The hybrid two-stage design allows for dropping the poorly performing treatments early on the basis of interim analysis results and for early termination if none of the experimental treatments seems promising. A Stein-type two-sample selection approach is used at both the selection and testing stages to solve the heteroscedastic problems caused by the unknown variances. It is assumed that the variances of the experimental and the control normal populations are unknown and unequal. In this paper, a hybrid selection and testing design for comparing the means of several experimental normal populations among themselves and with the mean of a control normal population is proposed. Various multi-stage randomized phase II/III designs have been proposed for the purpose of selecting one or more promising experimental treatments and comparing them with a control, while controlling overall Type I and Type II error rates. The problem of comparing several experimental treatments to a control arises frequently in clinical trials. Comparing them in the case where there exist suspect outliers in the pilot sample, we are empirically confident that the GMD- and MAD-based procedures appear more robust than the sample-standard-deviation-based procedures. A real-life data set of weight change from female anorexic patients is then analyzed to demonstrate the practical applicability of these modified two-stage MRPE procedures. Extensive simulation studies are utilized to validate our theoretical findings. For illustrative purposes, we further investigate specific modified two-stage MRPE procedures, where we substitute appropriate multiples of sample standard deviation, Gini’s mean difference (GMD), and mean absolute deviation (MAD) in the place of Wm, respectively. With stopping variables constructed based on an arbitrary general estimator Wm for σ, which satisfies a set of certain conditions, our procedures are proved to enjoy asymptotic first- and second-order efficiency as well as asymptotic first-order risk efficiency.
Two stage sequential testing plus#
Under the squared error loss plus linear cost of sampling, we revisit the classic problem of minimum risk point estimation (MRPE) for an unknown normal mean μ ( ∈ R ) when the population variance σ 2 ( ∈ R + ) also remains unknown. In general, from our simulation study, we can understand that, for highly variable drugs (CV ≥30), the appropriate GMR value is between (0.95, 1.05), which is also appropriate for low variable drugs to achieve the minimum sample size required to conduct any clinical trials.In this paper, we design an innovative and general class of modified two-stage sampling schemes to enhance double sampling and modified double sampling procedures. The purpose of this work was to determine the minimum number of sample size required to proceed the second stage of sequential design, and the simulation is done through R ve. Furthermore, the possible implication of two stage sequential design/ sample size re-estimation is to adjust the sample size based on the observed variance estimated from the first stage. In short, the right drug at the right time for the right patient.
Two stage sequential testing trial#
That is for a trial for a positive results, early stopping ensures that a new drug product can exploited sooner, while negative results indicated, early stopping avoids wastage of resources. The concept of sequential statistical methods was originally motivated by the need to obtain clinical benefits under certain economic constraints. Sequential design is an adaptive design that allows for pre-mature termination of a trial due to efficacy or futility based on the interim analyses.
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