Simulations highly recommended [Two-Stage / GS Designs]

posted by Helmut Homepage – Vienna, Austria, 2024-01-05 13:40 (165 d 02:28 ago) – Posting: # 23820
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Hi Achievwin,

❝ I am thinking of using two stage approach for a parallel BE study (in patients) is there an accepted procedure for sample size calculations? at stage 1 and stage 2?

In general I recommend to perform the first stage in a sample size which gives you reasonably high power to pass already. The second stage is then only a kind of ‘safety net’ if you fail in the first.

❝ Any instances where FDA accepted a two stage design methodology for calculating samples size for stage 2 after reviewing stage 1 results?

It was outlined in a presentation »Uses of Adaptive Design Approach for Generic Bioequivalence Study Submitted in FDA« by Xiaojian Jiang (Deputy Director Division of Bioequivalence II, Office of Bioequivalence, Office of Generic Drugs) at the 5th GBHI (Amsterdam. 28 Sep 2022).

The relevant points:(my emphases)
If you give me your current email address, I will send you the presentation.

Practically for parallel designs simulations are mandatory because:
  1. The number of subjects in the T- and R-groups (in both stages) will be different due to dropouts.
  2. Variances likely will be different; hence the Welch-Satterthwaite test instead if the common t-test (which is liberal in these cases) has to be used.
  3. Fine for the FDA because no specific statistical model is specified by the agency.
Simulations can be performed in the [image]-package Power2Stage, function power.tsd.p().
Start with a reasonably narrow grid of n1 / CV1 combinations to find a – preliminary – adjusted α which controls the Type I Error. Repeat with some scenarios based on the worst case expected dropout rate (same for T and R, all under T – none under R and vice versa), different CVs (CVT < CVR, CVT > CVR). If the Type I Error is still controlled in all scenarios, fine. If not (likely), adjust more.

One of mine. αadj 0.0274, validated for n1 124–250, homoscedastic CV 50% (our best guess), heteroscedastic (variance ratios 1:4 to 4:1). Maximum empirical Type I Error 0.04987:


32 pages report with justification, methods, all results, scripts to reproduce them. Not accepted by the EMA because “we don’t like (‼) simulations”…

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