Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach
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Publication:3549419
DOI10.1111/j.1541-0420.2007.00983.xzbMath1151.62093OpenAlexW2171182613WikidataQ33318959 ScholiaQ33318959MaRDI QIDQ3549419
Xihong Lin, Wen Ye, Jeremy M. G. Taylor
Publication date: 22 December 2008
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/2027.42/65518
Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Inference from stochastic processes (62M99) Estimation in survival analysis and censored data (62N02)
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Cites Work
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- A Risk Set Calibration Method for Failure Time Regression by Using a Covariate Reliability Sample
- The Evaluation of Multiple Surrogate Endpoints
- Covariate measurement errors and parameter estimation in a failure time regression model
- Semiparametric Stochastic Mixed Models for Longitudinal Data
- A Joint Model for Survival and Longitudinal Data Measured with Error
- Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome
- Evaluating Surrogate Markers of Clinical Outcome When Measured with Error
- Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS
- Bias correction in generalised linear mixed models with a single component of dispersion
- Measurement Error in Nonlinear Models
- The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure
- A Flexible B‐Spline Model for Multiple Longitudinal Biomarkers and Survival