Inference following biased coin designs in clinical trials.
Abstract
Randomization schemes for two-treatment clinical trials are studied. Theoretical expressions
for the power are derived under both complete randomization and Efron’s biased
coin design for normal and binary responses. The better the scheme is at balancing
the numbers of patients across treatments, the higher the power is. Efron’s biased coin
design is more powerful than complete randomization. Normal approximations to the
powers are obtained. The power of the adjustable biased coin design is also investigated
by simulation.
Covariate-adaptive randomization schemes are analysed when either global or marginal
balance across cells is sought. By considering a fixed-effects linear model for normal
treatment responses with several covariates, an analysis of covariance t test is carried
out. Its power is simulated for global and marginal balance, both in the absence and in
the presence of interactions between the covariates. Global balancing covariate-adaptive
schemes are more efficient when there are interactions between the covariates.
Restricted randomization schemes for more than two treatments are then considered.
Their asymptotic properties are provided. An adjustable biased coin design is introduced
for which assignments are based on the imbalance across treatments. The finitesample
properties of the imbalance under these randomization schemes are studied by
simulation. Assuming normal treatment responses, the power of the test for treatment
differences is also obtained and is highest for the new design. Imbalance properties of complete randomization and centre-stratified permuted block
randomization for several treatments are investigated. It is assumed that the patient recruitment
process follows a Poisson-gamma model. When the number of centres is large,
the imbalance for both schemes is approximately multivariate normal. The power of a
test for treatment differences is simulated for normal responses. The loss of power can
be compensated for by a small increase in sample size.
Authors
Yeung, Wai YinCollections
- Theses [3822]