Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson's disease.

A fixed one-sided significance level of 5% is commonly used to interpret the statistical significance of randomized clinical trial (RCT) outcomes. While it is necessary to reduce the false positive rate, the threshold used could be chosen quantitatively and transparently to specifically reflect patient preferences regarding benefit-risk tradeoffs as well as other considerations. How can patient preferences be explicitly incorporated into RCTs in Parkinson's disease (PD), and what is the impact on statistical thresholds for device approval? In this analysis, we apply Bayesian decision analysis (BDA) to PD patient preference scores elicited from survey data. BDA allows us to choose a sample size (n) and significance level (α) that maximizes the overall expected value to patients of a balanced two-arm fixed-sample RCT, where the expected value is computed under both null and alternative hypotheses. For PD patients who had previously received deep brain stimulation (DBS) treatment, the BDA-optimal significance levels fell between 4.0% and 10.0%, similar to or greater than the traditional value of 5%. Conversely, for patients who had never received DBS, the optimal significance level ranged from 0.2% to 4.4%. In both of these populations, the optimal significance level increased with the severity of the patients' cognitive and motor function symptoms. By explicitly incorporating patient preferences into clinical trial designs and the regulatory decision-making process, BDA provides a quantitative and transparent approach to combine clinical and statistical significance. For PD patients who have never received DBS treatment, a 5% significance threshold may not be conservative enough to reflect their risk-aversion level. However, this study shows that patients who previously received DBS treatment present a higher tolerance to accept therapeutic risks in exchange for improved efficacy which is reflected in a higher statistical threshold.

Introduction: US dermatologic health care needs assessment.

The health care needs assessment (HCNA) addressed in this issue of Dermatologic Clinics is designed to aid practitioners and policy makers by providing current, evidence-based research that can be used to guide United States' dermatologic care. The topics covered in this skin disease HCNA include those that are considered common dermatology care needs in society and those severe enough to create a burden on the medical system. Disease discussions address epidemiology, costs to society and patients, prevention, treatment, gaps in management, and future recommendations.

Conclusions and recommendations: United States dermatologic health care need assessment.

This United States skin disease health care needs assessment (HCNA) focuses on the most common and severe skin conditions. The purpose of this article is to highlight these skin conditions in a concise manner for efficient use by policy makers. Brief summaries of each article in this issue of Dermatologic Clinics are provided along with recommendations for better addressing dermatologic care needs.