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

The logistic transform for bounded outcome scores.

Emmanuel Lesaffre1, Dimitris Rizopoulos, Roula Tsonaka

  • 1Biostatistical Centre, Catholic University of Leuven, U.Z. St. Rafaël, Kapucijnenvoer 35, B-3000 Leuven, Belgium. emmanuel.lesaffre@med.kuleuven.be

Biostatistics (Oxford, England)
|April 7, 2006
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

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same author

Creatine/Creatinine Ratio and Myostatin as Biomarkers to Monitor Muscle Function in Duchenne Muscular Dystrophy Patients.

Journal of cachexia, sarcopenia and muscle·2026
Same author

Quantitative tandem mass tag-based serum proteomics for longitudinal biomarker monitoring in Duchenne muscular dystrophy.

Clinical proteomics·2026
Same author

MDBiomarkers: A queryable biomarkers database integrating multiple serum and tissue datasets for Duchenne muscular dystrophy.

Journal of neuromuscular diseases·2026
Same author

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same author

A comparative analysis of corticosteroids and exclusive enteral nutrition induction therapy in children with small bowel Crohn's disease: results of two prospective cohorts.

European journal of pediatrics·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

This study introduces a logistic transformation method for analyzing bounded outcome scores, crucial for clinical trials. The approach, using a logit-normal distribution, offers a robust alternative to traditional methods for non-standard score distributions.

Area of Science:

  • Biostatistics
  • Statistical modeling
  • Clinical trial methodology

Background:

  • Bounded outcome scores (0,1) often exhibit non-standard distributions (J- or U-shaped), limiting classical parametric analysis.
  • The logistic transformation, applied to a normal variable, yields a logit-normal (LN) distribution, capable of diverse shapes on (0,1).
  • Bounded outcomes are prevalent in quality-of-life, drug compliance, and pain studies, frequently including boundary values.

Purpose of the Study:

  • To present and evaluate a statistical method using the logistic transformation for analyzing bounded outcome scores in comparative clinical trials.
  • To extend the logit-normal model to accommodate baseline covariates and allow for treatment-dependent variance.
  • To assess the performance of the proposed method against existing statistical tests and regression models.

Related Experiment Videos

Main Methods:

  • Application of the logistic transformation to model bounded outcome scores, considering two cases: proportions with LN distribution and coarsened latent scores with LN distribution.
  • Extension of the logit-normal model to incorporate baseline covariates and allow for treatment-dependent variance.
  • A simulation study comparing the proposed method with the two-sample Wilcoxon test and ordinal probit regression.

Main Results:

  • The logistic transformation and logit-normal distribution provide a flexible framework for analyzing bounded outcomes.
  • Ignoring unequal variances in the logit-normal model can lead to significantly biased parameter estimates, highlighting the importance of distinguishing variance cases.
  • The proposed method demonstrates competitive performance against the Wilcoxon test and ordinal probit regression in simulations.

Conclusions:

  • The logistic transformation offers a valuable and adaptable statistical approach for analyzing bounded outcome scores in clinical research.
  • Accurate modeling of variance, particularly when unequal between treatment groups, is critical for reliable parameter estimation.
  • The method shows promise for application in comparative clinical trials, providing a robust alternative for handling non-standard bounded data.