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

A joint model for nonlinear longitudinal data with informative dropout.

Chuanpu Hu1, Mark E Sale

  • 1GlaxoSmithKline, P.O. Box 13398, Five Moore Drive, Research Triangle Park, NC 27709, USA. chuanpu.hu@gsk.com

Journal of Pharmacokinetics and Pharmacodynamics
|June 13, 2003
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

Causal Directed Acyclic Graphs to Mitigate Confounding Bias in Exposure-Response Analyses.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Latent Variable Indirect Response Modeling of Cendakimab Exposure-Response for Longitudinal Dysphagia Days Using a Combined Uniform-Binomial Likelihood Framework.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Integrating MBMA and QSP to Identify Key Covariates and Predict Treatment Outcomes in Relapsed/Refractory Multiple Myeloma.

CPT: pharmacometrics & systems pharmacology·2025
Same author

Novel endpoints based on tumor size ratio to support early clinical decision-making in oncology drug-development.

Journal of pharmacokinetics and pharmacodynamics·2024
Same author

Visual predictive check of longitudinal models and dropout.

Journal of pharmacokinetics and pharmacodynamics·2024
Same author

Likelihood comparisons in bounded outcome score analysis must be internally consistent.

Journal of pharmacokinetics and pharmacodynamics·2024
Same journal

Cohort population analysis of sparse data: Dexamethasone pharmacokinetics in mother and fetus based on blood sampling at birth.

Journal of pharmacokinetics and pharmacodynamics·2026
Same journal

Bridging the operational gap in population pharmacokinetic-pharmacodynamic analysis: an international perspective on the 2025 Chinese group standard.

Journal of pharmacokinetics and pharmacodynamics·2026
Same journal

Advancing quantitative clinical pharmacology competencies in Francophone Africa through an on-line learning framework.

Journal of pharmacokinetics and pharmacodynamics·2026
Same journal

Optimizing Subcutaneous Antibody Dosing Regimens Through Operating Space Maps: rHuPH20 Case Study.

Journal of pharmacokinetics and pharmacodynamics·2026
Same journal

Mechanistic modeling of FcRn-dependent IgG drug interactions: Clinical applications and dosing implications.

Journal of pharmacokinetics and pharmacodynamics·2026
Same journal

Comparing heavy-tailed residual error models for outlier handling in population PK modeling.

Journal of pharmacokinetics and pharmacodynamics·2026
See all related articles

Subject withdrawal, or informative dropout, in clinical trials can bias drug efficacy estimates. This study models informative dropout in nonlinear longitudinal data, improving prediction accuracy.

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Pharmacometrics

Background:

  • Subject withdrawal (dropout) is frequent in late-phase clinical trials.
  • Common methods like last observation carried forward (LOCF) can bias efficacy estimates.
  • Informative dropout, where withdrawal correlates with unobserved data, requires careful modeling.

Purpose of the Study:

  • To extend informative dropout modeling to nonlinear longitudinal data.
  • To assess the impact of dropout models on joint model predictions.
  • To provide a more accurate approach for analyzing clinical trial data with dropouts.

Main Methods:

  • Parametric modeling of both dropout hazard and longitudinal data.
  • Estimation of parameters via maximizing approximate joint likelihood.

Related Experiment Videos

  • Implementation using NONMEM software.
  • Validation using data from actual clinical trials.

    • Main Results:

    • The study demonstrates extending informative dropout modeling to nonlinear models.
    • Exploration of the impact of dropout models on predicting longitudinal data patterns.
    • Highlighting the potential for biased estimates with traditional methods like LOCF.

    Conclusions:

    • Informative dropout significantly impacts longitudinal data analysis in nonlinear models.
    • Joint modeling of dropout and longitudinal data provides more accurate predictions.
    • This approach enhances the reliability of clinical trial outcome assessments.