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Vinay Prasad, MD MPH 🎙️📷

21 Dec, 8 tweets, 3 min read

@JAMANetworkOpen

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)

In descriptive analysis, 96% of industry sponsored studies find a drug is cost effective, while this is only true for 30% of non industry sponsored studies

Don't worry! I bet the authors of the 4% of studies sponsored by pharma finding it not cost effective have been fired.

In a multivariate model, being funded by pharma is associated with an odds ratio of 40!!! that a study concludes a drug is cost -effective

This relationship is MASSIVE

Tobacco as a lung carcinogen is only OR 20!!!

Finally, here is a waterfall plot of the incremental cost-effectiveness of drugs BEYOND the threshold

Some cancer drugs are 1 MILLION dollars per QUALY beyond the threshold!!!

This is madness!

We spend so much on drugs that either (a) don't improve survival or quality of life (b) we don't know if they do or (c) do so only marginally

No society can exist funding these drugs

It is untenable

It will bankrupt us all

@vkprasadlab

They say Rome collapsed when half of all days were holidays; perhaps the same fate will happen in the USA when half of GDP is spent on cancer drugs that don't work or work marginally

Read the full paper here:
jamanetwork.com/journals/jaman…

& Follow @vkprasadlab for research updates

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Vinay Prasad, MD MPH 🎙️📷

@VPrasadMDMPH

19 Dec

@EJCI_News

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

Read 14 tweets

Vinay Prasad, MD MPH 🎙️📷

@VPrasadMDMPH

18 Dec

@mlythoe

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-…

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

A spider plot shows the tumor size for each patient, every time they were assessed, over time.

Read 11 tweets

Vinay Prasad, MD MPH 🎙️📷

@VPrasadMDMPH

16 Dec

@EJCI_News

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…

@vkprasadlab

1 @vkprasadlab

Read 4 tweets

Vinay Prasad, MD MPH 🎙️📷

@VPrasadMDMPH

14 Dec

@JAMANetworkOpen

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

Vinay Prasad, MD MPH 🎙️📷

@VPrasadMDMPH

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)

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

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

Read 9 tweets

Vinay Prasad, MD MPH 🎙️📷

@VPrasadMDMPH

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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