OBJECTIVES: As a patient’s health evolves over time, knowing its level at any point in time through repeated collection of biomarkers may be critical to determining the benefits of an intervention at that time-point. However, repeated biomarker collection is costly and inconvenient. Alternatively, predicted time trajectories of biomarkers based on patients’ baseline values can be used to inform dynamic decision-making. However, predicted biomarker levels are uncertain, giving rise to decision uncertainty. Value of information (VOI) methods can be used to determine at what time-point direct collection of biomarker data would be most valuable. METHODS: We illustrate our methods using longitudinal data from 1993-2011 from the cystic fibrosis national registry on patients. FEV% is typically measured on patients at regular clinic visits and the last measured value is used to determine expected survival and need for lung transplant. We contrasted this with an alternative approach. For illustration, we fixed the time of transplantation decision at year 2000. Prediction models were developed to use earlier measurements (years 1995-1999) to predict FEV% at year 2000 and these predictions were used to determine expected survival and the need for lung transplant. VOI approaches were applied based on the evolution of prediction uncertainty over time to determine the time-point where more precise information on biomarker levels would be most valuable. RESULTS: In this setting, the VOI model suggested that the value of collecting biomarker information every year is high at $100K/LY. For a cost of biomarker collection up to ~$40K, the cost of updating the biomarker every year is worthwhile. For a high cost of $50,000, the cost of updating is worthwhile at the 2-year mark. CONCLUSIONS: VOI approach to determining optimal time interval between updating biomarker data is feasible and could be applicable to a variety of clinical conditions.