Related Experiment Video
Updated: Jan 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Revisiting Hazard Ratios: Can We Define Causal Estimands for Time-Dependent Treatment Effects?
1Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.
None:
In this paper, some aspects concerning the causal interpretation of hazard contrasts are revisited. It is first investigated, in which sense the hazard ratio constitutes a causal effect. It is demonstrated that the hazard ratio at a timepoint represents a causal effect for the population at baseline, but in general not for any population at risk at time . Moreover, the scenario is studied, in which the survival curves coincide up to some timepoint and then separate. This investigation provides valuable insight both on the causal interpretation of the conventional hazard ratio and on properties of the recently proposed causal hazard ratio. The findings suggest that, without making further assumptions, there is in general no meaningful estimand for a treatment effect at time . It is therefore advocated to develop alternative estimands grounded in medically plausible assumptions about the joint distribution of counterfactual survival times.
More Related Videos
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
08:36Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
Published on: April 19, 2024
Related Concept Videos
Comparing the Survival Analysis of Two or More Groups
Hazard Ratio
For example, in a clinical trial...
Hazard Rate
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Assumptions of Survival Analysis
Kaplan-Meier Approach