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  • 标题:idmTPreg: Regression Model for Progressive Illness Death Data
  • 本地全文:下载
  • 作者:Leyla Azarang ; Manuel Oviedo de la Fuente
  • 期刊名称:R News
  • 印刷版ISSN:1609-3631
  • 出版年度:2018
  • 卷号:10
  • 期号:2
  • 页码:317-325
  • 语种:English
  • 出版社:The R Foundation for Statistical Computing
  • 摘要:The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the introduced estimators obtained using a direct binomial regression approach. Also, variance estimates and confidence bands are implemented in the package. This article presents guidelines for the use of the package.
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