|Title:||Estimation for the optimal combination of markers without modeling the censoring distribution||Authors:||Chiang, C.-T.
|Keywords:||AUC; Consistency; Estimating equations; GLM; IPW; Markers; Optimal composite marker; ROC curve; Selection probability; Survival data; Vital status||Issue Date:||2009||Journal Volume:||65||Journal Issue:||1||Start page/Pages:||152-158||Source:||Biometrics||Abstract:||
In the time-dependent receiver operating characteristic curve analysis with several baseline markers, research interest focuses on seeking appropriate composite markers to enhance the accuracy in predicting the vital status of individuals over time. Based on censored survival data, we proposed a more flexible estimation procedure for the optimal combination of markers under the validity of a time-varying coefficient generalized linear model for the event time without restrictive assumptions on the censoring pattern. The consistency of the proposed estimators is also established in this article. In contrast, the inverse probability weighting (IPW) approach might introduce a bias when the selection probabilities are misspecified in the estimating equations. The performance of both estimation procedures are examined and compared through a class of simulations. It is found from the simulation study that the proposed estimators are far superior to the IPW ones. Applying these methods to an angiography cohort, our estimation procedure is shown to be useful in predicting the time to all-cause and coronary artery disease related death. ? 2008, The International Biometric Society.
|DOI:||10.1111/j.1541-0420.2007.01040.x||SDG/Keyword:||amyloid A protein; C reactive protein; homocysteine; interleukin 6; data interpretation; disease; mortality; numerical method; numerical model; probability; simulation; survival; angiography; article; cause of death; censorship; cohort analysis; coronary artery disease; human; mathematical analysis; mathematical model; performance; predictor variable; probability; protein blood level; receiver operating characteristic; simulation; statistical model; survival; validity; vital statistics; Biological Markers; Biometry; Cause of Death; Computer Simulation; Coronary Artery Disease; Data Interpretation, Statistical; Humans; Probability; Survival Analysis; Time
|Appears in Collections:||應用數學科學研究所|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.