12 citations · 13 across the 8 of their papers we have counts for
9 papers
Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes
Erin E Gabriel, Michael C Sachs, Andreas Kryger Jensen
The probability of benefit is a valuable and important measure of treatment effect, which has advantages over the average treatment effect. Particularly for an ordinal outcome, it…
Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface
Gustav Jonzon, Michael C Sachs, Erin E Gabriel
Strong untestable assumptions are almost universal in causal point estimation. In particular settings, bounds can be derived to narrow the possible range of a causal effect. Symbol…
Flexible evaluation of surrogacy in Bayesian adaptive platform studies
Michael C Sachs, Erin E Gabriel, Alessio Crippa +1
Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results.…
Sharp nonparametric bounds for decomposition effects with two binary mediators
Erin E Gabriel, Michael C Sachs, Arvid Sjölander
In randomized trials, once the total effect of the intervention has been estimated, it is often of interest to explore mechanistic effects through mediators along the causal pathwa…
Nonparametric bounds for causal effects in imperfect randomized experiments
Erin E. Gabriel, Arvid Sjölander, Michael C. Sachs
Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments making the intervention effect that the experiment was designed to estimate nonide…
A General Method for Deriving Tight Symbolic Bounds on Causal Effects
Michael C Sachs, Gustav Jonzon, Arvid Sjölander +1
A causal query will commonly not be identifiable from observed data, in which case no estimator of the query can be contrived without further assumptions or measured variables, reg…
Causal bounds for outcome-dependent sampling in observational studies
Erin E. Gabriel, Michael C. Sachs, Arvid Sjölander
Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs of…
Aim for clinical utility, not just predictive accuracy
Michael C Sachs, Arvid Sjölander, Erin E Gabriel
The predictions from an accurate prognostic model can be of great interest to patients and clinicians. When predictions are reported to individuals, they may decide to take action…
Ensemble Prediction of Time to Event Outcomes with Competing Risks: A Case Study of Surgical Complications in Crohn's Disease
Michael C Sachs, Andrea Discacciati, Åsa Everhov +2
We develop a novel algorithm to predict the occurrence of major abdominal surgery within 5 years following Crohn's disease diagnosis using a panel of 29 baseline covariates from th…