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20172020
most citedEfficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates

2 citations · 4 across the 9 of their papers we have counts for

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stat.ME2020

Multi-outcome trials with a generalised number of efficacious outcomes

Martin Law, Michael J. Grayling, Adrian P. Mander

Existing multi-outcome designs focus almost entirely on evaluating whether all outcomes show evidence of efficacy or whether at least one outcome shows evidence of efficacy. While…

stat.ME2019

Optimal curtailed designs for single arm phase II clinical trials

Martin Law, Michael J. Grayling, Adrian P. Mander

In single-arm phase II oncology trials, the most popular choice of design is Simon's two-stage design, which allows early stopping at one interim analysis. However, the expected tr…

stat.ME2018

Blinded and unblinded sample size re-estimation in crossover trials balanced for period

Michael Grayling, Adrian Mander, James Wason

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these p…

stat.ME2018

Design optimisation and post-trial analysis in group sequential stepped-wedge cluster randomised trials

Michael Grayling, David Robertson, James Wason +1

Recently, methodology was presented to facilitate the incorporation of interim analyses in stepped-wedge (SW) cluster randomised trials (CRTs). Here, we extend this previous discus…

stat.ME20172 cited

Efficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates

Michael Grayling, James Wason, Adrian Mander

Primarily motivated by the drug development process, several publications have now presented methodology for the design of multi-arm multi-stage experiments with normally distribut…

stat.ME20171 cited

A two-stage Fisher exact test for multi-arm studies with binary outcome variables

Michael Grayling, Adrian Mander, James Wason

In small sample studies with binary outcome data, use of a normal approximation for hypothesis testing can lead to substantial inflation of the type-I error-rate. Consequently, exa…