parameter elimination by sequential testing

#PARAMETER ELIMINATION BY SEQUENTIAL TESTING HOW TO#
#PARAMETER ELIMINATION BY SEQUENTIAL TESTING TRIAL#

Approximate confidence intervals for the treatment mean difference and the individual means are constructed using the pivotal method of Woodroofe, and an approximation to the bias of the maximum likelihood estimator of the treatment mean difference is studied based on the work of Whitehead. Two inferential methods are considered following the design. The power function for this class of tests is the same as that under nonadaptive sampling, and significant decreases in the inferior treatment number can be achieved with only minor increases in the average total sample number. The class of one-sided group-sequential tests with response-adaptive sampling developed by Jennison and Turnbull is used to investigate which of the treatments has the larger mean response.

#PARAMETER ELIMINATION BY SEQUENTIAL TESTING TRIAL#

Finally, we propose a set of guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design.Ī sequential clinical trial model is considered in which two treatments with immediate normally distributed responses are to be compared.

Furthermore, we illustrate the computation of different estimators in practice using a real trial example. We then describe and compare proposed unbiased and bias-adjusted estimators of treatment effects for different types of adaptive designs. We first discuss how bias can affect standard estimators and critically assess the negative impact this can have.

#PARAMETER ELIMINATION BY SEQUENTIAL TESTING HOW TO#

This article addresses this issue by providing a comprehensive overview of proposed approaches to remove or reduce the potential bias in point estimation of treatment effects in an adaptive design, as well as illustrating how to implement them. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received less attention. In many adaptive designs, the conventional end-of-trial point estimates of the treatment effects are prone to bias, because they do not take into account the potential and realised trial adaptations. Recent FDA guidance on adaptive clinical trial designs defines bias as 'a systematic tendency for the estimate of treatment effect to deviate from its true value', and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias.

Parameter elimination by sequential testing