Our cross-sectional survey of CSOs has illuminated the complexities inherent in the task of recruiting trial participants from NHS mental health services.
When a gene-neutral, two-stage classifier was applied to the real-world task of cancer trial annotation, it achieved a highest accuracy of 89.8%, demonstrating the feasibility of developing a gene-neutral classifier for this task.
In addition, we applied the classifier to a real-world task of cancer trial annotation and evaluated its performance using a larger sample size (4,013 instances from 249 distinct human gene symbols detected from 250 trials).
In addition, we applied the gene-neutral classifier to the real-world task of cancer trial annotation of mentions of any gene in the HGNC list of human genes (33,128 in total).
Such a serene setting belies the task of time trials.
Participants failed to respond on 2.4% (Saccade task), 0.8% (Combined task), and 5.4% (Covert task) of all trials where relevant targets were presented.
The last method updates the classifier without knowing the task of the past trial (unsupervised adaptation).
We applied this toolbox to the typical task of a clinical trial statistician of making decisions for pre-specified sensitivity analyses or the efficacy analysis in a statistical analysis plan.
To this end, participants performed a Go/Nogo task with trials of varying levels of difficulty to elicit aware vs. unaware errors, as well as correct hits.
In an event-related design, participants performed Tower of London task trials of four different difficulty levels, alternating with corresponding baseline trials, all implemented in E-prime 1.1 sPsychology Software Toolsools).
All blocks were 18 s in length and contain four 4.5 s fixed time trials, for a total of 24 task trials and 28 baseline trials per run, 96 task trials and 112 baseline trials across the four runs.
