Seoul National University)
Seo, Jinuk (Data Science for Knowledge Creation Research Center, Seoul National University)
Park, Yonhtae (Data Science for Knowledge Creation Research Center, Seoul National University)
Cho, Sungzoon (Data Science for Knowledge Creation Research Center, Seoul National University)
Park, Jonghun (Data Science for Knowledge Creation Research Center, Seoul National University)
Kim, Youkyung (Data Science for Knowledge Creation Research Center, Seoul National University)
You, Kyungsang (Data Science for Knowledge Creation Research Center, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 309-324 More about this Journal
Abstract
In this paper we introduce how to analyze survival data via a SRC-Stat statistical package. This provides classical survival analysis (e.g. Cox's proportional hazards models for univariate survival data) as well as advanced survival analysis such as shared and nested frailty models for multivariate survival data. We illustrate the use of our package with practical data sets.
Keywords
Cox's proportional hazards models; frailty models; H-likelihood; multivariate survival data; random effects;
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