CVintra # Mean/SD [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2007-09-17 17:59 (6119 d 18:26 ago) – Posting: # 1087
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Dear Mathews!

❝ I know only the Cmax value of the drug from the literature. It is around 42.34 ± 13.27.

❝ How i can calculate the intra subject CV from this Cmax value? (is it required for Sample Size calculation?)


Unfortunatelly there's no way to calculate CVintra from CVtotal (CVtotal = CVintra + CVinter). Actually you don't even have CVtotal (in log scale), because CV = SD/mean (what you have) comes from untransformed data.
Also see this post.

❝ which value of "delta" we commonly used for calculating Sample Size?


With exception of Canada, where delta should be set to zero (ratio=1) and a correction for actual content should be made in the assessment of BE, delta should be set to the expected difference (e.g., from a reasonably sized pilot study).
The commonly applied approach is to set the expected ratio to 0.95 (or -5%). Rationale behind is that release specifications of a batch generally are ±5%; since this applies to both test and reference - and we assume an average deviation from declared content of ±2.5% we end up with ±5% for the study.

❝ there are different sample size formulas for additive model and multiplicative model?


Sure. Different model --> different error distribution / power function / sample sizes.

❝ which model we commonly used? why?


It's a generally accepted assumption that many biological variables (including PK metrics like AUC, Cmax) follow a lognormal distribution. Furthermore serial dilutions in analytics also lead to multiplicative errors. Since additivity of effects and homoscedasticity (homogeneity of variance) is a prerequisite for ANOVA, we apply a multiplicative model (i.e., work with log-transformed data instead with raw data). By this the distribution skewed to the right becomes symetrical and values below zero are avoided.

For tmax an additive model is most suitable; although a nonparametric analysis is also a must (ANOVA is incorrect for discrete data).

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