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pro vyhledávání: '"Robertson, David S."'
Autor:
Robertson, David S., Burnett, Thomas, Choodari-Oskooei, Babak, Dimairo, Munya, Grayling, Michael, Pallmann, Philip, Jaki, Thomas
In adaptive clinical trials, the conventional confidence interval (CI) for a treatment effect is prone to undesirable properties such as undercoverage and potential inconsistency with the final hypothesis testing decision. Accordingly, as is stated i
Externí odkaz:
http://arxiv.org/abs/2411.08771
Autor:
Robertson, David S., Burnett, Thomas, Choodari-Oskooei, Babak, Dimairo, Munya, Grayling, Michael, Pallmann, Philip, Jaki, Thomas
Regulatory guidance notes the need for caution in the interpretation of confidence intervals (CIs) constructed during and after an adaptive clinical trial. Conventional CIs of the treatment effects are prone to undercoverage (as well as other undesir
Externí odkaz:
http://arxiv.org/abs/2411.08495
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