Shelby Reed

Pain coping skills training (PCST) is efficacious in patients with cancer, but clinical access is limited. To inform implementation, as a secondary outcome, we estimated the cost-effectiveness of 8 dosing strategies of PCST evaluated in a sequential multiple assignment randomized trial among women with breast cancer and pain (N = 327). Women were randomized to initial doses and re-randomized to subsequent doses based on their initial response (ie, ≥30% pain reduction). A decision-analytic model was designed to incorporate costs and benefits associated with 8 different PCST dosing strategies. In the primary analysis, costs were limited to resources required to deliver PCST. Quality-adjusted life-years (QALYs) were modeled based on utility weights measured with the EuroQol-5 dimension 5-level at 4 assessments over 10 months. A probabilistic sensitivity analysis was performed to account for parameter uncertainty. Implementation of PCST initiated with the 5-session protocol was more costly ($693-853) than strategies initiated with the 1-session protocol ($288-496). QALYs for strategies beginning with the 5-session protocol were greater than for strategies beginning with the 1-session protocol. With the goal of implementing PCST as part of comprehensive cancer treatment and with willingness-to-pay thresholds ranging beyond $20,000 per QALY, the strategy most likely to provide the greatest number of QALYs at an acceptable cost was a 1-session PCST protocol followed by either 5 maintenance telephone calls for responders or 5 sessions of PCST for nonresponders. A PCST program with 1 initial session and subsequent dosing based on response provides good value and improved outcomes. PERSPECTIVE: This article presents the results of a cost analysis of the delivery of PCST, a nonpharmacological intervention, to women with breast cancer and pain. Results could potentially provide important cost-related information to health care providers and systems on the use of an efficacious and accessible nonmedication strategy for pain management. TRIALS REGISTRATION: ClinicalTrials.gov: NCT02791646, registered 6/2/2016.

While the quality-adjusted life-year construct has advantages of simplicity and consistency, simplicity requires strong assumptions. In particular, standard assumptions result in health-state utility functions that are unrealistically linear and separable in risk and duration. Consequently, sequencing of a series of health improvements has no effect on the total value of the sequence because each increment is assessed independently of previous increments. Utility functions in nearly all other areas of applied economics are assumed to be nonlinear with diminishing marginal utility so it matters where an improvement occurs in a sequence. We construct a conceptual framework that that demonstrates how diminishing marginal utility for health improvements could affect preferences for different sequence patterns. Using this framework, we derive conditions for which the sum of conventional health-state utilities understates, overstates, or approximates the sequence-sensitive value of health improvements. These patterns suggest the direction and magnitude of possible adjustments to conventional value calculations. We provide numerical examples and identify recent studies whose results are consistent with the conceptual model.

BACKGROUND: The statistical significance of clinical trial outcomes is generally interpreted quantitatively according to the same threshold of 2.5% (in one-sided tests) to control the false-positive rate or type I error, regardless of the burden of disease or patient preferences. The clinical significance of trial outcomes-including patient preferences-are also considered, but through qualitative means that may be challenging to reconcile with the statistical evidence. OBJECTIVE: We aimed to apply Bayesian decision analysis to heart failure device studies to choose an optimal significance threshold that maximizes the expected utility to patients across both the null and alternative hypotheses, thereby allowing clinical significance to be incorporated into statistical decisions either in the trial design stage or in the post-trial interpretation stage. In this context, utility is a measure of how much well-being the approval decision for the treatment provides to the patient. METHODS: We use the results from a discrete-choice experiment study focusing on heart failure patients' preferences, questioning respondents about their willingness to accept therapeutic risks in exchange for quantifiable benefits with alternative hypothetical medical device performance characteristics. These benefit-risk trade-off data allow us to estimate the loss in utility-from the patient perspective-of a false-positive or false-negative pivotal trial result. We compute the Bayesian decision analysis-optimal statistical significance threshold that maximizes the expected utility to heart failure patients for a hypothetical two-arm, fixed-sample, randomized controlled trial. An interactive Excel-based tool is provided that illustrates how the optimal statistical significance threshold changes as a function of patients' preferences for varying rates of false positives and false negatives, and as a function of assumed key parameters. RESULTS: In our baseline analysis, the Bayesian decision analysis-optimal significance threshold for a hypothetical two-arm randomized controlled trial with a fixed sample size of 600 patients per arm was 3.2%, with a statistical power of 83.2%. This result reflects the willingness of heart failure patients to bear additional risks of the investigational device in exchange for its probable benefits. However, for increased device-associated risks and for risk-averse subclasses of heart failure patients, Bayesian decision analysis-optimal significance thresholds may be smaller than 2.5%. CONCLUSIONS: A Bayesian decision analysis is a systematic, transparent, and repeatable process for combining clinical and statistical significance, explicitly incorporating burden of disease and patient preferences into the regulatory decision-making process.

Behavioral pain management interventions are efficacious for reducing pain in patients with cancer. However, optimal dosing of behavioral pain interventions for pain reduction is unknown, and this hinders routine clinical use. A Sequential Multiple Assignment Randomized Trial (SMART) was used to evaluate whether varying doses of Pain Coping Skills Training (PCST) and response-based dose adaptation can improve pain management in women with breast cancer. Participants (N = 327) had stage I-IIIC breast cancer and a worst pain score of >5/10. Pain severity (a priori primary outcome) was assessed before initial randomization (1:1 allocation) to PCST-Full (5 sessions) or PCST-Brief (1 session) and 5 to 8 weeks later. Responders (>30% pain reduction) were rerandomized to a maintenance dose or no dose and nonresponders (<30% pain reduction) to an increased or maintenance dose. Pain severity was assessed again 5 to 8 weeks later (assessment 3) and 6 months later (assessment 4). As hypothesized, PCST-Full resulted in greater mean percent pain reduction than PCST-Brief (M [SD] = -28.5% [39.6%] vs M [SD]= -14.8% [71.8%]; P = 0.041). At assessment 3 after second dosing, all intervention sequences evidenced pain reduction from assessment 1 with no differences between sequences. At assessment 4, all sequences evidenced pain reduction from assessment 1 with differences between sequences (P = 0.027). Participants initially receiving PCST-Full had greater pain reduction at assessment 4 (P = 0.056). Varying PCST doses led to pain reduction over time. Intervention sequences demonstrating the most durable decreases in pain reduction included PCST-Full. Pain Coping Skills Training with intervention adjustment based on response can produce sustainable pain reduction.

UNLABELLED: 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.