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Related Experiment Video

Updated: Jun 5, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Simultaneous confidence bounds for relative risks in multiple comparisons to control.

B Klingenberg¹

¹Department of Mathematics and Statistics, Williams College, Williamstown, MA 01267, U.S.A.. bklingen@williams.edu

Statistics in Medicine

|December 21, 2010

Summary

This study introduces methods for simultaneous confidence intervals to compare multiple vaccine treatments against a control. The best method provides accurate coverage and is powerful, with simpler options available for large studies.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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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

Area of Science:

Biostatistics
Clinical Trials
Vaccinology

Background:

Comparing multiple treatments to a control is common in vaccine studies.
Accurate statistical inference is crucial for evaluating treatment efficacy.

Purpose of the Study:

To develop and evaluate methods for constructing simultaneous upper confidence limits for relative risks.
To assess the performance of these methods in vaccine study settings.

Main Methods:

Inverting the minimum of score statistics and estimating the null correlation matrix.
Investigating simultaneous lower and two-sided confidence intervals.
Utilizing general R code for implementation and evaluation.

Main Results:

The proposed method achieved simultaneous coverage rates closest to the nominal level.
This method demonstrated the highest statistical power among those considered.
Computationally simpler alternatives were identified for scenarios with numerous comparisons.

Conclusions:

The developed method offers a robust approach for simultaneous inference in comparative vaccine studies.
The R code facilitates practical application and further evaluation of these statistical procedures.