接受者特徵曲線為現存發展完善的統計工具，用來評估生物指標區別疾病與非疾病之能力。近來相關研究已逐漸重視時間相依接受者特徵曲線，主要是為了增加估計效率及做動態預測。為了連結存活模型，文獻中已發展出以固定共變數的Cox模型為基礎的時間相依接受者特徵曲線，不僅可用來做動態預測且平均曲線下面積(被證明為一致性測度)可用來評估模型預測精準度。然而,Cox模型需要比例風險的假設，這在一些生物醫學的研究並無法成立。因此本研究計畫將嘗試建立以加速失敗模型為基礎的時間相依接受者特徵曲線。此外並將固定共變數推廣到時間相依共變數。藉由獲取Cox模型及加速失敗模型的一致性測度，我們可以較高的預測精準度做為模型選擇的依據。而當時間相依共變數可能包含測量誤差或是無法取得完整共變數歷史時,本研究亦嘗試以聯合模型法以補足觀察不到的共變數值。最後本研究計畫將以模擬研究及HIV世代研究來評估所要發展的統計方法。 ;The ROC (receiver operating characteristic) curve methodology is currently a well-developed statistical tool to evaluate the ability of biomarkers to discriminate the case (disease) and control (non-disease) of patients. Recent research has been focused on incorporating time-dependency to ROC framework to gain efficiency and to do dynamic prediction. To connect with survival model, the time-dependent ROC curves for the Cox model with fixed covariates has been derived not only for dynamic prediction but also for the model evaluation based on the average time-dependent AUC (the area under the ROC curves), which is proven to be a concordance summary measure for predictive accuracy. However, the Cox regression model needs a proportional hazards assumption which may fail in some of the medical studies. In such situation, we develop an approach to replace the Cox model by the accelerated failure time (AFT) model to derive time-dependent sensitivity and specificity. Moreover, we will further extend the developing approach to the Cox model or the AFT model with longitudinal covariates. Since the average time-dependent AUC is a concordance measure, for prediction purpose, we may select a better model between the Cox or the AFT model by choosing a model with higher predictive accuracy. When the longitudinal covariates are subject to measurement errors or do not have complete covariate history, an imputation method through joint model is used to correct the bias of estimates. Simulation studies will be conducted to evaluate the performance of proposed approach. A case study of Taiwanese HIV cohort data will be used to illustrate the usefulness of the proposed model-base time-dependent AUC and predictive accuracy.
