Discover and read the best of Twitter Threads about #TargetTrial
Most recents (9)
1/
One day everyone will recognize #selectionbias due to a #collider and the world will be a better place.
This time observational studies found a higher risk of omicron reinfection after a 3rd dose of #COVID19 vaccine. As usual, alarms went off.
Can you see the obvious bias?
One day everyone will recognize #selectionbias due to a #collider and the world will be a better place.
This time observational studies found a higher risk of omicron reinfection after a 3rd dose of #COVID19 vaccine. As usual, alarms went off.
Can you see the obvious bias?
2/
Those who receive a booster and get infected are, on average, more susceptible to infection than those who don't receive a booster and get infected.
So no surprise than those who receive a booster and get infected are more likely to get reinfected.
Led by @susanamcorella...
Those who receive a booster and get infected are, on average, more susceptible to infection than those who don't receive a booster and get infected.
So no surprise than those who receive a booster and get infected are more likely to get reinfected.
Led by @susanamcorella...
3/
... we described the bias @bmj_latest with simulations + real data.
We show that a vaccine booster will be associated with higher reinfection risk even if the booster has no harmful effect.
Now the good news: Preventing this #selectionbias is easy...
bmj.com/content/381/bm…
... we described the bias @bmj_latest with simulations + real data.
We show that a vaccine booster will be associated with higher reinfection risk even if the booster has no harmful effect.
Now the good news: Preventing this #selectionbias is easy...
bmj.com/content/381/bm…
1/
Our findings on a fourth dose (2nd booster) of the Pfizer-BioNTech #COVID19 vaccine are now published.
Compared with 3 doses only, a fourth dose had 68% effectiveness against COVID-19 hospitalization during the Omicron era in persons over 60 years of age.
Interestingly...
Our findings on a fourth dose (2nd booster) of the Pfizer-BioNTech #COVID19 vaccine are now published.
Compared with 3 doses only, a fourth dose had 68% effectiveness against COVID-19 hospitalization during the Omicron era in persons over 60 years of age.
Interestingly...
2/
... this is yet another example of the need for good #observational studies that emulate a #TargetTrial.
Would it be better to have a real randomized trial? Yes
Do we have a randomized trial? No
Will we have a randomized trial? Perhaps, but too late for a timely decision.
... this is yet another example of the need for good #observational studies that emulate a #TargetTrial.
Would it be better to have a real randomized trial? Yes
Do we have a randomized trial? No
Will we have a randomized trial? Perhaps, but too late for a timely decision.
3/
Last year, observational evidence was also used to recommend a first vaccine booster.
Our and others' studies provided evidence on the booster's protection against hospitalization after infection with Delta:
Policy makers listened. Lives were saved...
Last year, observational evidence was also used to recommend a first vaccine booster.
Our and others' studies provided evidence on the booster's protection against hospitalization after infection with Delta:
Policy makers listened. Lives were saved...
I am pleased & proud to see the #PLUStrial published in @NEJM today. It is my 10th @NEJM paper! Here’s my list of @NEJM papers and what I think they mean for clinical practice
#1: The #HEATtrial (Dec 2015) showed that using paracetamol to treat fever in ICU patients with suspected infections did not affect the number of ICU-free days. Mortality rates were similar in paracetamol & placebo-treated patients:
nejm.org/doi/full/10.10…
nejm.org/doi/full/10.10…
These data provide reassurance that if paracetamol is administered to treat fever in the setting of an infection or given for another reason to a patient who happens to have fever & infection, such as for analgesia, harm is unlikely to result.
@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.
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...
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...
... 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...
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.
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.
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.
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.
#Causalinference that talks the talk and walks the walk.
Claim: "Continuing #breastcancer screening past age 75 doesn't reduce 8-year breast cancer mortality."
Emulation of a #TargetTrial led by @xabieradrian with Medicare data
doi.org/10.7326/M18-11…
Let the discussion start.
Claim: "Continuing #breastcancer screening past age 75 doesn't reduce 8-year breast cancer mortality."
Emulation of a #TargetTrial led by @xabieradrian with Medicare data
doi.org/10.7326/M18-11…
Let the discussion start.
@xabieradrian @AnnalsofIM @HarvardEpi @harvard_data @HarvardBiostats @HarvardChanSPH @CMSGov @CMSgovPress @MonganInstitute @MassGeneralNews 2)
Because there's so much talk about #causalinference around here.
Computer scientists, economists, statisticians... talk a lot about the merits of #DeepLearning, instrumental variables, or whatever their preferred methodology is.
Everybody: This is your chance to shine.
Because there's so much talk about #causalinference around here.
Computer scientists, economists, statisticians... talk a lot about the merits of #DeepLearning, instrumental variables, or whatever their preferred methodology is.
Everybody: This is your chance to shine.
3)
No more toy examples. A real world question:
"At what age should #breastcancer screening stop?"
Need to compare the mortality of women under two dynamic screening strategies using a database of insurance claims with time-varying treatments and confounders.
How'd you do it?
No more toy examples. A real world question:
"At what age should #breastcancer screening stop?"
Need to compare the mortality of women under two dynamic screening strategies using a database of insurance claims with time-varying treatments and confounders.
How'd you do it?
Hello #epitwitter! Time for an @epiellie @AmJEpi tweetorial.
Today’s topic is the Target Trial Framework for #causalinference and how to apply it to improving observational studies.
#epiellie
Today’s topic is the Target Trial Framework for #causalinference and how to apply it to improving observational studies.
#epiellie
So, what is the #targettrial framework?
Well it’s not a new method! Instead think of it as pedagogical device that provides a structured way to build your research question and study design for observational studies and minimizes the potential for bias.
Well it’s not a new method! Instead think of it as pedagogical device that provides a structured way to build your research question and study design for observational studies and minimizes the potential for bias.
What does that mean?
To design an observational study, we first think about what the ideal hypothetical randomized trial (target trial) is that would let us answer our research question.
Then, we try to match our observational study as closely as possible to that trial design.
To design an observational study, we first think about what the ideal hypothetical randomized trial (target trial) is that would let us answer our research question.
Then, we try to match our observational study as closely as possible to that trial design.
Results of the #TARGETtrial, the largest critical care nutrition trial ever undertaken, are now online @NEJM
nejm.org/doi/full/10.10…
What we did, what we found, and what it means follows…
Please RT to help translate this new knowledge.
nejm.org/doi/full/10.10…
What we did, what we found, and what it means follows…
Please RT to help translate this new knowledge.
Thanks to funding from @HRCNewZealand & @NHMRC we randomised 4000 participants from 46 Australian and New Zealand ICUs in less than a year and a half!
Adults mechanically ventilated & expected to require enteral nutrition in ICU beyond the calendar day after randomisation were assigned to energy dense enteral nutrition (1.5kcal/mL) or standard care enteral nutrition (1.0kcal/mL) at a dose of 1mL/kg/hr based on ideal body weight
Judea Pearl has a new book (with Dana Mackenzie).
amazon.com/Book-Why-Scien…
To me, Judea is an intellectual hero. My life changed after hearing him at Harvard over 20 years ago. Like many of us in #causalinference, I owe so much to him.
And yet I disagree with him on a key issue.
amazon.com/Book-Why-Scien…
To me, Judea is an intellectual hero. My life changed after hearing him at Harvard over 20 years ago. Like many of us in #causalinference, I owe so much to him.
And yet I disagree with him on a key issue.
Pearl believes that any causal effect we can name must also exist.
To him, the meaning of “the causal effect of A on death” is self-evident. He says we can quantify, say, the causal effect of race or the causal effect of obesity.
I don't think we can.
To him, the meaning of “the causal effect of A on death” is self-evident. He says we can quantify, say, the causal effect of race or the causal effect of obesity.
I don't think we can.
We cannot estimate "the causal effect of obesity" because we don't know what that means.
For the causal effect of A to be well defined, we need a common understanding of the interventions that we would use to change A. Otherwise, the effect is undefined.
ncbi.nlm.nih.gov/pmc/articles/P…
For the causal effect of A to be well defined, we need a common understanding of the interventions that we would use to change A. Otherwise, the effect is undefined.
ncbi.nlm.nih.gov/pmc/articles/P…
