Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Estimating data transformations in nonlinear mixed effects models.

A Oberg1, M Davidian

  • 1Mayo Clinic, Section of Biostatistics, Rochester, Minnesota 55905, USA. ann@mayo.edu

Biometrics
|April 28, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia.

Annals of oncology : official journal of the European Society for Medical Oncology·2019
Same author

A Placebo-Controlled, Prospective, Randomized Clinical Trial of Polyethylene Glycol and Methylprednisolone Sodium Succinate in Dogs with Intervertebral Disk Herniation.

Journal of veterinary internal medicine·2015
Same author

Cardiac function after hematopoietic cell transplantation: an echocardiographic cross-sectional study in young adults treated in childhood.

Pediatric blood & cancer·2014
Same author

The intratumoural subsite and relation of CD8(+) and FOXP3(+) T lymphocytes in colorectal cancer provide important prognostic clues.

British journal of cancer·2014
Same author

The prognostic role of KRAS, BRAF, PIK3CA and PTEN in colorectal cancer.

British journal of cancer·2013
Same author

Lymph node tissue kallikrein-related peptidase 6 mRNA: a progression marker for colorectal cancer.

British journal of cancer·2012
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a new statistical method for analyzing repeated measurement data. It allows for data transformations to be estimated directly from the data, improving the accuracy of mixed effects models.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Repeated measurement data analysis often uses mixed effects models on transformed scales.
  • Transformations are typically chosen subjectively (e.g., log transformation), potentially leading to suboptimal model fit.
  • Achieving within-individual normality and constant variance is a common goal in data transformation.

Purpose of the Study:

  • To propose a novel mixed effects framework utilizing the transform-both-sides model.
  • To estimate the data transformation parametrically from the observed data.
  • To provide a practical fitting strategy and address inferential challenges.

Main Methods:

  • Developed a mixed effects framework based on the transform-both-sides model with a monotone parametric transformation.

Related Experiment Videos

  • Employed an approximation of the marginal likelihood for model fitting.
  • Investigated the impact of transformation estimation on standard errors for fixed effects.

    • Main Results:

    • The proposed method estimates the transformation directly from the data.
    • A practical fitting strategy using marginal likelihood approximation is described.
    • Asymptotic analysis shows standard error modifications are often negligible in common applications.

    Conclusions:

    • The proposed framework offers a data-driven approach to transformation selection in mixed effects models.
    • It allows for more accurate modeling of pharmacokinetic, growth, and other repeated measures data.
    • The method is implementable using standard statistical software, enhancing its practical utility.