1/n I was asked to give an industry statistician's view on A) below. Disclaimer: I do not know about the exact regulations (which might also be region-specific). What I offer is a 1st hand experience of what happens around a trial stopping early. A thread.
3/n I was the trial statistician for this trial: nejm.org/doi/full/10.10…

Planned efficacy interim after ~245 PFS events.
1) Data with sponsor, except for tmt assignment (=rando codes).
2) Rando codes with IxRS vendor.
3) Indep. Stat. Reporting Group (ISRG) coordinates.
4/n If you'd like to know more about the setup have a look at Janet Wittes' slides which she presented at recent EFSPI regulatory statistics workshop: efspi.org/EFSPI/Events/R…
5/n Sponsor sends clean data to ISRG & IxRS vendor sends rando codes. ISRG generates *unblinded* outputs and shares with iDMC. So, ISRG + iDMC approx know results. They meet, discuss, and submit recommendation to sponsor. Call this day 0.
6/n I got this call at 10:30pm. Not much sleep that night 😉Initiate
1) submission of rando codes to sponsor,
2) generation of pre-programmed outputs,
3) get iDMC outputs.

Stats team starts reviewing results, then shares with broader project team. If you are efficient, day 5.
7/n Now ISRG + iDMC + sponsor project team (stats, data mgmt, programmers, clinicians, regulatory colleagues, ...) know results. Start involving further folks at sponsor, such as business team, chain of command up to the top. Easily, this can be 100 people by now.

Say, day 10.
8/n *All* these people involved had to be prespecified and submitted to authorities - to give them means to check insider training. And AFAIK they actually do check.

Typically, a press release is then issued (say day 10), with topline results, so that everyone is on same page.
9/n Q's remain:
1) Why not add precise numbers to press release? Typically you sit on results to break them at conference. I'd prefer to communicate them earlier in fact.
10/n

2) Why not write paper along with press release? That would involve even more people (investigators, at least some of them!) and take more time, increasing the risk of insider trading.
11/n I hope this 1st hand account sheds some light on the process, and why it is in place. Happy to answer question, as much as I can!

Thanks @ADAlthousePhD for asking and providing a platform for explanation. Often, there are good reasons why things are as they are.

The end.

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More from @numbersman77

5 Oct
Registration still open for EFSPI regulatory statistics webinars. Currently we have 627 and 534 registrations for the two webinars. Webex can handle 1000 people dialing in, so go ahead and register! 😉

Program and registration: efspi.org/Documents/Even…

On Day 2 we now added...
...two talks discussing the impact of COVID-19 on clinical trials and their estimands:

Yongming Gu (Eli Lilly): Using a mix of strategies in handling intercurrent events and missing values for studies impacted by the COVID-19 pandemic

...and...
Guenther Mueller-Velten, Yi Wang, Melanie Wright (Novartis): Impact of COVID-19 and risk mitigation in a global cardiovascular outcomes trial

@EFSPItweet #estimands #regulatorystatistics #statstwitter #biostatistics
Read 4 tweets
2 Oct
1/n Don Rubin: Design trumps analysis.

projecteuclid.org/euclid.aoas/12…

Great paper summarizing potential outcomes framework and discussing the continuum from RCTs to observational studies.

I wish I had read that back in 2008! Some quotes:

#causalinference #statstwitter #epitwitter
"For obtaining causal inferences that are objective, and therefore have the best chance of revealing scientific truths, carefully designed and executed randomized experiments are generally considered to be the gold standard."
"All statistical studies for causal effects are seeking the same type of answer, and real world randomized experiments and comparative observational studies do not form a dichotomy, but rather are on a continuum, from well-suited for drawing causal inferences to poorly suited."
Read 7 tweets

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