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Updated: Apr 3, 2026

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

Statistical inference for the additive hazards model under outcome-dependent sampling.

Jichang Yu¹, Yanyan Liu², Dale P Sandler³

¹School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China.

The Canadian Journal of Statistics = Revue Canadienne De Statistique

|September 18, 2015

Summary

This summary is machine-generated.

This study introduces an outcome-dependent sampling (ODS) design for survival data, improving efficiency and power for cost-effective research. The proposed method offers a more powerful and efficient approach compared to existing designs.

Keywords:

Primary 62D05 additive hazards model inverse probability weight outcome-dependent sampling secondary 62N01

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Area of Science:

Biostatistics
Survival Analysis
Epidemiology

Background:

Cost-effective study designs and robust inference are crucial for investigators analyzing complex data.
Survival data with right censoring presents unique challenges in statistical modeling and analysis.
Existing sampling methods may not be optimal for efficiency in certain survival data scenarios.

Purpose of the Study:

To propose a novel outcome-dependent sampling (ODS) design for survival data with right censoring.
To develop a weighted pseudo-score estimator for regression parameters within the ODS framework.
To evaluate the efficiency and power of the ODS design compared to simple random sampling and other methods.

Main Methods:

Developed an outcome-dependent sampling (ODS) scheme for survival data under the additive hazards model.
Constructed a weighted pseudo-score estimator for regression parameters.
Derived asymptotic properties of the proposed estimator.
Conducted simulation studies to compare the proposed method with existing designs and estimators.

Main Results:

The proposed ODS design demonstrated superior power compared to other existing designs.
The weighted pseudo-score estimator showed higher efficiency than alternative estimators.
Relative efficiency and optimal subsample allocation strategies were evaluated.

Conclusions:

The outcome-dependent sampling (ODS) design offers a more powerful and cost-effective approach for survival data analysis.
The proposed weighted pseudo-score estimator provides a statistically efficient method for parameter estimation.
The ODS design is applicable to real-world epidemiological studies, such as analyzing radon exposure risks in cancer studies.