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Causal inference for semi-competing risks data.

Daniel Nevo1, Malka Gorfine1

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

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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 diseaseBoundsFrailty modelIllness-death modelPrincipal 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.