在醫學研究領域裡，長期性資料分析已經是一種經常被採用的分析方式。處理這種長期性資料的方法中，邊際效應的GEE方法以及混合效應模型經常用來探討引起疾病的可能危險因子與疾病風險間的相關性。但是，相關的模型診斷方法卻還沒有被正式地探討，也許不存在單一方法可以適用於所有長期性資料之模型診斷問題。主要的可能因素是個體內相依資料之差異以及重複測量次數之差異性很大。在長期性資料分析的模型診斷問題裡，單一指標（例如，連串檢定）是不可能解決所有模型診斷問題的。因此應用多重檢驗指標來描述其變異有其必要性。我們提出八種檢定方法來檢驗序列之隨機性並輔以傳統的殘差圖以及非傳統的個別殘差圖，以彌補長期性資料分析之模型診斷的相關問題。並且，我們將這些方法應用在台灣的四個臨床試驗研究。 Longitudinal study has become one of the most commonly adopted designs in medical research. The generalized estimating equations (GEE) method and/or mixed effects models are employed very often in causal inferences. The related model diagnostic procedures are not yet fully formalized, and perhaps never will be. The potential causes of major problems are the high variety of the dependence within subjects and/or the number of repeated measurements. A single testing procedure, e.g. run test, is not possible to resolve all model diagnostics problems in longitudinal data analysis. Multiple quantitative indexes for model diagnostics are needed to take into account this variety. We propose eight testing procedures for randomness accompanied with some conventional and/or non-conventional plots to remedy model diagnostics in longitudinal data analysis. The proposed issue in this thesis is well illustrated with four clinical studies in Taiwan.
