# data[].attributes.body
When managing an ongoing trial, why would it be an issue to present the number of participants allocated to each arm during, for example, a Trial Management Group (TMG) meeting?

Add your answer as a post (big plus in the bottom right hand side of your screen) starting with Example 1

# data[].attributes.body
The TMG should remain blinded. Early on, with small numbers, the TMG would get an idea about trial outcomes and might be able to relate them to one of the arms. There are also potential issues about allocation concealment of incoming patients, depending on how the trial recruits.

# data[].attributes.body
This may be an issue where ppi are involved In the tmg meeting. For example, if the patient volunteer is also partaking in the trial, this data may aid them in figuring out what arm they were allocated to. If the staff in the meeting are also blinded to the trial, sharing information about the arms may also effect blinding.

# data[].attributes.body
When managing an ongoing trial, why might you need to adapt your consort diagram in this particular area?

Add your answer as a post (big plus in the bottom right hand side of your screen) starting with Example 2

# data[].attributes.body
There may be trial-specific screening assessment to confirm eligibilty, worth giving detail on. It may be important to know if patient die between screening and randomisation. For inpatients, it's less likely they'll be uncontactable.
Important to account for all the reasons that screened patients are not randomised.

# data[].attributes.body
This area may need to be adapted where there is an issue in building participant number. If the eligibility criteria is too strict for example, eligibility may need to be more flexible to allow for more patients to partake in the trial. Another amendment may be with screening. An additional step may be needed where a trial needs to confirm eligibility of patients, for example where additional scans are needed.

# data[].attributes.body
When managing an ongoing trial, what could be problematic about the information  presented in the boxes highlighted with the red rectangle?

Add your answer as a post (big plus in the bottom right hand side of your screen) starting with Example 3

# data[].attributes.body
Presenting the information in the box may lead to bias. For example, if a clinician on the tmg who is also involved in treating trial patients notices a higher number of deaths in a particular arm, they may treat patients in that arm differently (e.g. providing additional care).

# data[].attributes.body
This could involve looking at the outcome data as the trial continues, and could give an indication of which treatments patients were getting - potentially unblinding.
If this is looked at during trial management, it could push investigators to draw early conclusions, which would be under-powered and potentially misleading.

# data[].attributes.body
When managing an ongoing trial, what could be problematic about the information  presented in the 'data available for analysis' box (highlighted with the red rectangle)?

Add your answer as a post (big plus in the bottom right hand side of your screen) starting with Example 4

# data[].attributes.body
Whether there is data available for analysis, will rely on several factors (time on study, snapshot available, statistician availability, data checking). It might be difficult to know what is influencing this number.
Also need to be clear on which analyses this takes into account.

# data[].attributes.body
as in the previous example, sharing information with those actively involved in the trial may lead to bias. For example, if clinicians or nurses see a lower number of data available in a particular arm, they may inappropriately decide that arm to be ineffective or harmful. This again may result in differences in patient care or data reporting.