Using a Box–Cox Transformation in the Analysis of Longitudinal Data with Incomplete Responses

DOI10.1111/1467-9876.00192zbMath0944.62106OpenAlexW2054461097MaRDI QIDQ4485119

Joseph G. Ibrahim, Geert Molenberghs, Stuart R. Lipsitz

Publication date: 26 September 2000

Published in: Journal of the Royal Statistical Society Series C: Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9876.00192

zbMATH Keywords

sensitivity analysisincomplete datamaximum likelihoodinfluence graphCD4 cell counts

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (11)

APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001 ⋮ Statistical simulation based on right skewed distributions ⋮ Missing data methods in longitudinal studies: a review ⋮ Modeling healthcare costs in simultaneous presence of asymmetry, heteroscedasticity and correlation ⋮ Using normal quantile plot to select an appropriate transformation to achieve normality ⋮ Semiparametric transformation models with Bayesian P-splines ⋮ Nonparametric estimation for time-varying transformation models with longitudinal data ⋮ Testing transformations for the linear mixed model ⋮ Analysis of multivariate non-Gaussian functional data: a semiparametric latent process approach ⋮ Nonparametric Estimation of Conditional Distributions and Rank-Tracking Probabilities With Time-Varying Transformation Models in Longitudinal Studies ⋮ A Methodology to Implement Box-Cox Transformation When No Covariate is Available

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