ArticleThe AAPS journal2012
Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.
Article in The AAPS journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Dose-response modelling of umeclidinium and fluticasone furoate/umeclidinium in asthma.European journal of clinical pharmacology · 2015Trial
- Pharmacokinetics of para-aminosalicylic acid in HIV-uninfected and HIV-coinfected tuberculosis patients receiving antiretroviral therapy, managed for multidrug-resistant and extensively drug-resistant tuberculosis.Antimicrobial agents and chemotherapy · 2014Trial
- Dose response of umeclidinium administered once or twice daily in patients with COPD: a randomised cross-over study.BMC pulmonary medicine · 2014Trial
- Population pharmacokinetic assessment of the effect of food on piperaquine bioavailability in patients with uncomplicated malaria.Antimicrobial agents and chemotherapy · 2014Trial
- Interoccasion variability in population pharmacokinetic models: identifiability, influence, interdependencies and derived study design recommendations.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Sparse item testing of clinical scales in neurology trials to alleviate burden to patients.Journal of neurology · 2024Article
- Sample Size Determination and Study Design Impact on Dose-Scale Pharmacodynamic Bioequivalence: a Case Study Using Orlistat.The AAPS journal · 2024Article
- Power to identify exposure-response relationships in phase IIa pulmonary tuberculosis trials with multi-dimensional bacterial load modeling.CPT: pharmacometrics & systems pharmacology · 2024Article
- The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods.Developmental cognitive neuroscience · 2023Article
- Standards for model-based early bactericidal activity analysis and sample size determination in tuberculosis drug development.Frontiers in pharmacology · 2023Article
- Saliva as a sampling matrix for therapeutic drug monitoring of gentamicin in neonates: A prospective population pharmacokinetic and simulation study.British journal of clinical pharmacology · 2022Observational
- The power of modelling pulsatile profiles.Journal of pharmacokinetics and pharmacodynamics · 2021Article
- Item Response Theory Modeling of the International Prostate Symptom Score in Patients with Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia.The AAPS journal · 2020Article
- Integrated Item Response Theory Modeling of Multiple Patient-Reported Outcomes Assessing Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia.The AAPS journal · 2020Article
- Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM.Journal of pharmacokinetics and pharmacodynamics · 2019Article
- A Mechanism-Based Population Pharmacokinetic Analysis Assessing the Feasibility of Efavirenz Dose Reduction to 400 mg in Pregnant Women.Clinical pharmacokinetics · 2018Article
- Study Design Selection in Early Clinical Anti-Hyperglycemic Drug Development: A Simulation Study of Glucose Tolerance Tests.CPT: pharmacometrics & systems pharmacology · 2018Article
- Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes.CPT: pharmacometrics & systems pharmacology · 2018Article
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4 authors.
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Abstract
Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.
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