Michael Charles Sachs

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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

1. 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

2. 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

3. Published

   Flexible evaluation of surrogacy in platform studies

   Sachs, Michael, Gabriel, Erin Evelyn, Crippa, A. & Daniels, M. J., 2023, In: Biostatistics. 25, 1, p. 220–236 17 p.

   Research output: Contribution to journal › Journal article › Research › peer-review