174 citations · 225 across the 6 of their papers we have counts for
6 papers
The Symmetric Pair Matching Design: A Self-Controlled Method with Automatic Adjustment for Time Effects
Robin Denz, Filippo Saatkamp, Katharina Meiszl +1
Self-controlled study designs eliminate confounding by individual-level characteristics that remain constant during the observation time and are thus widely used in pharmacoepidemi…
Causal Mediation Analysis with a Time-Dependent Mediator, Time-Dependent Confounders and a Time-to-Event Outcome: Revisiting the Difference Method
Robin Denz, Nina Timmesfeld
Mediation analysis is a powerful tool to decompose treatment effects into direct and indirect components, enabling explanations of total treatment effects in a formal statistical f…
adjustedCurves: Estimating Confounder-Adjusted Survival Curves in R
Robin Denz, Nina Timmesfeld
Kaplan-Meier curves stratified by treatment allocation are the most popular way to depict causal effects in studies with right-censored time-to-event endpoints. If the treatment is…
Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study
Robin Denz, Katharina Meiszl, Peter Ihle +5
With unprecedented speed, 192,248,678 doses of Covid-19 vaccines were administered in Germany by July 11, 2023 to combat the pandemic. Limitations of clinical trials imply that the…
Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome
Robin Denz, Nina Timmesfeld
Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization ap…
A Comparison of Different Methods to Adjust Survival Curves for Confounders
Robin Denz, Renate Klaaßen-Mielke, Nina Timmesfeld
Treatment specific survival curves are an important tool to illustrate the treatment effect in studies with time-to-event outcomes. In non-randomized studies, unadjusted estimates…