本文主要是在病例－對照研究下，利用貝氏分析的方法，討論危險因子（包括和基因相關之“致病基因＂及非基因相關之“環境因子＂）及疾病的關係。我們欲利用和目前研究有高度相似性的歷史資料，當為我們的事前分配的訊息，引入power prior 當為事前分配，它可以將歷史資料的訊息納入。
首先，討論在族群符合Hardy-Weinberg Equilibrium (HWE)及條件獨立的假設下，研究：1.單一基因座及疾病之關係； 2 .單一基因座、環境因子和疾病之關係。以上兩情況下，在病例－對照研究的資料及假設條件下，如何建構模型，以得到概似函數？另外，如何引入power prior 當為事前分配？如此則能進行貝氏分析。此為我們討論的目標之一。
再者，在一般化的情況下（不須在HWE 及條件獨立的假設），當危險因子（離散型變數或連續型變數離散化）的個數或類別很多時，則參數個數亦會增加，在估計上會遇到難以估計參數的問題；如何將我們沒有興趣的干擾參數消除，以簡化參數的個數，使得在貝氏分析的估計上能更有效率。
以上兩的主題皆利用power prior，可視為歷史資料的概似函數，不但包含了歷史資料的訊息且也保留了資料的模型結構，在使用上是十分簡便的。

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