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Analysis of Current Status Data with Missing Covariates
Author(s) -
Wen ChiChung,
Lin ChienTai
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01505.x
Subject(s) - covariate , missing data , statistics , proportional hazards model , inference , confidence interval , econometrics , survival analysis , computer science , data mining , mathematics , artificial intelligence
Summary Statistical inference based on right‐censored data for the proportional hazards (PH) model with missing covariates has received considerable attention, but interval‐censored or current status data with missing covariates has not yet been investigated. Our study is partly motivated by the analysis of fracture data from the 2005 National Health Interview Survey Original Database in Taiwan, where the occurrence of fractures was interval censored and the covariate osteoporosis was not reported for all residents. We assume that the data are realized from a PH model. A semiparametric maximum likelihood estimate implemented by a hybrid algorithm is proposed to analyze current status data with missing covariates. A comparison of the performance of our method with full‐cohort analysis, complete‐case analysis, and surrogate analysis is made via simulation with moderate sample sizes. The fracture data are then analyzed.