本論文中，我們根據廣義線性混合模式和廣義估計方程式模式，提出兩種 群序檢定方法以分析順序型長期追蹤資料，並以模擬研究來比較此兩種分析模式之型I誤差機率和檢定力。 此外，藉用臨床實例來闡述我們所提出的檢定方法。 For ethical, economical and administrative considerations, interim analyses are often conducted to allow for possibly early termination of a clinical trial. Group sequential methods are essentially used for a correct application of interim analyses. Three common group sequential methods are proposed by Pocock (1977), O''Brien and Fleming (1979) and Lan and DeMets (1983). Those classical group sequential methods are applied for cross-sequential data as well as based on the assumption of independent increment structure (IIS) between the successive test statistics. For longitudinal data, the IIS assumption between the successive test statistics is violated due to the correlation between the measurements from the same subject. However, Scharfstein{et al}. (1997) prove that the IIS holds in parametric and semi-parametric models when efficient test statistics are employed. In the article, we propose group sequential methods based on GLMM (generalized linear mixed model) and GEE (generalized estimating equations) model for analysing ordinal longitudinal data. These two methods are compared with respect to the probability of type I error and power by simulation studies. The testing procedures are illustrated by a clinical trial for ordinal responses.
