Too many cohorts and repeated measurements are a waste of resources

Abstract Objective Researchers in Health Sciences and Medicine often use cohort designs to study treatment effects and changes of outcome variables over time period. The costs of these studies can be reduced by choosing an optimal number of repeated measurements over time and by selecting cohorts of...

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Bibliographic Details
Published in:Journal of clinical epidemiology 2011-12, Vol.64 (12), p.1383-1390
Main Authors: Tekle, Fetene B, Tan, Frans E.S, Berger, Martijn P.F
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Cohort design
Cohort Studies
Confidence intervals
Cost-Benefit Analysis
Costs
D-optimal designs
Design
Disease prevention
Economic models
Epidemiology
Growth models
Guidelines as Topic
Health sciences
Humans
Internal Medicine
Linear Models
Longitudinal Studies - economics
Longitudinal study
Medical sciences
Miscellaneous
Mixed-effects model
Public health. Hygiene
Public health. Hygiene-occupational medicine
Relative efficiency
Research Design - standards
Research Support as Topic
Studies
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Summary:Abstract Objective Researchers in Health Sciences and Medicine often use cohort designs to study treatment effects and changes of outcome variables over time period. The costs of these studies can be reduced by choosing an optimal number of repeated measurements over time and by selecting cohorts of subjects more efficiently with optimal design procedures. The objective of this study is to provide evidence on how to design large-scale cohort studies with budget constraints as efficiently as possible. Study Design and Setting A linear cost function for repeated measurements is proposed, and this cost function is used in the optimization procedure. For a given budget/cost, different designs for linear mixed-effects models are compared by means of their efficiency. Results We found that adding more repeated measures is only beneficiary if the costs of selecting and measuring a new subject are much higher than the costs of obtaining an additional measurement for an already recruited subject. However, this gain in efficiency and power is not very large. Conclusion Adding more cohorts or repeated measurements do not necessarily lead to a gain in efficiency of the estimated model parameters. A general guideline for the optimal choice of a cohort design in practice is required and we offer this guideline.
ISSN:0895-4356
1878-5921
DOI:10.1016/j.jclinepi.2010.11.023