The increase in the popularity of non-inferiority clinical trials represents the increasing need to search for substitutes for some reference (standard) treatments. A new treatment would be preferred to the standard treatment if the benefits of adopting it outweigh a possible clinically insignificant reduction in treatment efficacy (non-inferiority margin). Statistical procedures have recently been developed for treatment comparisons in non-inferiority clinical trials that have multiple experimental (new) treatments. An ethical concern for non-inferiority trials is that some patients undergo the less effective treatments; this problem is more serious when multiple experimental treatments are included in a balanced trial in which the sample sizes are the same for all experimental treatments. With the aim of giving fewer patients the inferior treatments, we propose a response-adaptive treatment allocation scheme that is based on the doubly adaptive biased coin design. The proposed adaptive design is also shown to be superior to the balanced design in terms of testing power.
Non-inferiority (NI) trials are implemented when there is a practical demand to search for alternatives to standard therapies, such as to reduce side effects.An experimental treatment is considered non-inferior to the standard treatment when it exhibits clinically non-significant loss of efficacy. Ordinal categorical responses are frequently observed in clinical trials. It has been reported that responses measured using an ordinal scale produce more informative analysis than when responses collapse into binary outcomes. We study the NI trials using ordinal endpoints. We propose a latent variable model for ordinal categorical responses. Based on the proposed latent variable model, the mean efficacy of the different treatments is denoted by the corresponding mean parameter of the underlying continuous distributions. A two-step procedure is proposed for model identification and parameter estimation. A non-inferiority analysis can then be conducted based on the latent variable model and the corresponding estimation procedure. We also develop a method and an algorithm to produce an optimal sample size configuration based on the proposed testing procedure.Two clinical examples are provided for demonstrative purposes.
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