Cox regression with dependent error in covariates
نویسندگان
چکیده
منابع مشابه
Time-dependent covariates in the Cox proportional-hazards regression model.
The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to-event data with censoring and covariates. The covariates may change their values over time. This article discusses the use of such time-dependent covariates, which offer additional opportunities but must be used with caution. The interrelationships between the outcome and variable over time can ...
متن کاملCox regression in nested case-control studies with auxiliary covariates.
Nested case-control (NCC) design is a popular sampling method in large epidemiological studies for its cost effectiveness to investigate the temporal relationship of diseases with environmental exposures or biological precursors. Thomas' maximum partial likelihood estimator is commonly used to estimate the regression parameters in Cox's model for NCC data. In this article, we consider a situati...
متن کاملCox Regression for Current Status Data with Missing Covariates
Statistical inference based on the right-censored data for proportional hazard (PH) model with missing covariates has received considerable attention, but interval-censored or current status data with missing covariates are not yet investigated. Our study is partly motivated by analysis of fracture data from a cross-sectional study, where the ocurrence time of fracture was interval-censored and...
متن کاملUnderstanding the Cox Regression Models with Time-Change Covariates
The Cox regression model is a cornerstone of modern survival analysis and is widely used in many other fields as well. But the Cox models with time-change covariates are not easy to understand or visualize. We therefore offer a simple and easy-to-understand interpretation of the (arbitrary) baseline hazard and time-change covariate. This interpretation also provides a way to simulate variables ...
متن کاملCox Regression Models with Functional Covariates for Survival Data.
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criter...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrics
سال: 2017
ISSN: 0006-341X
DOI: 10.1111/biom.12741