Bioequivalence and Bioavailability Forum

Hi ElMaestro,

❝ I am looking at my results now and trying to figure out if there is a story in this. So let me ask you experts: Let us say we look at e.g. CV=.3 and N1=12 (6 per sequence at stage 1). What would you expect in terms of power and type I error (i.e. any difference from table I in Potvin's work), if 2 subjects go lost in sequence RT during stage 2?

Both the type I error and power will be lower than with a complete stage 2. Hence, if we use a framework which controls the TIE and estimate the sample size of stage 2 with an appropriate algo (1 df less than usual due to the stage term) we should not be worried about the TIE – a smaller sample size translates into a lower chance to show BE and hence, also a lower TIE. Like in fixed sample designs the impact of dropouts on power will be small.

On the other hand, in TSDs it is not a clever idea to include more than the estimated n₂ subjects (greedy CROs selling “additional subjects in order to compensate for potential loss in power due to dropouts” to sponsors). That’s a no-go because it might inflate the TIE. The literature on GSDs and adaptive methods is full of tricks adjusting the final α if the planned sample size was overrun. You don’t want to go there.

Not for an old salt like you but novices: If you think about modifying one of the validated frameworks it is mandatory to assess the operational characteristics (TIE, power) in simulations. Pocock’s α 0.0294 is not a natural constant. General rules: Futility criteria lower the TIE, whereas a minimum stage 2 sample size might lead to an inflated TIE.
Example: Molins et al.* modified Potvin’s methods by introducing a futility N_max 150 and a minimum n₂ of 1.5×n₁. Their adjusted α was 0.0301 for ‘modified B’ and 0.0280 for ‘modified C’.

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• Molins E, Cobo E, Ocaña J. Two‐stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: Which to choose? Stat Med. 2017;36(30):4777–88. doi:10.1002/sim.7452.