Sample size recommendation for a bioequivalent study
Keywords:Bioequivalence, Healthy volunteers, Sample Size, Power
AbstractThere are clear guidelines and suggestions on the sample size and power calculation from health authorities (HA) for Bio equivalence (BE) studies in Healthy volunteers (HV). The suggested power is at least 80\% and type 1 error is 5\%. In real life situations, the clinical trials plan with more than 80\%, giving rise to larger sample size. The increased power means more subjects, more wastage of time and more resources to complete the study, resulting in more money spent. This paper attempts to show how much reduction in the sample size can be achieved without affecting the scientific validity of the study and also the brief summary on the overall effect of reduced sample size on resources (subjects, time, blood and cost). We executed simulations in order to show the impact on the power and the 2 one sided confidence interval approach to show the study equivalence or otherwise. For illustration purpose, a couple of 2 period cross over studies were considered. 100 simulations were executed with different sample sizes to compare with the original results.
H. AMMETER (1962). Experience rating a new application of the collective theory of risk. ASTIN Bulletin, 2, pp. 261–270.
L. BADEA, D. TILIVEA (2008). Nonnegative decompositions with resampling for improving gene expression data biclustering stability. Proceedings of the 18th European Conference on Artificial Intelligence.
K. BENZ, T. M. BOHNERT (2014). Impact of pacemaker failover configuration on mean time to recovery for small cloud clusters. Proceedings of the 7th IEEE International Conference on Cloud Computing, pp. 384–391.
S. BHAMIDI, R. VAN DER HOFSTAD, G. HOOGHIEMSTRA (2010). First passage percolation on random graphs with finite mean degrees. Annals of Applied Probability, 20, pp. 1907–1965.
D. BHATTACHARJEE, N. MUKHOPADHYAY (2011). On mp test and the mvues in a n(theta, ctheta) distribution with unknown: Illustrations and applications. Journal of the Japan Statistical Society, 41, pp. 75–91.
I. CARIDI (2014). Properties of interaction networks underlying the minority game. Physical Review E, 90.
K. CHOI (2005). Some properties of sequential point estimation of the mean. Journal of Korean Data and Information Science Society, 16, pp. 657–663.
V. DONGEN (1999). Unbiased estimation of individual asymmetry. Journal of Evolutionary Biology, 13, pp. 107–112.
S. U. K. E. S. CHUNG (2013). Bayesian rainfall frequency analysis with extreme value using the informative prior distribution. KSCE Journal of Civil Engineering, 17, pp. 1502–1514.
P. FENN, N. RICKMAN, A. MCGUIRE (1994). Contracts and supply assurance in the UK health care market. Journal of Health Economics, 13, pp. 125–144.
E. FORTI, C. FRANZONI, M. SOBRERO (2007). The effect of patenting on the networks and connections of academic scientists. Working Paper.
G. D. FRASER, A. D. C. CHAN, J. R. GREEN, D. MACISAAC (2012). Detection of adc clipping, quantization noise, and amplifier saturation in surface electromyography. Proceedings of the IEEE International Symposium on Medical Measurements and Applications, pp. 1–5.
M. KALISZAN (2011). Does a draft really influence postmortem body cooling? Journal of Forensic Sciences, 56, pp. 1310–1314.
S. KHAN, R. GREINER (2013). Finding discriminatory genes: A methodology for validating microarray studies. Proceedings of the 13th IEEE International Conference on Data Mining Workshops, pp. 64–71.
S. K. KIM, S. L. KIM, Y. W. LEE (2007). Sequential confidence intervals with beta-protection in a normal distribution having equal mean and variance. Journal of Applied Mathematics and Computing, 23, pp. 479–488.
N. MUKHOPADHYAY (2006). Mvue for the mean with one observation. The American Statistician, 60, pp. 71–74.
N. MUKHOPADHYAY, D. BHATTACHARJEE (2010). A note on minimum variance unbiased estimation. Communications in Statistics—Theory and Methods, 39, pp.
N. MUKHOPADHYAY, G. CICCONETTI (2004). Applications of sequentially estimating the mean in a normal distribution having equal mean and variance. Sequential Analysis, 23, pp. 625–665.
B. M. D. S. N. MUKHOPADHYAY (2008). Theory and applications of a new methodology for the random sequential probability ratio test. Statistical Methodology, 5, pp. 424–453.
A. P. PRUDNIKOV, Y. A. BRYCHKOV, O. I. MARICHEV (1986). Integrals and Series, volume 1. Gordon and Breach Science Publishers, Amsterdam.
R DEVELOPMENT CORE TEAM (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria.
N. M. S. BANERJEE (2016). A general sequential fixed-accuracy confidence interval estimation methodology for a positive parameter: Illustrations using health and safety data. Annals of the Institute of Statistical Mathematics, 68, pp. 541–570.