ArticleJournal of pharmacokinetics and pharmacodynamics2010
Sample size/power calculations for repeated ordinal measurements in population pharmacodynamic experiments.
Article in Journal of pharmacokinetics and pharmacodynamics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Statistical power calculations for mixed pharmacokinetic study designs using a population approach.The AAPS journal · 2014Article
- Optimizing disease progression study designs for drug effect discrimination.Journal of pharmacokinetics and pharmacodynamics · 2013Article
- Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.The AAPS journal · 2012Article
- Population Fisher information matrix and optimal design of discrete data responses in population pharmacodynamic experiments.Journal of pharmacokinetics and pharmacodynamics · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Population pharmacodynamic experiments sometime involve repeated measurements of ordinal random variables at specific time points. Such longitudinal data presents a challenge during modelling due to correlation between measurements within an individual and often mixed-effects modelling approach may be used for the analysis. It is important that these studies are adequately powered by including an adequate number of subjects in order to detect a significant treatment effect. This paper describes a method for calculating sample size for repeated ordinal measurements in population pharmacodynamic experiments based on analysis by a mixed-effects modelling approach. The Wald test is used for testing the significance of treatment effects. This method is fast, simple and efficient. It can also be extended to account for differential allocation of subjects to the groups and unbalanced sampling designs between and within groups. The results obtained from two simulation studies using nonlinear mixed-effects modelling software (NONMEM) showed good agreement between the power obtained from simulation and nominal power used for sample size calculations.
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