Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Regression analysis of interval-censored failure time data

¹Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.

Statistics in Medicine

|March 15, 1997

Summary

This study introduces a discrete logistic model for analyzing interval-censored failure time data from clinical trials. This approach offers improved analysis for discrete data compared to continuous models.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The preparation and crystallization of Fab fragments of a family of mouse esterolytic catalytic antibodies and their complexes with a transition-state analogue.

Acta crystallographica. Section D, Biological crystallography·2001

Same author

A short course of methylprednisolone immunosuppression inhibits both rejection and spontaneous acceptance of rat liver allografts.

Transplantation·2001

Same author

v-Src transformation is mediated through farnesylated proteins.

The Journal of surgical research·2001

Same author

Quantitative analysis of bacterial gene expression by using the gusA reporter gene system.

Applied and environmental microbiology·2001

Same author

Decrease in cell surface galactose residues of Schizosaccharomyces pombe enhances its coflocculation with Pediococcus damnosus.

Applied and environmental microbiology·2001

Same author

Designing fast converging phylogenetic methods.

Bioinformatics (Oxford, England)·2001

Area of Science:

Biostatistics
Clinical Trials Methodology
Survival Analysis

Background:

Interval-censored failure time data are common in clinical trials and longitudinal studies.
Existing regression methods often use continuous models (e.g., Cox's proportional hazards model).
Clinical trial data are frequently discrete, posing challenges for continuous models.

Purpose of the Study:

To propose a novel regression analysis method for interval-censored failure time data.
To specifically address data that are discrete in scale, common in clinical settings.
To facilitate the comparison of failure time distributions across different treatments.

Main Methods:

Development of a discrete logistic model tailored for interval-censored data.
Application of the proposed model to regression analysis.
Exploration of the relationship between the discrete model and existing continuous methods.

Main Results:

The proposed discrete logistic model provides a suitable framework for analyzing discrete interval-censored failure time data.
The method allows for effective comparison of failure time distributions among treatment groups.
The study elucidates the connections between the novel discrete approach and established continuous regression techniques.

Conclusions:

A discrete logistic model is effective for regression analysis of discrete interval-censored failure time data.
This method enhances the analysis of data from clinical trials where observations are often discrete.
The proposed approach offers an alternative and potentially more appropriate tool for specific data types in survival analysis.

Related Experiment Videos