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pro vyhledávání: '"Robertson, David S"'
The use of the non-parametric Restricted Mean Survival Time endpoint (RMST) has grown in popularity as trialists look to analyse time-to-event outcomes without the restrictions of the proportional hazards assumption. In this paper, we evaluate the po
Externí odkaz:
http://arxiv.org/abs/2311.01872
Autor:
Robertson, David S., Choodari-Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, Jaki, Thomas
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to
Externí odkaz:
http://arxiv.org/abs/2211.15620
Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological ap
Externí odkaz:
http://arxiv.org/abs/2208.11418
Motivation: While the analysis of a single RNA sequencing (RNAseq) dataset has been well described in the literature, modern research workflows often have additional complexity in that related RNAseq experiments are performed sequentially over time.
Externí odkaz:
http://arxiv.org/abs/2206.02213
When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample accordi
Externí odkaz:
http://arxiv.org/abs/2205.03820
Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I
Externí odkaz:
http://arxiv.org/abs/2202.03838
Autor:
Robertson, David S., Choodari-Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, Jaki, Thomas
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 th
Externí odkaz:
http://arxiv.org/abs/2105.08836
Autor:
Kunzmann, Kevin, Grayling, Michael J., Lee, Kim M., Robertson, David S., Rufibach, Kaspar, Wason, James M. S.
Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Designs with adaptive sample size need to account for their optional stopping to guarantee strict type-I error-rate
Externí odkaz:
http://arxiv.org/abs/2010.06567
Autor:
Kunzmann, Kevin, Grayling, Michael J., Lee, Kim May, Robertson, David S., Rufibach, Kaspar, Wason, James M. S.
Publikováno v:
Am. Stat., 2021, 75(4), 424--432
Sample size derivation is a crucial element of the planning phase of any confirmatory trial. A sample size is typically derived based on constraints on the maximal acceptable type I error rate and a minimal desired power. Here, power depends on the u
Externí odkaz:
http://arxiv.org/abs/2006.15715
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomi
Externí odkaz:
http://arxiv.org/abs/2005.00564