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Rapid Sample Size Calculations for a Defined Likelihood Ratio Test-Based Power in Mixed-Effects Models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics)
2012 (English)In: AAPS Journal, ISSN 1550-7416, Vol. 14, no 2, 176-186 p.Article in journal (Refereed) Published
Abstract [en]

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 (Delta iOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The Delta iOFV is sampled and summed (a Delta iOFVs) for each study at each sample size of interest to study, and the percentage of a Delta iOFVs 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.

Place, publisher, year, edition, pages
2012. Vol. 14, no 2, 176-186 p.
Keyword [en]
likelihood ratio test, NONMEM, pharmacometrics, power, sample size
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-174344DOI: 10.1208/s12248-012-9327-8ISI: 000302814900004OAI: oai:DiVA.org:uu-174344DiVA: diva2:528240
Available from: 2012-05-24 Created: 2012-05-15 Last updated: 2015-01-23Bibliographically approved
In thesis
1. Model-Based Optimization of Clinical Trial Designs
Open this publication in new window or tab >>Model-Based Optimization of Clinical Trial Designs
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

General attrition rates in drug development pipeline have been recognized as a necessity to shift gears towards new methodologies that allow earlier and correct decisions, and the optimal use of all information accrued throughout the process. The quantitative science of pharmacometrics using pharmacokinetic-pharmacodynamic models was identified as one of the strategies core to this renaissance. Coupled with Optimal Design (OD), they constitute together an attractive toolkit to usher more rapidly and successfully new agents to marketing approval.

The general aim of this thesis was to investigate how the use of novel pharmacometric methodologies can improve the design and analysis of clinical trials within drug development. The implementation of a Monte-Carlo Mapped power method permitted to rapidly generate multiple hypotheses and to adequately compute the corresponding sample size within 1% of the time usually necessary in more traditional model-based power assessment. Allowing statistical inference across all data available and the integration of mechanistic interpretation of the models, the performance of this new methodology in proof-of-concept and dose-finding trials highlighted the possibility to reduce drastically the number of healthy volunteers and patients exposed to experimental drugs. This thesis furthermore addressed the benefits of OD in planning trials with bio analytical limits and toxicity constraints, through the development of novel optimality criteria that foremost pinpoint information and safety aspects. The use of these methodologies showed better estimation properties and robustness for the ensuing data analysis and reduced the number of patients exposed to severe toxicity by 7-fold.  Finally, predictive tools for maximum tolerated dose selection in Phase I oncology trials were explored for a combination therapy characterized by main dose-limiting hematological toxicity. In this example, Bayesian and model-based approaches provided the incentive to a paradigm change away from the traditional rule-based “3+3” design algorithm.

Throughout this thesis several examples have shown the possibility of streamlining clinical trials with more model-based design and analysis supports. Ultimately, efficient use of the data can elevate the probability of a successful trial and increase paramount ethical conduct.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 124 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 192
nonlinear mixed-effects models, pharmacometrics, likelihood ratio test, NONMEM, power, sample size, study design, proof-of-concept, dose-finding, population optimal design, LOQ, BQL data, neutropenia, docetaxel, myelosuppression, thrombocytopenia, MTD, Bayesian methods, 3+3 algorithm, dose escalation study
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
urn:nbn:se:uu:diva-233445 (URN)978-91-554-9063-8 (ISBN)
Public defence
2014-11-21, B41, BMC, Husargatan 3, Uppsala, 13:15 (English)
Available from: 2014-10-31 Created: 2014-10-05 Last updated: 2015-01-23

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