Too many cohorts and repeated measurements are a waste of resources

• Published in: Journal of clinical epidemiology Vol. 64; no. 12; pp. 1383 - 1390
• Main Authors: Tekle, Fetene B., Tan, Frans E.S., Berger, Martijn P.F.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.12.2011; Elsevier; Elsevier Limited
• 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; Mixed-effects model; Costs; Cohort design; Relative efficiency; D-optimal designs; Longitudinal study; Resource; Goal; Efficiency; Follow up study; Health economy; Cohort study; Repeated measurement; Models; Economic aspect; Wastes; Public health
• ISSN: 0895-4356; 1878-5921; 1878-5921
• DOI: 10.1016/j.jclinepi.2010.11.023

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. 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. 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. 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.