Open AccessModeling Hierarchically Clustered Longitudinal Survival Processes with Applications to Child Mortality and Maternal Health
Author(s) -
Barthélemy Kuate Defo
Publication year - 2001
Publication title -
canadian studies in population
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.157
H-Index - 8
eISSN - 1927-629X
pISSN - 0380-1489
DOI - 10.25336/p6z313
Subject(s) - longitudinal data , hazard , computer science , statistical model , event (particle physics) , estimation , multilevel model , event data , econometrics , duration (music) , data science , data mining , statistics , machine learning , engineering , mathematics , art , chemistry , physics , organic chemistry , systems engineering , literature , quantum mechanics , analytics
This paper merges two parallel developments since the 1970s of new statistical tools for data analysis: statistical methods known as hazard models that are used for analyzing event-duration data and statistical methods for analyzing hierarchically clustered data known as multilevel models. These developments have rarely been integrated in research practice and the formalization and estimation of models for hierarchically clustered survival data remain largely uncharted. I attempt to fill some of this gap and demonstrate the merits of formulating and estimating multilevel hazard models with longitudinal data.