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Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A Bayesian model for longitudinal count data with non-ignorable dropout.

Niko A Kaciroti1, Trivellore E Raghunathan, M Anthony Schork

  • 1University of Michigan, Ann Arbor, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to analyze asthma intervention outcomes, addressing challenges with missing data. The developed pattern-mixture model helps understand hospitalization rates when data is incomplete.

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Last Updated: Jun 6, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Pediatric chronic disease management
  • Biostatistics and epidemiological modeling
  • Longitudinal data analysis

Background:

  • Asthma is a significant chronic childhood illness.
  • Self-regulation principles guided an intervention program for asthma management.
  • Randomized longitudinal studies are crucial for evaluating such interventions.

Purpose of the Study:

  • To develop a statistical model for analyzing intervention effects on asthma-related hospitalizations.
  • To address the pervasive issue of non-ignorable missing data in longitudinal studies.
  • To provide a flexible framework for sensitivity analyses in the presence of missing data.

Main Methods:

  • Development of a pattern-mixture model for clustered longitudinal count data.
  • Parameterization using ratios of event rates to measure departures from ignorable missingness.
  • Sensitivity analyses conducted within a Bayesian framework, averaging over prior distributions.

Main Results:

  • The proposed parameterization allows for sensitivity analyses on data with non-ignorable missing values.
  • The model quantifies departures from ignorable missing data mechanisms.
  • The Bayesian approach incorporates uncertainty regarding the missing data mechanism.

Conclusions:

  • The developed pattern-mixture model offers an intuitive and flexible approach to analyze asthma intervention outcomes with missing data.
  • The methodology enables robust sensitivity analyses, crucial for reliable interpretation of results.
  • This framework enhances the incorporation of missing data uncertainties in statistical analyses of health interventions.