VALUE OF INFORMATION METHODS FOR OPTIMAL TIMING OF BIOMARKER COLLECTION

Purpose: 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, predictions based on patients’ earlier biomarker values may be used to inform dynamic decision-making; however, predicted biomarker levels are uncertain, giving rise to decision uncertainty. Our goal was to develop value of information (VOI) methods to determine at what time-point direct collection of biomarker data would be most valuable. This goal also fits squarely with this year’s SMDM theme “From Uncertainty to Action”.

Methods: We illustrate our methods using longitudinal data from 1993-2011 from the cystic fibrosis national registry on patients. FEV₁% is typically measured on patients at regular clinic visits and the last measured value is used to determine expected survival and need for lung transplantation. We contrasted this with an alternative approach. Biomarker prediction models were developed to predict FEV₁% values based on earlier measurements and these predictions were used to determine expected survival and the need for transplantation. 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. Using actual annual FEV₁% values, we validated the implication of the VOI methods for optimal timing of biomarker collection.

Results: Decision-making about lung transplant assignments over 18 years for 11,254 patients using predicted values of FEV₁% data generated a total of 138,699 life years for the same patients. The VOI model suggested that if only one biomarker measurement is available for prediction, the value of collecting the biomarker annually is very high. However, including more than one past measurement to capture patient history substantially decreases the value of annual collection such that the cost of updating biomarker levels is worthwhile only every three years, at $100K/LY. Furthermore, biomarker collection can be made even more efficient by targeting individuals with larger deterioration of predicted information over time. Actual annually collected FEV₁% data validated the VOI-based recommendation.

Conclusions: A VOI approach to determining the optimal time interval between updating biomarker data is feasible and could be applicable to a variety of clinical conditions.