# Parametric sample size calculation and non-parametric analysis

I'm curious to know if anyone has a specific reference (text or journal article) to support the common practice in the medical literature of performing sample size calculation using methods that are parametric (i.e. assuming a normal distribution and a certain variance of measurements) when the analysis of the primary trial outcome will be done using non-parametric methods.

An example: primary outcome is time to vomiting after giving a certain drug, which is known to have a mean value of 20 minutes (SD 6 minutes), but has a noticeably right-skewed distribution. The sample size calculation is done with the assumptions listed above, using the formula

$n(\text{per-group})=f(\alpha,\beta) \times (2\sigma^2 /(\mu_1 - \mu_2)^2 )$,

where $f(\alpha, \beta)$ changes based on the desired $\alpha$ and $\beta$ errors.

However, because of the skewness of the distribution, the analysis of the primary outcome will be based on ranks (non-parametric method such as the Mann Whitney U test).

Is this schema supportable by authors in the statistical literature, or should non-parametric sample size estimates be performed (and how would these be done)?

My thoughts are that, for ease of calculation, it is acceptable to do the above practice. After all, sample size estimates are just that - estimates that make several assumptions already - all of which are likely slightly (or very!) imprecise. However, I am curious to know what others think, and specifically to know if there are any references to support this line of reasoning.

Many thanks for any assistance.

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