1,721,514 research outputs found
Hall/CRC Press. 2022. 240 pages. ISBN: 9781138048980 (hbk). ISBN 9781315169859 (ebk). List price: £99.99
No full textBlinded Sample Size Recalculation in Noninferiority Trials: A Case Study in Dermatology
No full textWhen designing a clinical trial, a number of design features including the sample size have to be decided upon. The sample size calculation usually requires some discussion of relevant effect sizes and information about nuisance parameters such as standard deviations or overall event rates, with nuisance parameters being estimated from previous studies. Using a novel endpoint or moving into a new indication, no or only very limited information might be available and the sample size calculation is therefore subject to considerable uncertainty. Internal pilot study designs that allow sample size reestimation midcourse of the ongoing study have been proposed to make trials more robust to misspecifications of nuisance parameters in the planning phase. In this article we present the design of a recently completed randomized active controlled trial in dermatology as a case study. Furthermore, we demonstrate how type I error rate control can be achieved when testing for noninferiority and explore operating characteristics such as power and sample size distributions through simulations motivated by the case study. Finally, relevant regulatory guidelines on sample size reestimation are referred to
Investigating the heterogeneity of "study twins"
Full text linkMeta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favour of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, e.g. summarizing the results of two confirmatory clinical trials in phase III of a clinical development programme. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible, and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provides very little evidence on homogeneity or heterogeneity of effects, and ad-hoc decision criteria may often be misleading
Using the bayesmeta R package for Bayesian random-effects meta-regression
Full text linkBACKGROUND: Random-effects meta-analysis within a hierarchical normal
modeling framework is commonly implemented in a wide range of evidence
synthesis applications. More general problems may even be tackled when
considering meta-regression approaches that in addition allow for the inclusion
of study-level covariables. METHODS: We describe the Bayesian meta-regression
implementation provided in the bayesmeta R package including the choice of
priors, and we illustrate its practical use. RESULTS: A wide range of example
applications are given, such as binary and continuous covariables, subgroup
analysis, indirect comparisons, and model selection. Example R code is
provided. CONCLUSIONS: The bayesmeta package provides a flexible
implementation. Due to the avoidance of MCMC methods, computations are fast and
reproducible, facilitating quick sensitivity checks or large-scale simulation
studies.Comment: 17 pages, 8 figure
An Unconditional Test for Change Point Detection in Binary Sequences with Applications to Clinical Registries
No full textObjectives: Methods for change point (also sometimes referred to as threshold or breakpoint) detection in binary sequences are not new and were introduced as early as 1955. Much of the research in this area has focussed on asymptotic and exact conditional methods. Here we develop an exact unconditional test. Methods: An unconditional exact test is developed which assumes the total number of events as random instead of conditioning on the number of observed events. The new test is shown to be uniformly more powerful than Worsley's exact conditional test and means for its efficient numerical calculations are given. Adaptions of methods by Berger and Boos are made to deal with the issue that the unknown event probability imposes a nuisance parameter. The methods are compared in a Monte Carlo simulation study and applied to a cohort of patients undergoing traumatic orthopaedic surgery involving external fixators where a change in pin site infections is investigated. Results: The unconditional test controls the type I error rate at the nominal level and is uniformly more powerful than (or to be more precise uniformly at least as powerful as) Worsley's exact conditional test which is very conservative for small sample sizes. In the application a beneficial effect associated with the introduction of a new treatment procedure for pin site care could be revealed. Conclusions: We consider the new test an effective and easy to use exact test which is recommended in small sample size change point problems in binary sequences
Blinded Sample Size Recalculation in Noninferiority Trials: A Case Study in Dermatology
No full textWhen designing a clinical trial, a number of design features including the sample size have to be decided upon. The sample size calculation usually requires some discussion of relevant effect sizes and information about nuisance parameters such as standard deviations or overall event rates, with nuisance parameters being estimated from previous studies. Using a novel endpoint or moving into a new indication, no or only very limited information might be available and the sample size calculation is therefore subject to considerable uncertainty. Internal pilot study designs that allow sample size reestimation midcourse of the ongoing study have been proposed to make trials more robust to misspecifications of nuisance parameters in the planning phase. In this article we present the design of a recently completed randomized active controlled trial in dermatology as a case study. Furthermore, we demonstrate how type I error rate control can be achieved when testing for noninferiority and explore operating characteristics such as power and sample size distributions through simulations motivated by the case study. Finally, relevant regulatory guidelines on sample size reestimation are referred to
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