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Related Experiment Video

Updated: Feb 5, 2026

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

Proportional cross-ratio model.

Tianle Hu¹, Bin Nan², Xihong Lin³

¹Eli Lilly and Company, Indianapolis, IN, 46285, USA.

Lifetime Data Analysis

|September 9, 2018

Summary

This summary is machine-generated.

This study introduces a proportional cross-ratio model to analyze how covariates affect the dependence between failure times. The method accurately estimates these effects, demonstrating its utility in biological and medical research.

Keywords:

Bivariate survival Cross-ratio Empirical process theory Local pseudo-partial likelihood U-process

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Area of Science:

Biostatistics
Survival Analysis
Statistical Modeling

Background:

Correlated failure times are common in medical research.
The cross-ratio is a key measure of dependence between these times.
Understanding how covariates influence this dependence is crucial for accurate analysis.

Purpose of the Study:

To propose a proportional cross-ratio model to assess covariate effects on the dependence between failure times.
To develop a method for jointly estimating the baseline cross-ratio and covariate effects.
To validate the proposed method using simulation studies and real-world data.

Main Methods:

Developed a proportional cross-ratio model with a baseline cross-ratio function and multiplicative covariate effect.
Generalized the pseudo-partial likelihood approach for joint estimation.
Employed parametric modeling for the baseline cross-ratio.

Main Results:

The proposed parameter estimator is consistent and asymptotically normal.
Simulation studies confirmed the technique's performance in finite samples.
Applied the method to analyze menstrual cycle data and twin appendicitis data.

Conclusions:

The proportional cross-ratio model effectively analyzes covariate effects on correlated failure times.
The method provides a robust framework for joint estimation in survival analysis.
Demonstrated applicability in analyzing complex biological and genetic data.