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An EM algorithm for nonparametric estimation of the cumulative incidence function from repeated imperfect test results
Author(s) -
Witte Birgit I.,
Berkhof Johannes,
Jonker Marianne A.
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7373
Subject(s) - estimator , nonparametric statistics , statistics , expectation–maximization algorithm , mean squared error , mathematics , event (particle physics) , econometrics , algorithm , maximum likelihood , physics , quantum mechanics
In screening and surveillance studies, event times are interval censored. Besides, screening tests are imperfect so that the interval at which an event takes place may be uncertain. We describe an expectation–maximization algorithm to find the nonparametric maximum likelihood estimator of the cumulative incidence function of an event based on screening test data. Our algorithm has a closed‐form solution for the combined expectation and maximization step and is computationally undemanding. A simulation study indicated that the bias of the estimator tends to zero for large sample size, and its mean squared error is in general lower than the mean squared error of the estimator that assumes the screening test is perfect. We apply the algorithm to follow‐up data from women treated for cervical precancer. Copyright © 2017 John Wiley & Sons, Ltd.