1/
We emulated a target trial of two #COVID19 mRNA vaccines in the largest healthcare system in the US.

Both vaccines were similarly effective, with Moderna slightly better than Pfizer-BioNTech.

But that isn't the most important conclusion of our study.

2/
Spring of 2020: #COVID19 vaccines are developed.

October 2020: Results from randomized trial are announced.
businesswire.com/news/home/2021…

~6 months from development to evaluation of effectiveness.

Utterly impressive. Unprecedented.

Kudos to the pharmaceutical industry.

Now...
3/
December 2020: Vaccines become available.

December 2021: Where are the big randomized trials for COMPARATIVE effectiveness?

1 year, still *crickets*

Billions of taxpayer dollars and we don’t get to know which vaccine is better and safer?

Not so impressive, pharma industry.
4/
Our approval system provides incentives to determine whether new pharmaceutical products work compared with NOTHING.

After approval, unclear who is responsible for determining how well a product works compared with existing ones.

Despite high stakes in terms of lives and $.
5/
So that’s our paper's main conclusion.

Not so much that Moderna works a bit better than Pfizer-BioNTech (both vaccines are quite effective).

But that we need to invest in observational #causalinference because we can’t expect randomized trials will be conducted when needed.
6/
👏👏👏to @VAResearch for

maintaining databases to guide evidence-based decision-making in a public health crisis

supporting the #VACAUSAL Methods Core, a collaboration of @VABostonHC and Harvard's @CAUSALab, for state-of-the art comparative effectiveness/safety research.

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More from @_MiguelHernan

25 Aug
1/
Vaccine safety: We compared excess adverse events after #COVID19 vaccination (Pfizer-BioNTech) and after documented #SARSCoV2 infection.

nejm.org/doi/full/10.10…

Take-home message: Low excess risk of adverse events after vaccination, higher after infection.

Some thoughts👇
2/
Preferring #SARSCoV2 infection over vaccination has become even harder. (Remember: infection also increases the risk of severe disease/death)

This is a good illustration of how #randomized trials and #observational studies complement each other for better #causalinference...
3/
The original #randomized trial estimated vaccine effectiveness to prevent symptomatic infection, but was too small to quantify vaccine safety.

That's what #observational studies do.

Now a different sort of question: Why could we do this study in the first place?

2 reasons.
Read 8 tweets
18 Mar
1/

Many countries are vaccinating their elderly. Can we relax control measures now?

No.

Even with 50% of elderly vaccinated, uncontrolled #SARSCOV2 transmission may overrun the healthcare system.

We explain @AMJPublicHealth today, led by @_gmales
ajph.aphapublications.org/doi/10.2105/AJ…
2/

Data from Madrid, Spring 2020:

Critical care requirements peaked at 5 times the usual capacity.

Hospitals managed to increase ICU capacity by 3-fold.

Heroic but, sadly, insufficient.

The healthcare system collapsed. Not everybody who needed critical care received it.
3/

If 50% of the elderly had been vaccinated, critical care requirements still would have peaked at almost 4 times the usual capacity.

Greater than the ICU capacity of any country in the world.

Only a prolonged lockdown could return ICU requirements to normal.

One last thing:
Read 4 tweets
12 Mar
@ProfMattFox 1/
The odds ratio from a case-control study is an unbiased estimator of the

a. odds ratio in the underlying cohort when we sample controls among non-cases

b. rate ratio in the underlying cohort when we use with incidence density sampling

No rare outcome assumption required.
@ProfMattFox 2/
Because the odds ratio is approximately equal to the risk ratio when the outcome is rare, the odds ratio from a case-control study approximates the risk ratio in the underlying cohort when we sample controls among non-cases and the outcome is rare.

But...
@ProfMattFox 3/
... for an unbiased estimator of the risk ratio (regardless of the outcome being rare), we need a case-base design, not a classical case-control design.

Of course, all of the above only applies to time-fixed treatments or exposures.

As for the causal interpretation...
Read 5 tweets
5 Mar
1/
We recently confirmed the effectiveness of the Pfizer-BioNTech vaccine outside of randomized trials @NEJM.
nejm.org/doi/full/10.10…

Studies like ours are being used to promote a vaccine passport to travel in the US, UK, and European Union.

A few clarifications are in order.
2/
Before we start, a disclaimer:

Vaccine passports involve complex ethical, economic, and political considerations.

Here I talk exclusively about scientific issues. The goal is that those making decisions have a better understanding of what we do and do not know as of today.
3/
Based on our study, we can say confidently that the vaccine is highly effective in preventing you from getting sick with #COVID19.

Based on our study, we can't say confidently that the vaccine is highly effective in preventing you from getting infected and infecting others.
Read 9 tweets
24 Feb
1/
We've just confirmed the effectiveness of the Pfizer-BioNTech vaccine outside of randomized trials.

Details @NEJM: nejm.org/doi/full/10.10…

Yes, great news, but let's talk about methodological issues that arise when using #observational data to estimate vaccine effectiveness.
2/
A critical concern in observational studies of vaccine effectiveness is #confounding:

Suppose that people who get vaccinated have, on average, a lower risk of infection/disease than those who don't get vaccinated.

Then, even if the vaccine were useless, it'd look beneficial.
3/
To adjust for confounding:

We start by identifying potential confounders.

For example: Age
(vaccination campaigns prioritize older people and older people are more likely to develop severe disease)

Then we choose a valid adjustment method. In our paper, we matched on age.
Read 12 tweets
27 Nov 20
1/ Estimating the infection fatality risk (IFR) of #SARSCoV2 is hard.

Our estimates from Spain's #ENECOVID (just published):
Men: 1.1% to 1.4%
Women: 0.58% to 0.77%

After age 80
Men: 12% to 16%
Women: 4.6% to 6.5%

Why is the #IFR hard to estimate?
bmj.com/content/371/bm…
2/ The IFR in a population is the ratio of

number of deaths from SARS-CoV-2 infection (numerator)

and

number of individuals infected by SARS-CoV-2 (denominator)

during a prespecified period.

Both numerator and denominator are hard to quantify.
3/ Why is the denominator hard to quantify?

The number of infected with #SARSCoV2 is not the number of confirmed #COVID19 cases.

Because many infected individuals never have symptoms or have minor symptoms and are never diagnosed.

(For details, see journals.plos.org/plosntds/artic…)
Read 6 tweets

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