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Hazard Ratio

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Updated: Jul 6, 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

Double-smoothing in kernel hazard rate estimation.

R Weibbach¹, A Pfahlberg, O Gefeller

¹Institut für Wirtschafts- und Sozialstatistik, Fachbereich Statistik, Universität Dortmund, 44221 Dortmund, Germany. Rafael.Weissbach@uni-dortmund.de

Methods of Information in Medicine

|March 14, 2008

Summary

This summary is machine-generated.

We introduce double-smoothing, a novel data-adaptive method for selecting smoothing parameters in nonparametric hazard rate estimation. This technique enhances survival risk analysis in oncology by improving estimation stability.

Related Experiment Videos

Last Updated: Jul 6, 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

Area of Science:

Biostatistics
Survival Analysis
Nonparametric Statistics

Background:

Hazard rate estimation is crucial for differentiating survival risk patterns in oncological studies.
Nonparametric curve estimation aids in revealing survival patterns, but smoothing parameter selection is critical.
Data-adaptive smoothing methods are essential for accurate hazard rate estimation.

Purpose of the Study:

To develop and evaluate a new data-adaptive smoothing selection algorithm for nearest-neighbor bandwidth in hazard rate estimation.
To enhance the methodology for selecting smoothing parameters in nonparametric survival analysis.
To improve the stability and accuracy of hazard rate estimation in biostatistical applications.

Main Methods:

Introduced a novel selection algorithm named double-smoothing for nearest-neighbor bandwidth.
Utilized a finite sample approximation of the asymptotic relationship between fixed and nearest-neighbor bandwidths.
Applied the algorithm in a clinical study, comparing results to traditional fixed bandwidth methods.

Main Results:

The double-smoothing approach provides an additional data-adaptive smoothing step after fixed bandwidth smoothing.
Demonstrated practical performance and improved estimation stability compared to traditional methods.
Illustrated the application of the algorithm in a real-world clinical study.

Conclusions:

The double-smoothing approach expands methodological options for smoothing parameter selection in nonparametric hazard rate estimation.
Offers substantial estimation stability with a minimal increase in computational effort.
Provides significant benefits for biostatistical applications in survival analysis.