e-journal

Which individuals make dropout informative?

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Markers are internal host factors that measure the current disease or recovery status of an individual.
Individuals with more advanced disease progression are more likely to drop out, e.g. because they die.
Marker data after dropout are missing. Such missingness is certainly not completely at random. A mixed
effects model can be used if missingness of the marker data depends on measured marker values only
(missing at random). If missingness is not at random, such models yield biased results.We describe various
approaches that jointly model the marker development and dropout risk and may eliminate bias. One
example of such a model is a random effects selection model. Based on a real data set with frequent
follow-up, we compare results from a random effects model and a random effects selection model. Results
are remarkably similar. In a simulation study, we investigate how the bias in the parameter estimates from
a random effects model depends on the frequency of measurements and the time between the last
measurement and the dropout or censoring time. Results from the simulation study confirm that the
bias is small if follow-up is frequent.

Keywords
Longitudinal data, missing not at random, random effects selection model, AIDS markers

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Informasi Detail

Judul Seri

Statistical Methods in Medical Research

No. Panggil

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Penerbit

London : SAGE., 2014

Deskripsi Fisik

Statistical Methods in Medical Research 2014, Vol. 23(1) 91–106

Bahasa

English

ISBN/ISSN

DOI: 10.1177/0962280

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

2014, Vol. 23(1)

Subjek

STATISTIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

agus

Versi lain/terkait

Lampiran Berkas

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