VALUE OF INFORMATION ANALYSIS TO EXPLORE THE UNCERTAINTY OF AN EARLY IDENTIFICATION MODEL OF ALZHEIMER'S DISEASE

Purpose: To estimate the societal value of reducing uncertainty in the decision whether or not to use cerebrospinal fluid (CSF) biomarker testing to target treatments for patients with mild cognitive impairment (MCI) who are at increased risk of developing Alzheimer’s disease (AD).

Method: We used a previously developed model that evaluated the cost-effectiveness of different test-and-treat strategies for MCI patients. CSF biomarker testing categorized patients into risk groups to target treated with cholinesterase inhibitors for a subset of patients. We used value of information analysis (VOI) to quantify the expected gain from reducing parameter uncertainty associated these test-and-treat strategies. We derived the expected value of perfect information (EVPI) for all parameters or a single parameter (partial EVPI), as well as the corresponding expected value of sampling information (EVSI), and computed the optimal sample sizes for additional research through the expected net benefit of sampling (ENBS) for those parameters. To demonstrate the use of EVSI and ENBS to determine the optimal sample size of a new study, we assumed that a fixed cost of $10 million and a variable cost of $2,000 per patient for a study collecting data on all parameters. If data on only one parameter was to be collected, we assumed a fixed cost of $5 million and a variable cost of $1,000 per patient.

Result: The total EVPI was $1,991 per patient. Parameters of the treatment effectiveness on patients with mild AD and the treatment effectiveness on MCI patients were most responsible for uncertainty of the decision (partial EVPI = $1,031 and $567, respectively). A maximum ENBS of about $33 million was reached for an optimal sample size of 3,500 patients of a hypothetical new study including all parameters. A study collecting data to inform the parameter of the treatment effectiveness on patients with mild AD would have an optimal sample size of 1,900 patients. Because the estimated population EVSI for the treatment effectiveness on MCI patients was less than study costs assumed, additional research to inform this parameter was not justified.

Conclusion: Given our estimates of study costs, the efficient study design for the use of CSF biomarker testing on MCI patients for early intervention purpose involves a trial of 1,900 patients on the treatment effectiveness on patients with mild AD.