ODMIXED: A tool to obtain optimal designs for heterogeneous longitudinal studies with dropout

Shirley Ortega; Frans E. S. Tan; Martijn P. F. Berger

doi:10.1016/j.cmpb.2010.04.004

ODMIXED: A tool to obtain optimal designs for heterogeneous longitudinal studies with dropout

Shirley Ortega^*, Frans E. S. Tan, Martijn P. F. Berger

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

ODMIXED is a computer program to obtain optimal designs for linear mixed models of longitudinal studies. These designs account for heterogeneous correlated errors and for data with dropout. Designs are compared by using relative efficiencies, e.g., between a D-optimal design for homogeneous data and another for heterogeneous data or between a D-optimal design for complete data against another that optimizes designs when data is missing at random. Two examples are worked out to illustrate how researchers could use this computer program to profit of optimal design theory at the planning stage of longitudinal studies.

Original language	English
Pages (from-to)	62-71
Journal	Computer Methods and Programs in Biomedicine
Volume	101
Issue number	1
DOIs	https://doi.org/10.1016/j.cmpb.2010.04.004
Publication status	Published - Jan 2011

Keywords

D-optimal designs
Dropout
Heterogeneous autocorrelation
Linear mixed models
Longitudinal data
Relative efficiency

Access to Document

10.1016/j.cmpb.2010.04.004

Cite this

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title = "ODMIXED: A tool to obtain optimal designs for heterogeneous longitudinal studies with dropout",

abstract = "ODMIXED is a computer program to obtain optimal designs for linear mixed models of longitudinal studies. These designs account for heterogeneous correlated errors and for data with dropout. Designs are compared by using relative efficiencies, e.g., between a D-optimal design for homogeneous data and another for heterogeneous data or between a D-optimal design for complete data against another that optimizes designs when data is missing at random. Two examples are worked out to illustrate how researchers could use this computer program to profit of optimal design theory at the planning stage of longitudinal studies. ",

keywords = "D-optimal designs, Dropout, Heterogeneous autocorrelation, Linear mixed models, Longitudinal data, Relative efficiency",

author = "Shirley Ortega and Tan, {Frans E. S.} and Berger, {Martijn P. F.}",

year = "2011",

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doi = "10.1016/j.cmpb.2010.04.004",

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KW - Heterogeneous autocorrelation

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KW - Longitudinal data

KW - Relative efficiency

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