Predictability of designs which adjust for imbalances in prognostic factors |
| |
Authors: | Yolanda Barbá chano,D. Stephen Coad,Derek R. Robinson |
| |
Affiliation: | 1. Department of Clinical Research and Development, Royal Marsden Hospital, Downs Road, Sutton SM2 5PT, UK;2. School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, UK;3. Department of Mathematics, University of Sussex, Falmer, Brighton BN1 9RF, UK |
| |
Abstract: | Minimisation is a method often used in clinical trials to balance the treatment groups with respect to some prognostic factors. In the case of two treatments, the predictability of this method is calculated for different numbers of factors, different numbers of levels of each factor and for different proportions of the population at each level. It is shown that if we know nothing about the previous patients except the last treatment allocation, the next treatment can be correctly guessed more than 60% of the time if no biased coin is used. If the two previous assignments are known to have been the same, the next treatment can be guessed correctly around 80% of the time. Therefore, it is suggested that a biased coin should always be used with minimisation. Different choices of biased coin are investigated in terms of the reduction in predictability and the increase in imbalance that they produce. An alternative design to minimisation which makes use of optimum design theory is also investigated, by means of simulation, and does not appear to have any clear advantages over minimisation with a biased coin. |
| |
Keywords: | Adaptive design Biased coin DA-optimum design Markov chain Minimisation Stationary distribution |
本文献已被 ScienceDirect 等数据库收录! |
|