36 citations · 39 across the 8 of their papers we have counts for
8 papers
Deriving Complete Constraints in Hidden Variable Models
Michael C. Sachs, Erin E. Gabriel, Robin J. Evans +1
Hidden variable graphical models can sometimes imply constraints on the observable distribution that are more complex than simple conditional independence relations. These observab…
Nonparametric Bounds for Evaluating the Clinical Utility of Treatment Rules
Johannes Hruza, Erin Gabriel, Arvid Sjölander +2
Evaluating the value of new clinical treatment rules based on patient characteristics is important but often complicated by hidden confounding factors in observational studies. Sta…
On the limitations of causal inference with current-treatment Cox models
Mark B. Knudsen, Erin E. Gabriel, Torben Martinussen +2
Cox models with time-varying treatments often include only the current treatment level. Translating such a model into causally meaningful intervention-specific survival probabiliti…
Definition, Identification, and Estimation of the Direct and Indirect Number Needed to Treat
Valentin Vancak, Arvid Sjölander
The number needed to treat (NNT) is an efficacy and effect size measure commonly used in epidemiological studies and meta-analyses. The NNT was originally defined as the average nu…
Estimation of the Number Needed to Treat, the Number Needed to be Exposed, and the Exposure Impact Number with Instrumental Variables
Valentin Vancak, Arvid Sjölander
The Number needed to treat (NNT) is an efficacy index defined as the average number of patients needed to treat to attain one additional treatment benefit. In observational studies…
Sensitivity Analysis of G-estimators to Invalid Instrumental Variables
Valentin Vancak, Arvid Sjölander
Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effe…
Dealing with multiple testing: To adjust or not to adjust
Yudi Pawitan, Arvid Sjölander
Multiple testing problems arise naturally in scientific studies because of the need to capture or convey more information with more variables. The literature is enormous, but the e…
Analysis of 1:1 Matched Cohort Studies and Twin Studies, with Binary Exposures and Binary Outcomes
Arvid Sjölander, Anna L. V. Johansson, Cecilia Lundholm +3
To improve confounder adjustments, observational studies are often matched on potential confounders. While matched case-control studies are common and well covered in the literatur…