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Aalen's linear model for sampled risk set data: a large sample study.

J Zhang¹, O Borgan

¹Department of Statistics, University of Chicago, IL 60637, USA.

Lifetime Data Analysis

|January 29, 2000

Summary

This study develops an estimator for Aalen's linear hazard regression model using sampled risk data. It analyzes the estimator's properties and compares study design efficiencies.

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Area of Science:

Biostatistics
Survival Analysis
Epidemiological Methods

Background:

Aalen's linear hazard regression model is used for survival data analysis.
Estimating parameter functions from sampled risk set data presents challenges.
Previous methods by Borgan and Langholz (1997) provide a foundation.

Purpose of the Study:

To develop and analyze an estimator for parameter functions in Aalen's model using sampled risk set data.
To investigate the asymptotic distributional properties of this novel estimator.
To compare the efficiency of nested case-control and counter-matched designs against full cohort analysis.

Main Methods:

Utilizing a counting process formulation for survival data.
Applying the martingale central limit theorem to derive asymptotic properties.
Employing simulation studies or theoretical analysis to assess estimator performance.

Main Results:

The proposed estimator is shown to have desirable asymptotic distributional properties.
The study quantifies the relative efficiencies of different epidemiological study designs.
Specific performance characteristics of the estimator under various conditions are detailed.

Conclusions:

The developed estimator provides a valid approach for analyzing sampled risk set data within Aalen's model.
Understanding the asymptotic properties is crucial for reliable inference.
The findings offer guidance on selecting efficient study designs for epidemiological research.

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