過去相關學者大部分是比較依據漏斗圖來檢定是否有出版偏差的統計方法，而學者Jin et al.(2015)指出到目前為止，尚未有研究者針對選擇模型的方法進行統計模擬比較。因此，本論文針對二元反應變數的整合分析論文，比較Dear和Begg及Copas選擇模型偵測與校正出版偏差的能力，以利後續研究者選擇適當的方法來檢定與校正出版偏差。Dear和Begg及Copas選擇模型方法最大差異在於選擇權重方式不同。經模擬發現，Dear和Begg的方法在高勝算比且研究論文間的異質性高時，其方法的型I誤差率表現失當，而Copas則維持要求的名目水準，所以檢定力比較只在研究論文間的異質性為較低時，其結果為Copas方法的檢定力都高於Dear和Begg方法的檢定力。接著，欲瞭解Dear和Begg及Copas選擇模型校正出版偏差的準確性，經模擬發現，在多種情況下，Dear和Begg的估計量之偏誤與均方誤差較Copas的估計量大，故本論文建議使用Copas選擇模型來偵測與校正出版偏差。

Publication bias is a major concern in meta-analysis, since it will lead to biased research conclusion. Many statistical methods have been developed to detect and adjust publication bias. However, Jin et al. (2014) mentioned that no simulation is conducted to investigate the performance of selection methods. Therefore, this thesis conducts a simulation study to compare two selection methods, Dear and Begg (1992) and Copas selection model, with binary outcomes. According to the simulation results, Copas selection model is recommended for use in both detecting and correcting publication bias.

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