How many longitudinal covariate measurements are needed for risk prediction?
Reinikainen, J., Karvanen, J., & Tolonen, H. (2016). How many longitudinal covariate measurements are needed for risk prediction?. Journal of Clinical Epidemiology, 69, 114-124. doi:10.1016/j.jclinepi.2015.06.022
Published inJournal of Clinical Epidemiology
© 2016 Elsevier Inc. This is a preprint version of an article whose final and definitive form has been published by Elsevier.
Objective: In epidemiological follow-up studies, many key covariates, such as smoking, use of medication, blood pressure and cholesterol, are time-varying. Because of practical and financial limitations, time-varying covariates cannot be measured continuously, but only at certain prespecified time points. We study how the number of these longitudinal measurements can be chosen cost-efficiently by evaluating the usefulness of the measurements for risk prediction. Study Design and Setting: The usefulness is addressed by measuring the improvement in model discrimination between models using different amounts of longitudinal information. We use simulated follow-up data and the data from the Finnish East–West study, a follow-up study, with eight longitudinal covariate measurements carried out between 1959 and 1999. Results: In a simulation study, we show how the variability and the hazard ratio of a time-varying covariate are connected to the importance of re-measurements. In the East–West study, it is seen that for older people, the risk predictions obtained using only every other measurement are almost equivalent to the predictions obtained using all eight measurements. Conclusion: Decisions about the study design have significant effects on the costs. The cost-efficiency can be improved by applying the measures of model discrimination to data from previous studies and simulations. ...