Optimal design and analysis procedures in two stage trials with a binary endpoint

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Optimal design and analysis procedures in two stage trials with a binary endpoint's image

Description:	Bowden, J (MRC Biostats) Monday 26 September 2011, 14:20-14:30


Created:	2011-10-04 09:18
Collection:	Design and Analysis of Experiments Cambridge Statistics Initiative 2011
Publisher:	Isaac Newton Institute
Copyright:	Bowden, J
Language:	eng (English)


Abstract:	Two-stage trial designs provide the exibility to stop early for efficacy or futility, and are popular because they have a smaller sample size on average compared to a traditional trial with the same type I and II errors. This makes them financially attractive but also has the ethical benefit of reducing, in the long run, the number of patients who are given ineffective treatments. Therefore designs which minimise the expected sample size are referred to as 'ptimal'. However, two-stage designs can impart a substantial bias into the parameter estimate at the end of the trial. The properties of standard and bias adjusted maximum likelihood estimators, as well as mean and median unbiased estimators are reviewed with respect to a binary endpoint. Optimal two-stage design and analysis procedures are then identified that balance projected sample size considerations with estimator performance.

Abstract:

Two-stage trial designs provide the exibility to stop early for efficacy or futility, and are popular because they have a smaller sample size on average compared to a traditional trial with the same type I and II errors. This makes them financially attractive but also has the ethical benefit of reducing, in the long run, the number of patients who are given ineffective treatments. Therefore designs which minimise the expected sample size are referred to as 'ptimal'. However, two-stage designs can impart a substantial bias into the parameter estimate at the end of the trial. The properties of standard and bias adjusted maximum likelihood estimators, as well as mean and median unbiased estimators are reviewed with respect to a binary endpoint. Optimal two-stage design and analysis procedures are then identified that balance projected sample size considerations with estimator performance.

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