To shorten the time for drug development and regulatory approval, a growing number of clinical trials are being conducted in multiple regions simultaneously. One of the challenges to multi-regional clinical trials (MRCT) is how to utilize the data obtained from other regions within the entire trial to help make local approval decisions. In addition to the global efficacy, the evidence of consistency in treatment effects between the local region and the entire trial is usually required for regional approval. In recent years, a number of statistical models and consistency criteria have been proposed. The sample size requirement for the region of interest was also studied. However, there is no specific regional requirement being broadly accepted; sample size planning considering regional requirement of all regions of interest is not well developed; how to apply the adaptive design to MRCT has not been studied. In this dissertation, we have made a number of contributions. First, we propose a unified regional requirement for the consistency assessment of MRCT, which generalizes the requirements proposed by Ko et al. (2010), Chen et al. (2012) and Tsong et al. (2012), make recommendations for choosing the value of parameters defining the proposed requirement, and determine the sample size increase needed to preserve power. Second, we propose two optimal designs for MRCT: minimal total sample size design and maximal utility design, which will provide more effective sample size allocation to ensure certain overall power and assurance probabilities of all interested regions. We also introduce the factors which should be considered in designing MRCT and analyze how each factor affects sample size planning. Third, we propose an unblinded region-level adaptive design to perform sample size re-estimation and re-allocation at interim based on the observed values of each region. We can determine not only whether to stop the whole MRCT based on the conditional power, but also whether to stop any individual region based on the conditional success rate at interim. The simulation results support that the proposed adaptive design has better performance than the classical design in terms of overall power and success rate of each region.