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Related Experiment Video

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Pseudo-partial likelihood for proportional hazards models with biased-sampling data.

Wei Yann Tsai¹

¹Department of Biostatistics , Columbia University , New York, New York 10032 , U.S.A. wt5@columbia.edu.

|March 17, 2012

Summary

This study introduces a pseudo-partial likelihood method for proportional hazards models using biased-sampling data. This approach handles complex data scenarios, improving statistical analysis for biased samples.

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Area of Science:

Statistics
Biostatistics
Survival Analysis

Background:

Biased-sampling data presents challenges in proportional hazards modeling.
Standard survival analysis methods may not be suitable for biased or truncated datasets.

Purpose of the Study:

To develop a robust statistical method for proportional hazards models with biased-sampling data.
To derive the asymptotic properties of the proposed estimator.

Main Methods:

Embedding biased-sampling data into a left-truncated data framework.
Defining a pseudo-partial likelihood based on the expectation of the log partial likelihood.
Deriving asymptotic properties for the maximum pseudo-partial likelihood estimator.

Main Results:

A novel pseudo-partial likelihood is formulated for biased-sampling data.
Asymptotic properties of the resulting estimator are theoretically established.
The method is applicable to various biased data scenarios.

Conclusions:

The proposed pseudo-partial likelihood offers a viable approach for proportional hazards models with biased-sampling data.
The method demonstrates potential for analyzing length-biased data, right-censored biased samples, and models with missing covariates.