Rapid Sample Size Calculations for a Defined Likelihood Ratio Test-Based Power in Mixed-Effects Models
2012 (English)In: AAPS Journal, ISSN 1550-7416, Vol. 14, no 2, 176-186 p.Article in journal (Refereed) Published
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.
likelihood ratio test, NONMEM, pharmacometrics, power, sample size
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:uu:diva-174344DOI: 10.1208/s12248-012-9327-8ISI: 000302814900004OAI: oai:DiVA.org:uu-174344DiVA: diva2:528240