Lockdown measures and relative changes in the age-specific incidence of SARS-CoV-2 in Spain.
D GB, D P, Jm G, B P, C FE, M L, A L, E G, Ma H.
medRxiv. 2020 Jul 02. PMID: 32637975
Member of the Harvard-MIT Health Sciences and Technology Faculty
Health Sciences and Technology
Massachusetts Institute of Technology
Faculty Affiliate in the Department of Biostatistics
Biostatistics
Harvard T.H. Chan School of Public Health
My research is focused on methodology for causal inference, including comparative effectiveness of policy and clinical interventions.
In an ideal world, all policy and clinical decisions would be based on the findings of randomized experiments. For example, public health recommendations to avoid saturated fat or medical prescription of a particular painkiller would be based on the findings of long-term studies that compared the effectiveness of several randomly assigned interventions in large groups of people from the target population that adhered to the study interventions. Unfortunately, such randomized experiments are often unethical, impractical, or simply too lengthy for timely decisions.
My collaborators and I combine observational data, mostly untestable assumptions, and statistical methods to emulate hypothetical randomized experiments. We emphasize the need to formulate well defined causal questions, and use analytic approaches whose validity does not require assumptions that conflict with current subject-matter knowledge. For example, in settings in which experts suspect the presence of time-dependent confounders affected by prior treatment, we do not use adjustment methods (e.g., conventional regression analysis) that require the absence of such confounders.
While causal inferences from observational data are always risky, an appropriate analysis of observational studies often results in the best available evidence for policy or clinical decision-making. At the very least, the findings from well designed and properly analyzed observational studies may guide the design of future randomized experiments.
Our applied work is focused on optimal use of antiretroviral therapy in persons infected with HIV, lifestyle and pharmacological interventions to reduce the incidence of cardiovascular disease, and the effects of erythropoiesis-stimulating agents among dialysis patients.
D GB, D P, Jm G, B P, C FE, M L, A L, E G, Ma H.
medRxiv. 2020 Jul 02. PMID: 32637975
Emilsson L, García-Albéniz X, Hernan MA.
JAMA Oncol. 2018 07 01. 4(7):1016-1017. PMID: 29800046
Huitfeldt A, Hernan MA, Kalager M, Robins JM.
EGEMS (Wash DC). 2016. 4(1):1234. PMID: 27891526
Hernán MA.
Ann Epidemiol. 2016 10. 26(10):674-680. PMID: 27641316
Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I.
J Clin Epidemiol. 2016 11. 79:70-75. PMID: 27237061
Beardsley J, Le T.
Neurology. 2015 Feb 10. 84(6):632. PMID: 25666633
Young JG, Hernan MA, Robins JM.
Epidemiol Methods. 2014 Dec. 3(1):1-19. PMID: 25866704
Hernán MA, Savitz DA.
Epidemiology. 2013 May. 24(3):344-5. PMID: 23549177
Hernán MA.
Stat Med. 2012 Nov 10. 31(25):3060-1; discussion 3066-7. PMID: 23055183
Researchers are now harnessing vast amounts of information to assess what works in medicine and public health.
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