臨床試驗中，研究人員為確保藥品的安全性及探討人體對此藥的療效反應，只針對符合特定條件的患者進行試驗。對於試驗設計的嚴謹度以及資料分析方法的適切性必須相當要求，試驗的詳細內容皆必須試前擬定並詳載於試驗計畫書(protocol)中。然而，試驗的進行往往不如預先規畫時的順利，故為了節省試驗所需耗費的時間與金錢或安全性的考量，通常會在試驗進行一段時間後，進行試驗計畫變更 (protocol amendment)。若試驗計畫中的受試條件在試驗過程中經過變更，則試驗變更前後所蒐集到的資料可能不是來自原計畫中定義的目標母體。事實上，此時採用原本試前所規劃的統計方法進行推論，會產生偏差 (bias)，而且會無法達到試前預設的檢定力，進而誤導臨床研究方向。所以在受試條件被不斷變更的情形下，必須修正統計方法以及重新估算所需樣本數。連續型療效反應(continuous endpoint)且試驗發生變更的相關研究中，為了合併變更前後的資料以估計平均療效，Chow et al. 在2005年提出將每次變更下平均療效的先驗分布引入，以貝氏方法(Bayesian methodology)建立概似函數以求得平均療效的最大概似估計量，並重新估算所需樣本數。此外，針對受試條件變更的試驗，學者Chow 和 Shao (2005) 提出以統計模式連結反應變數與受試條件之間的關係，透過模式參數的估計，進一步估計目標母體之平均療效。本論文將學者Chow和Shao (2005) 以模式連結療效反應與受試條件的概念，以及Chow et al. (2005)所提出之貝氏方法，延伸至二元療效反應(binary endpoint)且受試條件變更的試驗中，以推論目標母體之有效反應率，並針對所提的檢定方法進行樣本數的修正。

It is not uncommon to modify trial procedures and/or statistical methods of on-going clinical trials through protocol amendments. A major modification could result in a shift in target patient population. In addition, frequent and significant modifications could lead to a totally different study, which is unable to address the medical questions that the original study intends to answer. Chow and Shao (2005) proposed a covariate-adjusted model with continuous study endpoint. Chow et al. (2005) proposed using a sensitivity index, as defined in Chow et al. (2002), to measure the impact of population shift. Under the assumption that the shift in location parameter is random and the shift in scale parameter is fixed, Chow et al. (2005) proposed a Bayesian approach for inferences of the treatment effect. In this dissertation, following similar ideas of Chow and Shao (2005) and Chow et al. (2005), statistical inference and sample size adjustment based on a binary study endpoint for trials with protocol amendments are derived.

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