Journal of biopharmaceutical statistics
-
This work focuses on the modification of two classical phase II trials designs, the A'Hern design, a single-arm single-stage design, and the Sargent and Goldberg design introduced in the context of flexible screening designs. In the first part of the paper, we have proposed a drift-adjusted A'Hern design, a hybrid design combining the A'Hern design and the Sargent and Goldberg design. Indeed, classical single-arm phase II designs such as the A'Hern design are still widely used in oncology. ⋯ The latter, introduced in recent years by Yap et al. and Wu et al., extended the Sargent and Goldberg design to include a comparison to a historical control. However, their sample size computations may have potential weaknesses, which motivated us to revisit the existing approaches. A detailed simulation study has been carried out to evaluate the operating characteristics of the drift-adjusted A'Hern design and the different sample size strategies of the screened selection designs.
-
The traditional rule-based design, 3 + 3, has been shown to be less likely to achieve the objectives of dose-finding trials when compared with model-based designs. We propose a new rule-based design called i3 + 3, which is based on simple but more advanced rules that account for the variabilities in the observed data. ⋯ The i3 + 3 design is far superior than the 3 + 3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3 + 3 also demonstrates comparable performances.
-
In this article, we propose and evaluate three alternative randomization strategies to the adaptive randomization (AR) stage used in a seamless Phase I/II dose-finding design. The original design was proposed by Wages and Tait in 2015 for trials of molecularly targeted agents in cancer treatments, where dose-efficacy assumptions are not always monotonically increasing. Our goal is to improve the design's overall performance regarding the estimation of optimal dose as well as patient allocation to effective treatments. ⋯ Unlike the original method, our proposed adaption does not require an arbitrarily specified sample size for the adaptive randomization stage. Simulations are used to compare the proposed strategies and a final strategy is recommended. Under most scenarios, our recommended method allocates more patients to the optimal dose while improving accuracy in selecting the final optimal dose without increasing the overall risk of toxicity.
-
Comparative Study
Good statistical practice in utilizing real-world data in a comparative study for premarket evaluation of medical devices.
Observational (non-randomized) comparative studies have been adopted in the pre-market safety/effectiveness evaluation of medical devices. There has been an increased interest in utilizing this design with the growing available real-world data. ⋯ In this paper, challenges and opportunities are discussed from the regulatory perspective. Considerations and good statistical practice to mitigate the potential bias are presented.