Mikail Nourredine
@mikailnourredine.bsky.social
MD Psychiatry and PhD Biostatistics working at the Department of Biostatistics @CHUdeLyon @UnivLyon1 @LbbeLyon
#causalinference #indirectcomparisons #clinicaltrial
#causalinference #indirectcomparisons #clinicaltrial
Reposted by Mikail Nourredine
That led to this paper which was accepted in JRSSA. In it, I make the Epidemiology paper a bit more formal, extend to continuous covariates, proposed two new AIPW estimators, and provide a new illustrative example
academic.oup.com/jrsssa/advan...
academic.oup.com/jrsssa/advan...
Synthesis estimators for transportability with positivity violations by a continuous covariate
Abstract. Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct
academic.oup.com
December 27, 2024 at 1:07 PM
That led to this paper which was accepted in JRSSA. In it, I make the Epidemiology paper a bit more formal, extend to continuous covariates, proposed two new AIPW estimators, and provide a new illustrative example
academic.oup.com/jrsssa/advan...
academic.oup.com/jrsssa/advan...
Reposted by Mikail Nourredine
The first paper on using a mathematical model to fill in nonpositive regions was published in Epidemiology in the January issue. This paper lays out the basics in the case of nonpositivity by a binary variable and we proposed IPW and g-computation estimators
journals.lww.com/epidem/abstr...
journals.lww.com/epidem/abstr...
Transportability Without Positivity: A Synthesis of... : Epidemiology
n a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomi...
journals.lww.com
December 27, 2024 at 1:07 PM
The first paper on using a mathematical model to fill in nonpositive regions was published in Epidemiology in the January issue. This paper lays out the basics in the case of nonpositivity by a binary variable and we proposed IPW and g-computation estimators
journals.lww.com/epidem/abstr...
journals.lww.com/epidem/abstr...
Reposted by Mikail Nourredine
Upgrade your #causalinference arsenal.
A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook
Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.
Enjoy the #WhatIfBook plus code and data. Also, it's free.
A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook
Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.
Enjoy the #WhatIfBook plus code and data. Also, it's free.
December 23, 2024 at 9:28 AM
Upgrade your #causalinference arsenal.
A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook
Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.
Enjoy the #WhatIfBook plus code and data. Also, it's free.
A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook
Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.
Enjoy the #WhatIfBook plus code and data. Also, it's free.
Reposted by Mikail Nourredine
People: please don't ML for the sake of ML.
I keep seeing manuscripts using fancy machine learning on brain-imaging data, where, in my opinion (having processed a lot of brain-imaging data), the method is way too complex for the richness of the data.
Fancier is not better per se
I keep seeing manuscripts using fancy machine learning on brain-imaging data, where, in my opinion (having processed a lot of brain-imaging data), the method is way too complex for the richness of the data.
Fancier is not better per se
December 19, 2024 at 9:32 PM
People: please don't ML for the sake of ML.
I keep seeing manuscripts using fancy machine learning on brain-imaging data, where, in my opinion (having processed a lot of brain-imaging data), the method is way too complex for the richness of the data.
Fancier is not better per se
I keep seeing manuscripts using fancy machine learning on brain-imaging data, where, in my opinion (having processed a lot of brain-imaging data), the method is way too complex for the richness of the data.
Fancier is not better per se
Reposted by Mikail Nourredine
📣 Do you want to learn about recent advances in causal inference?
Colleagues at INSERM are organising a workshop gathering international experts in the field. Bonus: it's happening in two amazing locations 🌇🇫🇷
Colleagues at INSERM are organising a workshop gathering international experts in the field. Bonus: it's happening in two amazing locations 🌇🇫🇷
December 18, 2024 at 9:02 AM
📣 Do you want to learn about recent advances in causal inference?
Colleagues at INSERM are organising a workshop gathering international experts in the field. Bonus: it's happening in two amazing locations 🌇🇫🇷
Colleagues at INSERM are organising a workshop gathering international experts in the field. Bonus: it's happening in two amazing locations 🌇🇫🇷