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Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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Multi-input and Multi-variable systems01:22

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Inference for outcome probabilities in multi-state models.

Per Kragh Andersen1, Maja Pohar Perme

  • 1Department of Biostatistics, University of Copenhagen, O. Farimagsgade 5, PB 2099, 1014, Copenhagen K, Denmark. P.K.Andersen@biostat.ku.dk

Lifetime Data Analysis
|September 16, 2008
PubMed
Summary
This summary is machine-generated.

Multi-state models analyze bone marrow transplant outcomes, including death, relapse, and graft recovery. This review covers regression methods for transition intensities and probabilities using semi-parametric and parametric approaches.

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Last Updated: Jul 1, 2026

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Published on: March 1, 2022

Area of Science:

  • Biostatistics
  • Hematology
  • Medical Statistics

Background:

  • Bone marrow transplantation involves complex patient follow-up with multiple event types.
  • Events observed include ultimate outcomes (death, relapse) and transient states (graft versus host disease, graft recovery).

Purpose of the Study:

  • To review analytical approaches for bone marrow transplantation studies using multi-state models.
  • To emphasize regression models for transition intensities and state occupation probabilities.

Main Methods:

  • Discussion of multi-state models for analyzing time-dependent events in clinical studies.
  • Review of semi-parametric models, such as Cox regression.
  • Review of parametric models, including those with piecewise constant intensities.

Main Results:

  • Multi-state models provide a framework for analyzing complex event data in longitudinal studies.
  • Regression models allow for the investigation of factors influencing transitions between states.
  • Both semi-parametric and parametric approaches offer valuable tools for statistical analysis.

Conclusions:

  • Multi-state models are well-suited for the analysis of bone marrow transplantation data.
  • Regression techniques enhance the understanding of event dynamics and patient outcomes.
  • The choice between semi-parametric and parametric models depends on specific study characteristics and assumptions.