In long-term clinical trials we often need to monitor the patients' enrollment, compliance, and treatment effect during the study. In this paper we take the conditional power approach and consider a two-stage design based on the ideas of Li et al. (2002) for trials with survival endpoints. We make projections and decisions regarding the future course of the trial from the interim data. The decision includes possible early termination of the trial for convincing evidence of futility or efficacy, and projection includes how many additional patients are needed to enroll and how long the enrollment and follow-up may be when continuing the trial. The flexibility of the adaptive design is demonstrated by an example, the Coumadin Aspirin Reinfarction Study.

Adaptive sample size methods have been a popular topic in the field of clinical trials. There are a few basic requirements for the adaptive methods to be acceptable from the international regulatory viewpoint. All valid methods need to control the overall type-I error rate at the pre-specified level. The rule of the interim and final decisions needs to be explicit and clearly documentable. It is extremely desirable that the method employed also provides estimation of the treatment effect in addition to the significance test. In this paper we describe the point and confidence interval estimation for the likelihood approach of sample-size adaptive design proposed by Li et al. (Biostatistics 3:277-287, 2002, J. Biopharm. Stat. 15:707-718, 2005. We use the median unbiased estimator (Cox and Hinkley, Theoretical Statistics, p. 273, 1974) for estimating the treatment effect and demonstrate that the estimator has small mean squared error compared to the naïve method, and that the confidence interval estimation has correct coverage probability.

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Consistency of treatment effects across different regions in multiregional clinical trials (MRCTs) has been an important question for the regulatory authorities. Many consistency definitions are proposed in literature. One of the definitions of consistency is expressed as qualitative consistency, whereas inconsistency is defined as qualitative treatment by region interaction. This article focuses on the qualitative consistency and extends Gail-Simon and Sasabuchi's one-sided multivariate likelihood ratio tests. Simulations are used to evaluate operating characteristics of these qualitative consistency assessment approaches. For a given number of regions, the guideline for setting significance level, and consistency cut-off are explored.