病例對照研究常被用來探索罕見疾病與潛在風險因子之間的關係。當干擾因子很難明確量化時，可採用配對設計控制干擾因子的影響，但是會伴隨過多特定配對層的截距. Breslow & Day (1980) 使用條件概似函數以解決干擾參數過多的問題。本文旨在介紹Chen & Wang (2010) 所提出的適合度檢定。該檢定統計量是由共變量二階動差的條件最大概似估計量和無母數最大概似估計量的差建構而來。最後，我們透過模擬研究與兩組實例分析評估該統計量在實務上的可行性。 Case-control studies are often used to explore the relationship between the rare disease and potential risk factors. When the confounding factors are difficult to be quantified, matching designs are used to control the confounding effects. But this results in highly stratum-specific intercepts. Breslow & Day (1980) adopt the conditional approach to eliminate the intercepts. In this paper, we introduce a new goodness-of-fit test which is proposed by Chen & Wang (2010). This test statistic is constructed by the difference between the estimates of the second moment of covariate which estimated by the conditional m.l.e. and nonparametric m.l.e., respectively. We assess the performance of the proposed method through simulation studies, and analyze two real datasets for illustration.