Two-stage estimation on adjustment for cross-over in oncology trials / 中国临床药理学与治疗学

AIM: To investigate the application of two-stage estimation (TSE) on adjustment for treatment switch in oncology trials. METHODS: The theory and implementation of TSE method was described, and was applied to adjust the data from a two-arm randomized controlled trial of anti-tumor drugs. The changes of survival curves and hazard ratio of two groups after adjustment for cross-over were evaluated. In addition, the results of two-stage estimation and rank preserving structural failure time model (RPSFT) were compared. RESULTS: After adjustment for cross-over using TSE methods, the results showed that the median survival time of control group was shorter than the original one, and the hazard ratio was lower than the observed value. Moreover, TSE method showed similar results to rank preserving structural failure time model. CONCLUSION: The TSE method is relatively simple to use, reliable and has a good practice property in cross-over analysis of oncology trials. At the same time, it is necessary to pay attention to its application scopes.

A novel model-assisted design in phase I clinical trials: Bayesian optimal interval design / 中国临床药理学与治疗学

AIM: To introduce a novel and flexible model-assisted design for Phase I clinical trials: Bayesian optimal interval (BOIN) design, including the process of implementation, practical implementation, and evaluation of its performance. METHODS: BOIN design decides dose escalation/de-escalation by comparing the observed toxicity rate at the current dose with an escalation boundary and a de-escalation boundary that are optimized to minimize the probability of making incorrect decision of dose assignment. The application of the BOIN design is illustrated using a trial example. RESULTS: BOIN combines the advantages of the algorithm-based methods and model-based methods. It enjoys desirable statistical properties -it is optimal, safe, robust and easy to implement. Simulation study shows that the BOIN substantially outperforms the existing designs with higher accuracy to identify the maximum tolerated dose (MTD). CONCLUSION: BOIN design possesses the similar statistical performance to the much more complicated model-based designs. It is simple to implement, and easy to calibrate to meet the safety requirement mandated by regulatory agents. The BOIN design has been widely used in different types of cancers. It is a novel design that holds great potential to substantially improve phase I trials in China.