A Bayesian approach to pilot-pivotal trials for bioequivalence assessment.

BACKGROUND: To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. METHODS: We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. RESULTS: Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. CONCLUSIONS: The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size.

The Influence of Diagnosis Intervention Packet Policy Intervention on Medication Structure and Drug Cost of Elderly Hypertensive Inpatients in China: A Multicenter Interrupted Time-Series Analysis.

Background: DIP is a new medical insurance payment system developed in China which was implemented in Guangzhou in January 2018, but few studies have focused on its intervention effect on the drug burden of elderly hypertensive patients. Methods: Nine medical institutions in Guangzhou, China, were selected, among which, daily full medical orders of elderly hypertensive inpatients from 2016 to 2020 were randomly collected. To assess the impact of DIP policy intervention on patient drug burden, we took the data after policy implementation in January 2018, as the intervention data, and applied a segmented regression model with interrupted time series to analyze the trend and changes in average daily drug costs per month and medication structure, stratified by age, sex, and inpatient department. Results: A total of 34,276 elderly hypertensive patients' daily full medical orders were obtained. The immediate level change of drug costs after intervention was -23.884 RMB/month (P = 0.652), and the trend change was statistically significant (-15.642 RMB/month, P = 0.002). The relative cumulative effect at the end of the study was -78.860% (95% CI: -86.087% to -69.076%), and the intervention effect was more significant in surgical and male patients. The analysis of drug structure changes showed that after the implementation of the DIP policy intervention, the proportion of anti-infective drugs, anti-tumor drugs, and biological products all showed a significant downward trend (P < 0.05), while nutritional drugs showed a significant upward trend (P = 0.011), but no immediate horizontal change in slope was observed. Conclusion: The typical practice in China showed that DIP policy intervention can improve the drug burden of elderly hypertensive hospitalized patients and has a stable long-term effect, and the intervention effect is not consistent across different clinical department and populations with different characteristics, and it would also cause changes in the medication structure.