Noemi Kreif PhD
- Noemi Kreif's Contacts
- 15-17 Tavistock Place
- WC1H 9SH
- T: 020 7 927 2781
Noemi Kreif is a lecturer in health economics. She has been awarded a 3 years UK Medical Research Council Early Career Fellowship in the economics of health, on improving statistical methods to address confounding in the economic evaluation of health interventions. Her current work focuses on translating advanced causal inference methods to health economic evaluation and decision modelling, in the complex settings of longitudinal confounding.
I am a seminar leader for the MSc courses: Economic Evaluation, and Introduction to Health Economics, at the LSHTM.
I also co-organise the LSHTM short course "Methods for addressing selection bias in health economic evaluation" ( http://www.lshtm.ac.uk/study/cpd/smasbhe.html ).
I am interested in applying and extending approaches from the causal inference literature, to the economic evaluation of non-randomised health interventions and policies. My current research areas include translating advanced causal inference methods, such as targeted maximum likelihood estimation, to health economic evaluation in complex settings with longitudinal confounding. My previous research include the extension of the generalised propensity score method with machine learing, and the use of the synthetic control approach to evaluate health policies. My PhD, linked to the ESRC funded project (RES-061-25-0343), compared statistical methods for addressing selection bias in economic evaluations that use patient-level observational data.
Please see related research outputs from the project:
You can also find my list of publications under my Academia profile..
.. and my refereeing activity:
- Economic evaluation
- Health technology assessment
- Statistical methods
Examination of the synthetic control method for evaluating health policies with multiple treated units
Kreif, N; Grieve, R; Hangartner, D; Turner, AJ; Nikolova, S; Sutton, M;
Health Economics, In Press;
Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.
Kreif, N.; Grieve, R.; Díaz, I.; Harrison, D.;
Health Econ, 2015; 24(9):1213-28
Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.
Kreif, N.; Gruber, S.; Radice, R.; Grieve, R.; Sekhon, J.S.;
Stat Methods Med Res, 2014;
Overview of Parametric Survival Analysis for Health-Economic Applications
Ishak, K. J.; Kreif, N.; Benedict, A.; Muszbek, N.
Pharmacoeconomics, 2013; 31(8):663-675
Regression-adjusted matching and double-robust methods for estimating average treatment effects in health economic evaluation
Kreif, N.; Grieve, R.; Radice, R.; Sekhon, J.S.
Health Services and Outcomes Research Methodology, 2013; 13(2-4):174-202
Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data.
Kreif, N. ; Grieve, R. ; Radice, R. ; Sadique, Z. ; Ramsahai, R. ; Sekhon, J.S. ;
Med Decis Making, 2012; 32(6):750-63
Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach.
Radice, R. ; Ramsahai, R. ; Grieve, R. ; Kreif, N. ; Sadique, Z. ; Sekhon, J.S. ;
Int J Biostat, 2012; 8(1):25
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