Fitting Conditional Survival Models to Meta‐Analytic Data by Using a Transformation Toward Mixed‐Effects Models

DOI10.1111/J.1541-0420.2007.00960.XzbMath1146.62094OpenAlexW2054310111WikidataQ31142120 ScholiaQ31142120MaRDI QIDQ3530102

Tomasz Burzykowski, Goele Massonnet, Paul Janssen

Publication date: 15 October 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1942/8460

zbMATH Keywords

linear mixed modelsrandom treatment effectsmodel transformations

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (3)

Testing for decreasing heterogeneity in a new time-varying frailty model ⋮ Continuous covariate frailty models for censored and truncated clustered data ⋮ Correlated versus uncorrelated frailty Cox models: A comparison of different estimation procedures

Uses Software

• WWGbook

Cites Work

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• A strong representation of the product-limit estimator for left truncated and right censored data
• Strong representations of the survival function estimator for truncated and censored data with applications
• The product-limit estimator and the bootstrap: Some asymptotic representations
• Dynamic regression models for survival data.
• Modeling Multivariate Survival Data by a Semiparametric Random Effects Proportional Odds Model
• Additive hazards model with multivariate failure time data
• Analysis of Covariance with Incomplete Data Via Semiparametric Model Transformations
• Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood
• Resampling Plans for Frailty Models

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