Has anyone in *medicine* (or otherwise, but particularly interested in US academic medicine) actually proposed a study where they said they'd use an alpha threshold above 0.05? How was it received? (cont)
(Also, please do me a favor, spare me the arguments about NHST being a flawed paradigm on this particular thread)
Clearly not all studies have the same tradeoffs of a false-positive vs a false-negative finding, and in some cases a higher alpha threshold seems like it should be warranted...
...my understanding is that in some contexts (pharma companies in early stage research, for example) this is more accepted since they just want to identify things that are promising enough to pursue further...
...but I'm curious, in a grant-reviewed academic-medicine setting, has anyone actually proposed this? How was it received? Happy to hear stories publicly or privately, if anyone willing to share.
Of course, I ask because I've specifically been asked to work up a study design where the sample size is simply not going to feasibly allow good power (by conventional reckoning) at an alpha=0.05, but I think there's a semi-legitimate case to be made...
...that in the specific context of this study, a higher alpha level should be permitted, and I'm curious to hear from folks that have attempted this if they were received sympathetically or got the "it's unusual to use alpha more than 0.05, everyone knows that is significance"
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@Jabaluck@_MiguelHernan@aecoppock I think (perhaps unsurprisingly) that this shows “different people from different fields see things differently because they work in different contexts” - the scenario you painted here is not really possible with how most *medical* RCTs enroll patients & collect baseline data
@Jabaluck@_MiguelHernan@aecoppock The workflow for most medical RCTs (excepting a few trial designs…which I’ll try to address at the end if I have time) is basically this:
@Jabaluck@_MiguelHernan@aecoppock 1. Clinics/practices/hospitals know that they are enrolling patients in such-and-such trial with such-and-such criteria.
Amusing Friday thoughts: I've been reading Stuart Pocock's 1983 book Clinical Trials: A Practical Approach (do not concern yourself with the reason).
There is a passage on "Statistical Computing" in Chapter 11 of the book which one might have expected would age poorly, but is in fact remarkable for how well several of the statements have held up.
"I would like to refer briefly to the frequent misuse of statistical packages. Since they make each analysis task so easy to perform, there is a real danger that the user requests a whole range of analyses without any clear conception of what he is looking for."
Fun thread using some simulations modeled on the ARREST trial design (presented @CritCareReviews a few months ago) to talk through some potential features you might see when we talk about “adaptive” trials
DISCLAIMER: this is not just a “frequentist” versus “Bayesian” thread. Yes, this trial used a Bayesian statistical approach, but there are frequentist options for interim analyses & adaptive features, and that’s a longer debate for another day.
DISCLAIMER 2: this is just a taste using one motivational example for discussion; please don’t draw total sweeping generalizations about “what adaptive trials do” from this thread, as the utility of each “feature” must always be carefully considered in that specific context
Here is a little intro thread on how to do simulations of randomized controlled trials.
This thread will take awhile to get all the way through & posted, so please be patient. Maybe wait a few minutes and then come back to it.
This can be quite useful if you’re trying to understand the operating characteristics (power, type I error probability, potential biases introduced by early stopping rules) of a particular trial design.
I will use R for this thread. It is free. I am not interested in debates about your favorite stats program at this time.
If you want to do it in something else, the *process* can still be educational; you’ll just have to learn to mimic this process in your preferred program.
Here’s a brief follow-up thread answering a sidebar question to the last 2 weeks’ threads on interim analyses in RCT’s and stopping when an efficacy threshold is crossed
The “TL;DR” summary of the previous lesson(s): yes, an RCT that stops early based on an efficacy threshold will tend to overestimate the treatment effect a bit, but that doesn’t actually mean the “trial is more likely to be a false positive result”
(Also, it seems that this is generally true for both frequentist and Bayesian analyses, though the prior may mitigate the degree to which this occurs in a Bayesian analysis)
As promised last week, here is a thread to explore and explain some beliefs about interim analyses and efficacy stopping in randomized controlled trials.
Brief explanation of motivation for this thread: many people learn (correctly) that randomized trials which stop early *for efficacy reasons* will tend to overestimate the magnitude of a treatment effect.
This sometimes gets mistakenly extended to believing that trials which stopped early for efficacy are more likely to be “false-positive” results, e.g. treatments that don’t actually work but just got lucky at an early interim analysis.