Use of Patient Preferences Data Regarding Multiple Risks to Inform Regulatory Decisions.

Background and Objectives. Risk-tolerance measures from patient-preference studies typically focus on individual adverse events. We recently introduced an approach that extends maximum acceptable risk (MAR) calculations to simultaneous maximum acceptable risk thresholds (SMART) for multiple treatment-related risks. We extend these methods to include the computation and display of confidence intervals and apply the approach to 3 published discrete-choice experiments to evaluate its utility to inform regulatory decisions. Methods. We generate MAR estimates and SMART curves and compare them with trial-based benefit-risk profiles of select treatments for depression, psoriasis, and thyroid cancer. Results. In the depression study, SMART curves with 70% to 95% confidence intervals portray which combinations of 2 adverse events would be considered acceptable. In the psoriasis example, the asymmetric confidence intervals for the SMART curve indicate that relying on independent MARs versus SMART curves when there are nonlinear preferences can lead to decisions that could expose patients to greater risks than they would accept. The thyroid cancer application shows an example in which the clinical incidence of each of 3 adverse events is lower than the single-event MARs for the expected treatment benefit, yet the collective risk profile surpasses acceptable levels when considered jointly. Limitations. Nonrandom sample of studies. Conclusions. When evaluating conventional MARs in which the observed incidences are near the estimated MARs or where preferences demonstrate diminishing marginal disutility of risk, conventional MAR estimates will overstate risk acceptance, which could lead to misinformed decisions, potentially placing patients at greater risk of adverse events than they would accept. Implications. The SMART method, herein extended to include confidence intervals, provides a reproducible, transparent evidence-based approach to enable decision makers to use data from discrete-choice experiments to account for multiple adverse events. Highlights: Estimates of maximum acceptable risk (MAR) for a defined treatment benefit can be useful to inform regulatory decisions; however, the conventional metric considers one adverse event at a time.This article applies a new approach known as SMART (simultaneous maximum acceptable risk thresholds) that accounts for multiple adverse events to 3 published discrete-choice experiments.Findings reveal that conventional MARs could lead decision makers to accept a treatment based on individual risks that would not be acceptable if multiple risks are considered simultaneously.

Quantifying Benefit-Risk Preferences for Heart Failure Devices: A Stated-Preference Study.

BACKGROUND: Regulatory and clinical decisions involving health technologies require judgements about relative importance of their expected benefits and risks. We sought to quantify heart-failure patients' acceptance of therapeutic risks in exchange for improved effectiveness with implantable devices. METHODS: Individuals with heart failure recruited from a national web panel or academic medical center completed a web-based discrete-choice experiment survey in which they were randomized to one of 40 blocks of 8 experimentally controlled choice questions comprised of 2 device scenarios and a no-device scenario. Device scenarios offered an additional year of physical functioning equivalent to New York Heart Association class III or a year with improved (ie, class II) symptoms, or both, with 30-day mortality risks ranging from 0% to 15%, in-hospital complication risks ranging from 0% to 40%, and a remote adjustment device feature. Logit-based regression models fit participants' choices as a function of health outcomes, risks and remote adjustment. RESULTS: Latent-class analysis of 613 participants (mean age, 65; 49% female) revealed that two-thirds were best represented by a pro-device, more risk-tolerant class, accepting up to 9% (95% CI, 7%-11%) absolute risk of device-associated mortality for a one-year gain in improved functioning (New York Heart Association class II). Approximately 20% were best represented by a less risk-tolerant class, accepting a maximum device-associated mortality risk of 3% (95% CI, 1%-4%) for the same benefit. The remaining class had strong antidevice preferences, thus maximum-acceptable risk was not calculated. CONCLUSIONS: Quantitative evidence on benefit-risk tradeoffs for implantable heart-failure device profiles may facilitate incorporating patients' views during product development, regulatory decision-making, and clinical practice.

Incidence, cost, and mortality of neutropenia hospitalization associated with chemotherapy.

BACKGROUND: Neutropenia is a common side effect of chemotherapy, often requiring hospitalization for treatment of severe cases. Neutropenia hospitalization (NH) rates have been reported in individual studies, but national estimates are needed. METHODS: Chemotherapy-induced NHs were identified in the 1999 hospital discharge data bases from 7 states. Cancer and chemotherapy prevalence data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program and the National Cancer Data Base were used to calculate national NH rates for 13 cancer types. NH cost was estimated by multiplying charges by institution-specific, cost-to-charge ratios from the 1999 Centers for Medicare and Medicaid Services Hospital Cost Report. NH incidence was projected to national levels using population data from the United States Census and the Centers for Disease Control and Prevention. RESULTS: There were 20,780 discharges with documentation of cancer, chemotherapy, and neutropenia identified. Projecting to national levels, NH incidence was estimated at 60,294 cases (7.83 cases per 1000 cancer patients). The mean NH cost was 13,372 dollars. The mortality rate among patients with NH was estimated at 6.8% or 1 death for every 14 hospitalized patients. Among 13 selected cancer types, the NH rate was 34.20 cases per 1000 patients receiving chemotherapy (1 in 29 patients). NH was particularly common in patients with hematologic tumors, with an incidence of 43.3 cases per 1000 patients with such tumors (1 in 23 patients). The average NH cost for hematologic malignancies was 20,400 dollars, more than double the cost of NH for solid tumors. CONCLUSIONS: According to the current study, NH affects > 60,000 patients with cancer each year in the United States, with an average cost of 13,372 dollars per hospitalization and an associated inpatient mortality rate of 6.8%.