Dataset Information

Sequential bioequivalence trial designs with increased power and controlled type I error rates.

ABSTRACT: Methods to implement two-stage designs in two-treatment, two-sequence, and two-period crossover bioequivalence studies have only recently been developed. The two-stage methods have so far only been described for a targeted study power of 80%. Since it is sometimes desirable to increase the targeted power to 90%, this study identifies sets of alphas that work for the recently developed two-stage methods while controlling type I error rates around 5% for assumed geometric mean test/reference ratios of 0.90 and 0.95 at targeted power of 90%, and provides a characterization of the methods in terms of the resulting sample sizes and power. Depending on the actual variability and the chosen sample size at stage 1, the actual power will be between 83% and 100%. The previously characterised methods at target power 80% as well as 90% result in trivial inflation of type 1 error, but the type 1 error inflation at 90% target powers with decreased alpha at the second stage result in slightly less inflation. These results may be useful for applicant wishing to achieve increased power in bioequivalence trials without a penalty for type I error rates.

SUBMITTER: Fuglsang A

PROVIDER: S-EPMC3691437 | biostudies-other | 2013 Jul

REPOSITORIES: biostudies-other

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Sequential bioequivalence trial designs with increased power and controlled type I error rates.

Fuglsang Anders A

The AAPS journal 20130330 3

Methods to implement two-stage designs in two-treatment, two-sequence, and two-period crossover bioequivalence studies have only recently been developed. The two-stage methods have so far only been described for a targeted study power of 80%. Since it is sometimes desirable to increase the targeted power to 90%, this study identifies sets of alphas that work for the recently developed two-stage methods while controlling type I error rates around 5% for assumed geometric mean test/reference ratio ...[more]

PMID: 23543603

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Project description:BackgroundGenome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers.ResultsIn this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10 -5 for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers.ConclusionsOur results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.

Dataset Information

Sequential bioequivalence trial designs with increased power and controlled type I error rates.

Publications

Sequential bioequivalence trial designs with increased power and controlled type I error rates.

Similar Datasets

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