ORAL ABSTRACTS: APPLICATIONS OF VALUE OF INFORMATION AND COST EFFECTIVENESS ANALYSIS
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.
Method: We constructed a Markov model to estimate the lifetime costs and quality-adjusted life-years (QALYs) of clinical assessment plus cerebrospinal fluid biomarker levels compared to clinical assessment alone for the diagnosis of AD in a cohort of patients with suspected AD. We considered the influence of the prevalence of AD in the referred population, the cost of the biomarker test, the quality-of-life reduction associated with the invasiveness of lumbar puncture, and the combination assessment sensitivity and specificity. Lifetime discounted costs, including those of unpaid caregivers, and health benefits in QALYs were estimated using a U.S. societal perspective.
Result: At an AD prevalence of 15% in the referred population, clinical assessment plus biomarker (sensitivity of 68% and specificity of 93%) costs $1,747 per person less and reduces QALYs by 0.011 when compared to clinical assessment alone (sensitivity of 43% and specificity of 81%). The cost savings are largely due to the reduction in the number of false positives who receive costly treatment with no utility benefit. The decrease in QALYs is primarily due to the invasiveness of lumbar puncture (quality-of-life reduction of 0.02 QALYs). At a willingness to pay of $50,000/QALY or $100,000/QALY, clinical assessment plus biomarker is preferred to clinical assessment alone. If the prevalence of AD in the referral population is greater than 35%, clinical diagnosis plus biomarker decreases costs and increases QALYs compared to clinical diagnosis alone. Clinical assessment alone is preferred if the quality-of-life reduction from lumbar puncture is greater than 0.03 QALY and for referred populations with low AD prevalence (less than 8% of the population).
Conclusion: The optimal diagnostic strategy depends on the prevalence of AD in the population and the quality-of-life reduction from lumbar puncture, but it is likely that clinical diagnosis plus biomarker is preferred for patients referred for suspected AD.
Anti-vascular endothelial growth factor (anti-VEGF) therapies can prevent blindness from age-related macular degeneration (AMD), but they consume 1/6th of Medicare’s part B drug budget. The FDA-approved medication, ranibizumab, costs almost 40x the off-label medication, bevacizumab. Randomized trials have shown similar efficacy, but the trials were not powered to detect small differences in important safety outcomes. Prior cost-effectiveness analyses suggest ranibizumab has an incremental cost-effectiveness ratio in the millions of dollars when compared to bevacizumab. However, 1/3rd of patients currently receive ranibizumab. We assess the value of perfect implementation with current information and compare this to the value of perfect implementation andinformation.
We combine population models of disease incidence and prevalence along with patient-level models of disease progression and treatment to project 10-year costs and health outcomes for AMD patients in the United States treated with ranibizumab and bevacizumab. We examine societal costs both under current practice and with perfect implementation where all patients receive cost-effective therapy. We synthesize uncertainty in clinical trial safety results and ascertain the expected value of perfect information (EVPI) (assuming perfect implementation) using Monte Carlo simulation.
With current information, bevacizumab is cost-effective assuming a willingness-to-pay (WTP) of $100,000/QALY. Over 10 years, 2.6 million patients will receive anti-VEGF treatment for AMD in the United States. If all treated patients were to receive bevacizumab, the value of perfect implementation would be $46 billion. Monte Carlo simulation shows an 8% chance that the preferred therapy would be ranibizumab. The EVPI is $4.9 billion. These results are robust to the choice of threshold WTP.
Comparing the value of perfect implementation with the value of perfect information helps prioritize policies. Because of the large populations involved and the vast cost differences in therapies, the value of perfect implementation of anti-VEGF therapies for AMD is about an order of magnitude larger than the value of information. This suggests that policies should focus on ways to increase use of therapy known to be cost-effective for AMD. The value of future studies that gather more information on the effectiveness of therapies for AMD should be thought of in terms of how the results may influence real-world therapy choices by patients and providers, improving implementation of cost-effective therapies.
Method: A cost-effectiveness analysis was conducted from a healthcare perspective. A Markov model was constructed, evaluating the cost-effectiveness of BT compared to usual care (UC). Model inputs included all direct healthcare costs, cost-savings from exacerbation reduction, and QOL benefits from exacerbation and mortality reduction. Utilities and costs were obtained from recent clinical trials and secondary data sources. Outcomes were measured in quality adjusted life years (QALY), incremental cost-effectiveness ratios (ICERs) and clinical events including hospitalizations. The analysis was conducted at 5-years, the time-frame of the clinical trials, and 10-years, a conservative estimate of BT’s minimum effectiveness. Deterministic and probabilistic sensitivity analysis (PSA) was performed to assess the robustness of the model and inputs to population and parameter changes.
Result: BT had an ICER of $45,170/QALY at 5-years, and $29,821/QALY at 10 years compared to UC. At both time frames, BT’s ICER was below the conservative willingness to pay threshold (WTP) of $50,000/QALY. Other outcomes indicated at 10 years, the BT group would have $4,633 less in ER and hospitalization costs, and $2,592-$4,244 less in medication costs.
Sensitivity analysis indicated results were sensitive to the cost of BT and probability of exacerbations in the BT and UC group. At 10-years, two thresholds were identified. At a WTP of $50,000, if the cost of the BT series exceeded $10,384 or if the probability of exacerbations fell below 0.63/year with UC, BT would no longer be cost-effective. PSA produced results similar to the baseline analysis. The cost-effectiveness acceptability curve summarized BT would be cost-effective at 10-years with a 93.3% probability for a WTP of $50,000.
Conclusion: BT is likely to be a cost effective treatment for asthmatics at high risk of exacerbations. Continuing to follow asthmatics treated with BT beyond five-years will help inform longer efficacy and support its cost-effectiveness.
Purpose: Gout is the most common inflammatory arthritis in the United States, and several urate-lowering treatment strategies are used to manage symptoms. The value of collecting additional information of key parameters in the cost-effectiveness of urate-lowering treatment strategies for the management of gout is unknown. We apply a meta-modeling approach to calculate the expected value of perfect information (EVPI), expected value of partial perfect information (EVPPI), and the expected value of sample information for parameters (EVPSI) on all model parameters (e.g., utilities, efficacy, and cost).
Methods: We used a previously developed model that evaluated the cost-effectiveness of five urate-lowering strategies: no treatment, allopurinol or febuxostat only, allopurinol- febuxostat sequential therapy, and febuxostat-allopurinol sequential therapy. Health states in the model accounted for disease status: controlled, uncontrolled on medication, and uncontrolled off medication. To quantify uncertainty in the model we conducted a probabilistic sensitivity analysis (PSA). We implemented a linear regression meta-model to the dataset generated from the PSA. Conceptually similar parameters were evaluated together (e.g., utilities) since a single study is likely to inform all of these parameters. To inform future research design we extrapolated EVPI, EVPPI, and EVPSI on a United States population level for an annual incidence of 29,376 new gout patients assuming a decision lifetime of 10 years. Finally, we calculated the optimal sample size of a future study assuming a patient survey would be administered during a clinical visit (fixed cost $6,000; cost per patient $100) to evaluate the parameter group of interest.
Results: Population EVPI varies by a decision maker's willingness-to-pay (WTP) per quality-adjusted life year and is $227 million for WTP of $100,000. EVPPI is highest for utility parameters when WTP is $50,000-$100,000. Figure 1 shows population EVPSI for parameters evaluating utilities, cost of research, expected net benefit of sampling (ENBS), and the optimal sample size for a survey conducted in a clinic evaluating gout patients' health utilities. Given a WTP of $100,000, the optimal sample size of a survey based research study evaluating the health utility of gout patients is 8,600. If the costs of research doubles the optimal sample size is 5,700.
Conclusions: Future studies should be conducted to evaluate utility of gout patients.
Method: We developed a probabilistic state-transition microsimulation model to compare two strategies: MRD testing followed by risk-directed treatment versus no testing. The base case was a 6-year old child with newly diagnosed precursor B-cell ALL at the beginning of induction chemotherapy. We tracked changes in the risk of relapse after the first and second MRD tests at the end of induction and consolidation, respectively, to simulate patients’ prognosis and predict treatment in consolidation and maintenance phases. The analytic perspective was that of the Ontario Ministry of Health and Long-Term Care and the time horizon was the patient’s lifetime. The effect of MRD-risk-directed treatment intensification was based on the estimate of the UKALL2003 trial; other input parameters were estimated from the literature, expert opinion and a paediatric population-based clinical networked information system (POGONIS). Outcomes were expressed as the lifetime probability of relapse or bone marrow transplant (BMT), survival, quality-adjusted life years (QALYs), lifetime costs and incremental cost-effectiveness ratios. Costs and QALYs were discounted at 5%.
Result: MRD testing versus no testing was associated with increased life expectancy (66.50 vs. 66.04 years) and lower rates of first relapse or first BMT (relapse: 40.08% vs. 41.37%; BMT: 20.97% vs. 21.07%). Compared to no testing, MRD testing was associated with an increased quality-adjusted survival of 0.08 QALYs (95% confidence interval [CI]: -0.29; 0.46) and incremental costs of $3,863 (95%CI: -8,498; 15,530), yielding an incremental cost-effectiveness ratio of $50,249/QALY gained. The results were sensitive to the effectiveness of MRD-risk directed treatment, costs of ALL treatment and probability of BMT after consolidation. The probability that MRD testing was cost-effective was 57.80% at a threshold of $100,000/QALY.
Conclusion: MRD testing of newly diagnosed patients with childhood ALL followed by risk- directed treatment results in better health outcomes and appears to be cost-effective with a considerable degree of uncertainty.