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Related Experiment Video

Updated: Oct 8, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Causal inference for semi-competing risks data.

Daniel Nevo¹, Malka Gorfine¹

¹Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel.

Biostatistics (Oxford, England)

|December 30, 2021

Summary

This summary is machine-generated.

The Apolipoprotein E epsilon4 allele (APOE) influences Alzheimer's disease (AD) and mortality. This study uses semi-competing risks to analyze APOE's complex causal effects on AD diagnosis and death.

Keywords:

Alzheimer’s disease Bounds Frailty model Illness-death model Principal stratification

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Area of Science:

Biostatistics
Epidemiology
Genetics

Background:

The Apolipoprotein E epsilon4 allele (APOE) is a significant genetic risk factor for late-onset Alzheimer's disease (AD).
Defining the causal impact of APOE on both AD onset and mortality is challenging due to the interplay between these two events.
AD occurrence can influence the age of death, complicating direct causal inference.

Purpose of the Study:

To propose novel statistical methods for estimating the causal effects of APOE on both Alzheimer's disease diagnosis and death.
To address the complexities arising from the semi-competing risks nature of these dual outcomes.
To develop a framework that accounts for the order of AD diagnosis and death.

Main Methods:

Utilizing a semi-competing risks framework for time-to-event data analysis.
Proposing new estimands stratified by the order of AD diagnosis and death.
Introducing a novel assumption leveraging time-to-event data properties, offering more flexibility than traditional monotonicity assumptions.
Developing and implementing nonparametric and semiparametric estimation methods.

Main Results:

Derivation of results on partial identifiability of causal effects.
Development of a sensitivity analysis approach to assess the impact of assumption violations.
Identification of conditions under which full identification of causal effects is achievable.
Presentation of estimation methods for right-censored semi-competing risks data.

Conclusions:

The proposed semi-competing risks framework provides a robust method for disentangling the complex causal relationships between APOE, Alzheimer's disease, and mortality.
The novel estimands and assumptions offer a more nuanced understanding of genetic risk factors in the presence of competing events.
The developed statistical methods facilitate more accurate causal inference in complex epidemiological and genetic studies.