Liu Qing, Li Gang, Anderson Keaven M, Lim Pilar
Janssen Research and Development, LLC, Raritan, NJ 08869, USA.
J Biopharm Stat. 2012;22(4):617-40. doi: 10.1080/10543406.2012.678226.
Group sequential designs are rarely used for clinical trials with substantial over running due to fast enrollment or long duration of treatment and follow-up. Traditionally, such trials rely on fixed sample size designs. Recently, various two-stage adaptive designs have been introduced to allow sample size adjustment to increase statistical power or avoid unnecessarily large trials. However, these adaptive designs can be seriously inefficient. To address this infamous problem, we propose a likelihood-based two-stage adaptive design where sample size adjustment is derived from a pseudo group sequential design using cumulative conditional power. We show through numerical examples that this design cannot be improved by group sequential designs. In addition, the approach may uniformly improve any existing two-stage adaptive designs with sample size adjustment. For statistical inference, we provide methods for sequential p-values and confidence intervals, as well as median unbiased and minimum variance unbiased estimates. We show that the claim of inefficiency of adaptive designs by Tsiatis and Mehta ( 2003 ) is logically flawed, and thereby provide a strong defense of Cui et al. ( 1999 ).
由于入组速度快或治疗及随访时间长导致临床试验严重超期时，成组序贯设计很少被使用。传统上，此类试验依赖固定样本量设计。最近，各种两阶段自适应设计被引入，以允许调整样本量来提高统计效能或避免不必要的大规模试验。然而，这些自适应设计可能效率极低。为解决这个臭名昭著的问题，我们提出一种基于似然的两阶段自适应设计，其中样本量调整源自使用累积条件效能的伪成组序贯设计。我们通过数值例子表明，这种设计不能通过成组序贯设计得到改进。此外，该方法可以统一改进任何现有的带样本量调整的两阶段自适应设计。对于统计推断，我们提供了序贯p值和置信区间的方法，以及中位数无偏估计和最小方差无偏估计。我们表明，Tsiatis和Mehta（2003年）关于自适应设计效率低下的说法在逻辑上存在缺陷，从而为Cui等人（1999年）提供了有力辩护。
