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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14677

Title: Longitudinal conditional models with intermittent missingness: SAS code and applications
Authors: Uranga, R.
Molenberghs, G.
Issue Date: 2014
Citation: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 84 (4), p. 753-780
Abstract: This work provides a set of macros performed with SAS (Statistical Analysis System) for Windows, which can be used to fit conditional models under intermittent missingness in longitudinal data. A formalized transition model, including random effects for individuals and measurement error, is presented. Model fitting is based on the missing completely at random or missing at random assumptions, and the separability condition. The problem translates to maximization of the marginal observed data density only, which for Gaussian data is again Gaussian, meaning that the likelihood can be expressed in terms of the mean and covariance matrix of the observed data vector. A simulation study is presented and misspecification issues are considered. A practical application is also given, where conditional models are fitted to the data from a clinical trial that assessed the effect of a Cuban medicine on a disease of the respiratory system.
Notes: Reprint Address: Uranga, R (reprint author) Natl Ctr Clin Trials, Dept Data Management & Stat, 23 & 200 St, Havana, Cuba. E-mail Addresses:rolando.uranga@cencec.sld.cu
URI: http://hdl.handle.net/1942/14677
DOI: 10.1080/00949655.2012.725403
ISI #: 000336834100004
ISSN: 0094-9655
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
vabb, 2014
Appears in Collections: Research publications

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