在醫學研究中，常使用二元診斷法來估計正概似比(positive likelihood ratio)與負概似比(negative likelihood ratio)，當遇到疾病訊息缺失時，要估計正概似比與負概似比往往相對困難些，本文針對疾病訊息是否有缺失時提出了概似函數方法。在獨立的伯努利概似函數推論下，可得到分數檢定、華德檢定與概似檢定。本文利用模擬與實例分析分別比較概似函數方法、Montero-Alonso與Roldan-Nofuentes (2018) 提出的華德檢定、Simel et al. (1991) 納皮爾概似比檢定與Walter (1975) 概似比檢定的型一誤差的機率、信賴區間上下界與覆蓋率，可發現本文的模型在解決疾病訊息缺失問題中更具優勢。;In medical research, the classic parameters use to assess the accuracy of a binary diagnostic test are the positive likelihood ratio and the negative likelihood ratio.The presence of missing data, When the disease information is missing, the positive likelihood ratio and the negative likelihood ratio are difficult to estimation.The article considers the disease information is missing or not, proposd the likelihood function method. The article use independent binary likelihood function, inferenced score test, wald test and likelihood ratios test statistics.We use simulation and real data analysis to demonstrate the merit of our new parametric robust technique. We also make comparison with to the wald test proposed by Montero-Alonso and Roldan-Nofuentes (2018), the Napierian likelihood test statistics proposed by Simel et al. (1991) and the likelihood test statistics proposed by Walter (1975).
