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Mireille E Schnitzer

Showing results (1-10 of 71) with videos related to

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Epidemiology (Cambridge, Mass.)|March 3, 2022
Estimands and Estimation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Connections to Causal InferenceMireille E Schnitzer
Pharmacology Research & Perspectives|September 28, 2018
Methods for the assessment of selection bias in drug safety during pregnancy studies using electronic medical dataMireille E Schnitzer, Lucie Blais
Statistics in Medicine|November 3, 2017
Collaborative targeted learning using regression shrinkageMireille E Schnitzer, Matthew Cefalu
Statistics in Medicine|August 20, 2020
A tutorial on dealing with time-varying eligibility for treatment: Comparing the risk of major bleeding with direct-acting oral anticoagulants vs warfarinMireille E Schnitzer, Robert W Platt, Madeleine Durand
The International Journal of Biostatistics|July 31, 2015
Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal InferenceMireille E Schnitzer, Judith J Lok, Susan Gruber
Epidemiology (Cambridge, Mass.)|March 7, 2020
Importance of Homogeneous Effect Modification for Causal Interpretation of Meta-analysesRussell J Steele, Mireille E Schnitzer, Ian Shrier
Biostatistics (Oxford, England)|July 31, 2015
Double robust and efficient estimation of a prognostic model for events in the presence of dependent censoringMireille E Schnitzer, Judith J Lok, Ronald J Bosch
The New England Journal of Medicine|September 8, 2021
Covid-19 Vaccine Effectiveness and the Test-Negative DesignNatalie E Dean, Joseph W Hogan, Mireille E Schnitzer
Statistics in Medicine|February 22, 2025
A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Analysis of Québec Administrative DataCong Jiang, Denis Talbot, Sara Carazo, et al.
Respiratory Medicine|May 20, 2022
Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning modelTianze Jiao, Mireille E Schnitzer, Amélie Forget, et al.
Pageof 8

Showing results (1-10 of 71) with videos related to

Sort By:
Pageof 8
Epidemiology (Cambridge, Mass.)|March 3, 2022
Estimands and Estimation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Connections to Causal InferenceMireille E Schnitzer
Pharmacology Research & Perspectives|September 28, 2018
Methods for the assessment of selection bias in drug safety during pregnancy studies using electronic medical dataMireille E Schnitzer, Lucie Blais
Statistics in Medicine|November 3, 2017
Collaborative targeted learning using regression shrinkageMireille E Schnitzer, Matthew Cefalu
Statistics in Medicine|August 20, 2020
A tutorial on dealing with time-varying eligibility for treatment: Comparing the risk of major bleeding with direct-acting oral anticoagulants vs warfarinMireille E Schnitzer, Robert W Platt, Madeleine Durand
The International Journal of Biostatistics|July 31, 2015
Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal InferenceMireille E Schnitzer, Judith J Lok, Susan Gruber
Epidemiology (Cambridge, Mass.)|March 7, 2020
Importance of Homogeneous Effect Modification for Causal Interpretation of Meta-analysesRussell J Steele, Mireille E Schnitzer, Ian Shrier
Biostatistics (Oxford, England)|July 31, 2015
Double robust and efficient estimation of a prognostic model for events in the presence of dependent censoringMireille E Schnitzer, Judith J Lok, Ronald J Bosch
The New England Journal of Medicine|September 8, 2021
Covid-19 Vaccine Effectiveness and the Test-Negative DesignNatalie E Dean, Joseph W Hogan, Mireille E Schnitzer
Statistics in Medicine|February 22, 2025
A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Analysis of Québec Administrative DataCong Jiang, Denis Talbot, Sara Carazo, et al.
Respiratory Medicine|May 20, 2022
Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning modelTianze Jiao, Mireille E Schnitzer, Amélie Forget, et al.
Pageof 8