D-optimal cohort designs for linear mixed-effects models.

Full Abstract

The D-optimality criterion is used to construct optimal designs for different numbers of independent cohorts, which constitute a number of repeated measurements per subject over time. A cost function for longitudinal data is proposed, and the optimality criterion is optimized taking into account the cost of the study. First, an optimal number of design points for a given number of cohorts and cost was identified. Then, an optimal number of cohorts is identified by comparing the relative efficiencies (REs). A numerical study shows that for models describing the trend of a continuous outcome over time by polynomials, the most efficient number of repeated measurements is equal to the sum of the total number of cohorts and the degree of the polynomial in the model. REs of a purely longitudinal cohort design with only one cohort, and mixed longitudinal and cross-sectional cohort designs with more cohorts are compared. The results show that a purely longitudinal cohort design with only one cohort of subjects measured at the optimal time points is the most efficient design. The findings in this paper show that one can obtain a highly efficient design for parameter estimation with only a few repeated measurements. The results of this study will reduce the cost of data collection and ease the logistical burdens in cohort studies.

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Author information

Author/s: Tekle, Fetene B (FB); Tan, Frans E S (FE); Berger, Martijn P F (MP);

Affiliation: Department of Methodology and Statistics, University of Maastricht, Maastricht, The Netherlands. fetene.bekele(-atsign-)stat.unimaas.nl

Journal and publication information

Publication Type: Journal Article

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2008-Jun; vol 27 (issue 14) : pp 2586-600

Dates: Created 2008/05/28; Completed 2008/10/07;

PMID: 17726724, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Linear Models
Randomized Controlled Trials as Topic
Research Design - statistics & numerical data

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