ArticleThe AAPS journal2015
Performance of nonlinear mixed effects models in the presence of informative dropout.
Article in The AAPS journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy.CPT: pharmacometrics & systems pharmacology · 2021Trial
- Application of Item Response Theory to Model Disease Progression and Agomelatine Effect in Patients with Major Depressive Disorder.The AAPS journal · 2019Trial
- Extrapolating Vincristine-Induced Peripheral Neuropathy From Caucasian to Kenyan Populations: Impact of Type I and Type II Selection Bias.CPT: pharmacometrics & systems pharmacology · 2026Article
- Derivation and Clinical Utility of Safety Targets for Linezolid-Related Adverse Events in Drug-Resistant Tuberculosis Treatment.Pharmaceuticals (Basel, Switzerland) · 2023Article
- A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients.Journal of pharmacokinetics and pharmacodynamics · 2021Article
- Bayesian Individual Dynamic Predictions with Uncertainty of Longitudinal Biomarkers and Risks of Survival Events in a Joint Modelling Framework: a Comparison Between Stan, Monolix, and NONMEM.The AAPS journal · 2020Article
- Demonstrating Contribution of Components of Fixed-Dose Drug Combinations Through Longitudinal Exposure-Response Analysis.The AAPS journal · 2020Article
- A review of disease progression models of Parkinson's disease and applications in clinical trials.Movement disorders : official journal of the Movement Disorder Society · 2016Review
- Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals.Cancer chemotherapy and pharmacology · 2016Article
- Establishing Good Practices for Exposure-Response Analysis of Clinical Endpoints in Drug Development.CPT: pharmacometrics & systems pharmacology · 2015Article
- Nonlinear Mixed-effect Models for Prostate-specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: A Comparison by Simulation of Two-stage and Joint Approaches.The AAPS journal · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Informative dropout can lead to bias in statistical analyses if not handled appropriately. The objective of this simulation study was to investigate the performance of nonlinear mixed effects models with regard to bias and precision, with and without handling informative dropout. An efficacy variable and dropout depending on that efficacy variable were simulated and model parameters were reestimated, with or without including a dropout model. The Laplace and FOCE-I estimation methods in NONMEM 7, and the stochastic simulations and estimations (SSE) functionality in PsN, were used in the analysis. For the base scenario, bias was low, less than 5% for all fixed effects parameters, when a dropout model was used in the estimations. When a dropout model was not included, bias increased up to 8% for the Laplace method and up to 21% if the FOCE-I estimation method was applied. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate, but was relatively unaffected by the number of subjects in the study. This study illustrates that ignoring informative dropout can lead to biased parameters in nonlinear mixed effects modeling, but even in cases with few observations or high dropout rate, the bias is relatively low and only translates into small effects on predictions of the underlying effect variable. A dropout model is, however, crucial in the presence of informative dropout in order to make realistic simulations of trial outcomes.
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