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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

Nonparametric incidence estimation from prevalent cohort survival data.

Marco Carone¹, Masoud Asgharian, Mei-Cheng Wang

¹Division of Biostatistics, School of Public Health, University of California Berkeley, 109 Haviland Hall, Berkeley, California 94720, U.S.A. , mcarone@berkeley.edu.

|July 12, 2013

Summary

This study introduces a new method to estimate disease incidence using prevalent cohort data, which is more feasible than traditional incident cohort studies. The findings help in understanding dementia incidence in the Canadian elderly population.

Keywords:

Age-specific incidence Cross-sectional sampling Left-truncation Point process Stratification

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Area of Science:

Epidemiology
Biostatistics

Background:

Incidence is crucial for epidemiological studies but often challenging to measure directly.
Prevalent cohort studies offer greater feasibility by recruiting existing cases.

Purpose of the Study:

To develop an efficient nonparametric estimator for cumulative incidence using prevalent cohort data.
To analyze the estimator's properties and applicability in epidemiological research.

Main Methods:

Derivation of a nonparametric estimator for cumulative incidence from prevalent cohort data.
Investigation of asymptotic properties and adjustments for temporal variations in survival and incidence.
Application of the method to the Canadian Study of Health and Aging data.

Main Results:

An efficient nonparametric estimator for cumulative incidence was successfully derived and validated.
The method allows for arbitrary calendar time variations in disease incidence and age-specific adjustments.
Analysis of Canadian elderly data provided insights into dementia incidence.

Conclusions:

The developed estimator provides a viable approach for calculating incidence from prevalent cohort studies.
This method enhances the ability to study disease incidence, such as dementia, in specific populations.
The findings have implications for epidemiological research and public health planning.