Too many cohorts and repeated measurements are a waste of resources.

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.

[1]  Martijn P. F. Berger,et al.  OPTIMAL ALLOCATION OF TIME POINTS FOR THE RANDOM EFFECTS MODEL , 1999 .

[2]  E. Corder,et al.  Stroke and Apolipoprotein E &egr;4 Are Independent Risk Factors for Cognitive Decline: A Population-Based Study , 2000, Stroke.

[3]  Carmen Rodriguez,et al.  The American Cancer Society Cancer Prevention Study II Nutrition Cohort , 2002, Cancer.

[4]  Martijn P. F. Berger,et al.  A maximin criterion for the logistic random intercept model with covariates , 2006 .

[5]  Mirjam Moerbeek.  Robustness properties of A , 2005, Comput. Stat. Data Anal..

[6]  G. Colditz,et al.  Oral Contraceptive Use and Mortality during 12 Years of Follow-Up: The Nurses' Health Study , 1994, Annals of Internal Medicine.

[7]  Martijn P F Berger,et al.  D‐optimal cohort designs for linear mixed‐effects models , 2008, Statistics in medicine.

[8]  Optimal designs in growth curve models — II Correlated model for quadratic growth: optimal designs for parameter estimation and growth prediction , 1998 .

[9]  Markus Abt,et al.  Optimal designs in growth curve models: Part I Correlated model for linear growth: Optimal designs for slope parameter estimation and growth prediction , 1997 .

[10]  T. Dawber,et al.  The Framingham Study: The Epidemiology of Atherosclerotic Disease , 1980 .

[11]  Anthony C. Atkinson,et al.  Optimum Experimental Designs, with SAS , 2007 .

[12]  Martijn P. F. Berger,et al.  A Comparison of Efficiencies of Longitudinal, Mixed Longitudinal, and Cross-Sectional Designs , 1986 .

[13]  S. A. Ortega-Azurduy,et al.  The effect of dropout on the efficiency of D‐optimal designs of linear mixed models , 2008, Statistics in medicine.

[14]  F. Chang,et al.  Optimal designs for a growth curve model , 2002 .

[15]  H. de Vries,et al.  The SMILE study: a study of medical information and lifestyles in Eindhoven, the rationale and contents of a large prospective dynamic cohort study , 2008, BMC public health.