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Analysis of interval‐grouped recurrent‐event data using piecewise constant rate functions
Author(s) -
Lawless J. F.,
Zhan M.
Publication year - 1998
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315717
Subject(s) - piecewise , constant (computer programming) , mathematics , poisson regression , interval (graph theory) , event (particle physics) , poisson distribution , statistics , segmented regression , regression , baseline (sea) , longitudinal data , regression analysis , computer science , data mining , nonlinear regression , medicine , mathematical analysis , combinatorics , population , physics , environmental health , quantum mechanics , programming language , oceanography , geology
We consider situations where subjects in a longitudinal study experience recurrent events. However, the events are observed only in the form of counts for intervals which can vary across subjects. Methods for estimating the mean and rate functions of the recurrent‐event processes are presented, based on loglinear regression models which incorporate piecewise‐constant baseline rate functions. Robust methods and methods based on mixed Poisson processes are compared in a simulation study and in an example involving superficial bladder tumours in humans. Both approaches provide a simple and effective way to deal with interval‐grouped data.