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A NONPARAMETRIC MIXED‐EFFECTS MODEL FOR CANCER MORTALITY
Author(s) -
Tonda Tetsuji,
Satoh Kenichi,
Nakayama Teruyuki,
Katanoda Kota,
Sobue Tomotaka,
Ohtaki Megu
Publication year - 2011
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2011.00615.x
Subject(s) - mixed model , mathematics , nonparametric statistics , nonparametric regression , random effects model , kernel smoother , smoothing , longitudinal data , statistics , covariate , econometrics , data set , kernel method , computer science , medicine , data mining , artificial intelligence , meta analysis , radial basis function kernel , support vector machine
Summary There are several ways to handle within‐subject correlations with a longitudinal discrete outcome, such as mortality. The most frequently used models are either marginal or random‐effects types. This paper deals with a random‐effects‐based approach. We propose a nonparametric regression model having time‐varying mixed effects for longitudinal cancer mortality data. The time‐varying mixed effects in the proposed model are estimated by combining kernel‐smoothing techniques and a growth‐curve model. As an illustration based on real data, we apply the proposed method to a set of prefecture‐specific data on mortality from large‐bowel cancer in Japan.