Methods: We apply a Markov decision process framework to evaluate how long to continue HCV screening in US men. We identify the optimal information collection policy for two parameters assumed constant across cohorts - reductions in quality-of-life from awareness of HCV-positive status and the fibrosis-stage distribution at screen - detected diagnosis at age 50 - alone and in combination with information collection about HCV prevalence which is decreasing across cohorts. We estimate lifetime costs and benefits using a previously-developed HCV screening model and HCV prevalence dynamics derived from NHANES. The assumed willingness-to-pay threshold is $75,000 per QALY.
Results: The presence of a parameter which varies across cohorts influences the per-person value-of-information about both time-varying and static parameters. In these cases, we show analytically that it may be optimal to delay information collection. Given our prior beliefs, the optimal strategy is to collect sample information about the reduction in quality-of-life from awareness of HCV-positive status immediately and then, depending on the result of that study, collect information on HCV prevalence 3 to 20 years in the future. This strategy, less the cost of information collection, increases the expected incremental net monetary benefit (INMB) by $2.3 million compared to a strategy of collecting information about both immediately. The optimal time to collect information about the fibrosis-stage distribution is in 12 years, increasing the expected INMB by $1.7 million compared to a strategy of collecting information about both immediately.
Conclusions: We demonstrate that when parameters vary across cohorts, the optimal information collection policy, for both time-varying and static parameters, may be to delay information collection until it is more likely to influence the decision. Our dynamic programming framework enables the consideration of delayed information collection in numerous contexts.