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Related Experiment Video

Updated: Jun 16, 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

On semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome.

Rui Song¹, Haibo Zhou, Michael R Kosorok

¹Departments of Biostatistics, University of North Carolina Chapel Hill, North Carolina 27599-7420, U.S.A.

|January 29, 2010

Summary

Outcome-dependent sampling enhances study efficiency and links to missing-data frameworks. A computationally convenient estimator achieves efficient information bounds for continuous outcomes.

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Last Updated: Jun 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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

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Published on: January 8, 2020

Area of Science:

Biostatistics
Epidemiology
Statistical Inference

Background:

Outcome-dependent sampling (ODS) designs improve study efficiency.
Continuous outcomes present unique challenges for ODS.

Purpose of the Study:

To extend two-stage case-control designs to continuous outcomes using ODS.
To link ODS to missing-data and biased-sampling frameworks.
To develop a computationally convenient and efficient semiparametric maximum likelihood estimator.

Main Methods:

Semiparametric inference techniques.
Missing-data imputation and analysis methods.
Development of a semiparametric maximum likelihood estimator.

Main Results:

ODS for continuous outcomes is an extension of two-stage case-control designs.
A natural link exists between two-stage ODS and missing-data/biased-sampling frameworks.
The proposed estimator is computationally convenient and semiparametrically efficient.

Conclusions:

ODS offers a cost-effective approach to enhance study efficiency for continuous outcomes.
Semiparametric methods provide a robust framework for analyzing ODS data.
The developed estimator achieves optimal efficiency, validated by theory and simulation.