Our new paper in @EJCI_News argues that Randomized trials are necessary in medicine & PH for interventions w putative benefit & at best MED to LG effect size.

Parachutes & smoking are not good counter examples

Here is the explanation 🧵
onlinelibrary.wiley.com/doi/abs/10.111…
Some people argue that b/c we did not need RCTs to know smoking is harmful or Parachutes are life saving, we don't need them to test cloth masking, or the Impella, or some new cancer drug, or HCQ, or <insert ur favorite practice>

But this is based on misunderstanding
There is a huge range of things we can do to someone that might hurt them or save them, imagine the spectrum (below)

Let's start on the harms side
At most extreme, you could shoot someone in the chest at point blank range or throw them off a cliff

There has never been an RCT that doing that is harmful.

Wow, who knew?!?

But we know it is harmful. The effect size is massive. Near certain death.
Now, look at the right most edge

There is no RCT that pulling someone out of the way of a speeding truck is life saving, & no (non-humorous) RCT of parachutes but again the effect size is massive.

Visible to naked eye
Now look to the middle left.

Smoking, pollutants in the water, carcinogens in food-- none of these have RCTs showing they are harmful, and we generally do no not run RCTs for putative harms
We draw upon risk factor epi & make a determination that mitigation is reasonable.

If we wish, we can subject smoking CESSATION strategies to an RCT. You can power trials for smoking reduction, but there is no rule that says you can't power them for all cause mortality
By doing that, you immediate move to the right side of the mid-point; the green arrow

you have an intervention that possibly offers a modest to marginal effect size.

Turns out that is where most of biomedicine lies

Or what we call "The RCT Zone"
Here, RCTs are desperately needed to separate true effect from hope & wishful thinking & propaganda

Early in the pandemic, some opposed cloth mask RCTs saying that cloth masks were like a parachute

Seemed farfetched to me, and now
That was a terrible decision
Of course their effect on the primary endpoint in Bangladesh was 0%. Cloth failed. Surgical had 11% RRR (but open Qs)

Regardless, RCTs were not only possible, they were desperately needed; the effect size was at best modest but possibly null, and RCTs work well to separate
True effects from wishful thinking

If we had more RCTs of masking-- particularly kids in school--- we would end a bitter debate that is driven by low credibility data

medpagetoday.com/opinion/vinay-…
Of course many believe that high quality observational studies like 'target trials' will emulate RCTs

I talk about that more in this thread & paper last year
Closing thoughts:

Most of the time people say you can't do an RCT-- they have little conceptual clarity on what they are saying.

For interventions with at best med-lg effect sizes (not massive effect size), you usually need to do an RCT

We discuss much more in our new paper
Check it out here:
onlinelibrary.wiley.com/doi/abs/10.111…

And if you enjoyed this, follow @vkprasadlab for more updates regarding our research

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More from @VPrasadMDMPH

21 Dec
In our new paper in @JAMANetworkOpen we take a deep look into cost-effectiveness (CEA) studies of cancer drugs

Bottom line: If a CEA study is funded by pharma, it is 40x (OMG!) more likely to find the drug is cost effective

A 🧵 explaining what we found
jamanetwork.com/journals/jaman…
For every cancer drug indication approved between 2015-20, we searched for cost-effectiveness studies

we found between 0 and 9 per drug!!

Some trials were industry sponsored & others neutral
Here are the baseline characteristics of the studies we looked it.

Only 1/2 to 2/3 of drugs have even shown they improve survival

The rest have unknown effects on survival

That is not good enough

It is FDA failure! (these days that's common)
Read 8 tweets
18 Dec
Swimmers, Spider, & Waterfall Plots are everywhere in Oncology

Led by @mlythoe & Olivier
We offer an improvement in our new paper
The Iceberg Plot

Let me explain why it is preferable & teach you about all 4....
[Tweetorial]
ejcancer.com/article/S0959-… Image
All other plots we use in oncology
Tell you what happens AFTER you start the protocol

A swimmers plot shows when treatment was given, and when response and progression occurred for each individual Image
A spider plot shows the tumor size for each patient, every time they were assessed, over time. Image
Read 11 tweets
16 Dec
Now out in @EJCI_News
Logan Powell & I show where randomized trials necessary

When people say 'we don't need an RCT of smoking (to prove harm) or parachutes (to prove benefit)' does that apply to widespread medical interventions?
🧵
onlinelibrary.wiley.com/doi/10.1111/ec…
2
Read 4 tweets
14 Dec
Led by Timothee Olivier, our new paper is now out at @JAMANetworkOpen

We analyze 12 years of FDA approvals, and do the hard work of sorting them into
New Mechanism of Action (MOA)
& New MOA for that tumor type
Vs next in class

jamanetwork.com/journals/jaman…
First we find, more drug approvals over time!

More approvals means more innovation, right?
Next we show how many drug approvals are truly innovative

The dark bar shows the first approval of a new MOA across tumor types, or within a tumor type (bottom pane)

(bottom pane) the brown bar is moving to an earlier line
light blue = next in class
Read 7 tweets
11 Dec
Few prelim thoughts on this trial (from quick read)
#ASH21
1. It is not a 'second line' trial, it is a trial in the worst subset of second line pts & cannot extrapolate beyond

Primary refractory & relapse <12 mo

(TBH, a lot of people doing this already) Image
As such, it should not generalize to relapse > 12 months

2. That said, for those included, axi-cel seems preferable to chemo then auto; I am not surprised this is true in the most chemo insensitive biology. But a few more thoughts Image
3. This is Wrong, you are not supposed to do this 👇👇
Standard practice is to take these pts to CAR-T if needed in the control arm; thus, you must compare routine, upfront CAR-T to using CAR-T as salvage when indicated and standard of care.

And you don't adjust for it Image
Read 9 tweets
30 Nov
Every time you add a dose of vax
from 1 to 2
2 to 3
3 to 4

you have some increased risk of myocarditis leading to hospitalization (for sure)

& possibly, some lower risk of being very sick with covid

How do we weigh these?
🧵
Of course good vaccine approvals occur when:

the reduction in risk of bad covid outcomes from getting 1 more dose is
> (Greater than)
the risk of bad vaccine outcomes from getting 1 more dose

This must be re-calculated with each dose
There is uncertainty around both estimates

We know the rate of myocarditis after dose 2 in these ages (1 in 5-10k), but not dose 3

We know the risk of hospitalization at these ages among unvaccinated

That risk falls with 1 dose; it falls a bit more with 2
Read 13 tweets

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