ArticleJournal of pharmacokinetics and pharmacodynamics2019
Operating characteristics of stepwise covariate selection in pharmacometric modeling.
Article in Journal of pharmacokinetics and pharmacodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.CPT: pharmacometrics & systems pharmacology · 2025Pooled it
- Optimization of Cefotaxime Treatment in Intensive Care Patients: Impact of Inter- and Intra-patient Variability on Pharmacokinetics and Target Attainment.Clinical pharmacokinetics · 2026Article
- Exposure-Response Analyses of Datopotamab Deruxtecan (Dato-DXd) in Patients with Hormone Receptor (HR) Positive, Human Epidermal Growth Factor Receptor 2 (HER2)-Negative Breast Cancer.Journal of clinical pharmacology · 2026Article
- Assessing Covariate Clinical Relevance in High-Dimensional PK Analysis: A Comparison of SCM+, FFEM, and FREM Approaches.CPT: pharmacometrics & systems pharmacology · 2026Article
- The Regularized Horseshoe for Covariate Selection Improves Convenience and Predictive Performance in Population PK/PD Models.CPT: pharmacometrics & systems pharmacology · 2026Article
- Conditional Versus Unconditional Covariate Effects in Pharmacometric Models: Implications for Interpretation, Communication, and Reporting.CPT: pharmacometrics & systems pharmacology · 2026Article
- Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches - further results based on more conventional practices.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Machine Learning Prediction and Validation of Plasma Concentration-Time Profiles.Molecular pharmaceutics · 2025Article
- Therapeutic Drug Monitoring-Based Population Pharmacokinetics of Amikacin in Patients at a Teaching Hospital.Antibiotics (Basel, Switzerland) · 2025Article
- Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Trilaciclib dosage in Chinese patients with extensive-stage small cell lung cancer: a pooled pharmacometrics analysis.Acta pharmacologica Sinica · 2024Article
- Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.The AAPS journal · 2024Article
- Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Population pharmacokinetics and dosing optimization of imipenem in Chinese elderly patients.Frontiers in pharmacology · 2024Article
- Evaluation of the effect of erenumab on migraine-specific questionnaire in patients with chronic and episodic migraine.CPT: pharmacometrics & systems pharmacology · 2023Article
- Operational characteristics of full random effects modelling ('frem') compared to stepwise covariate modelling ('scm').Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Population Pharmacokinetics of Tenecteplase in Patients With Acute Myocardial Infarction and Application to Patients With Acute Ischemic Stroke.Journal of clinical pharmacology · 2023Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Population Pharmacokinetic and Concentration-QTc Analysis of Delamanid in Pediatric Participants with Multidrug-Resistant Tuberculosis.Antimicrobial agents and chemotherapy · 2022Article
- Population Pharmacokinetics and Dosing Regimen of Lithium in Chinese Patients With Bipolar Disorder.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stepwise covariate modeling (SCM) is a widely used tool in pharmacometric analyses to identify covariates that explain between-subject variability (BSV) in exposure and exposure-response relationships. However, this approach has several potential weaknesses, including over-estimated covariate effect and incorrect selection of covariates due to collinearity. In this work, we investigated the operating characteristics (i.e., accuracy, precision, and power) of SCM in a controlled setting by simulating sixteen scenarios with up to four covariate relationships. The SCM analysis showed a decrease in the power to detect the true covariates as model complexity increased. Furthermore, false highly correlated covariates were frequently selected in place of or in addition to the true covariates. Relative root mean square errors (RMRSE) ranged from 1 to 51% for the fixed effects parameters, increased with the number of covariates included in the model, and were slightly higher than the RMRSE obtained with a simple re-estimation exercise with the true model (i.e., stochastic simulation and estimation). RMRSE for BSV increased with the number of covariates included in the model, with a covariance parameter RMRSE of almost 135% in the most complex scenario. Loose boundary conditions on the continuous covariate power relation appeared to have an impact on the covariate model selection in SCM. A stricter boundary condition helped achieve high power (> 90%), even in the most complex scenario. Finally, reducing the sample size in terms of number of subjects or number of samples proved to have an impact on the power to detect the correct model.
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