Michael Charles Sachs
Associate Professor
Section of Biostatistics
Øster Farimagsgade 5 opg. B
1353 København K
Primary fields of research
The development and evaluation of biomarker signatures for treatment selection, computational methods and interfaces for deriving symbolic bounds on causal effects, and regression modeling of cumulative estimands with censored event history data.
General areas of interest: Reproducible research, computational statistics, R programming, Bayesian nonparametric models, survival analysis.
Teaching
"Programming and Statistical Modeling in R". Course webpage: https://sachsmc.github.io/r-programming/
Current research
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Selected publications
Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R
Sachs, Michael & Gabriel, Erin Evelyn, 2022, In: Journal of Statistical Software. 102, 9, p. 1-34 34 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
A General Method for Deriving Tight Symbolic Bounds on Causal Effects
Sachs, Michael, Jonzon, G., Sjölander, A. & Gabriel, Erin Evelyn, 2023, In: Journal of Computational and Graphical Statistics. 32, 2, 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
- E-pub ahead of print
Flexible evaluation of surrogacy in platform studies
Sachs, Michael, Gabriel, Erin Evelyn, Crippa, A. & Daniels, M. J., 2023, (E-pub ahead of print) In: Biostatistics. 17 p.Research output: Contribution to journal › Journal article › Research › peer-review
ID: 303495478
Most downloads
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11
downloads
A General Method for Deriving Tight Symbolic Bounds on Causal Effects
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
10
downloads
Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R
Research output: Contribution to journal › Journal article › Research › peer-review