Abstract
The present article is related to a nonparametric fixed-width confidence interval estimation of the parameter µ = P(X < Y ) = R F(y)dG(y), where F and G are two unknown continuous distribution functions. The estimation procedure is based on a sample obtained under some non-iid adaptive situation. We provide various asymptotic results related to the proposed procedure and compare it with a non-adaptive procedure.
Highlights
Suppose a clinical trial is conducted for comparing two competing treatments, say A and B
We assume that Zi ∼ F or G according as the i-th patient receives treatment A or B using the adaptive design, where F and G are two unknown continuous distribution functions (d.f.’s)
It is intended to make an inference about the probability of requiring lower remission time by one drug than the other
Summary
Suppose a clinical trial is conducted for comparing two competing treatments, say A and B. In the present situation, assuming continuous responses, our work is related to (2) by considering an adaptive design which allows δn to depend on all the previous allocations and observations in order to achieve some ethical gain in terms of a larger proportion of allocation to the better treatment. In this connection, one can go through the work by Rosenberger and.
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