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Related Concept Videos

Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Published on: December 9, 2015

Destructive weighted Poisson cure rate models.

Josemar Rodrigues1, Mário de Castro, N Balakrishnan

  • 1Departamento de Estatística, Universidade Federal de São Carlos, Via Washington Luís, km 235, Caixa Postal 676, São Carlos, SP, 13565-905, Brazil. vjosemar@ufscar.br

Lifetime Data Analysis
|November 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible cure rate survival model using a compound weighted Poisson distribution for competing risks. The model offers enhanced dispersion flexibility and a realistic biological interpretation of event occurrence.

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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

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Published on: December 9, 2015

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Cure rate survival models are essential for analyzing data where a portion of subjects may never experience the event.
  • Existing models, like the promotion time cure model, have limitations in flexibility and biological interpretation.
  • Understanding competing risks is crucial for accurate event occurrence modeling.

Purpose of the Study:

  • To develop a novel and flexible cure rate survival model.
  • To incorporate competing causes of the event of interest.
  • To provide a realistic biological interpretation of event occurrence mechanisms.

Main Methods:

  • Development of a flexible cure rate survival model.
  • Assumption of a compound weighted Poisson distribution for the number of competing causes.
  • Comparison with the promotion time cure model regarding dispersion flexibility.

Main Results:

  • The proposed model demonstrates greater flexibility in dispersion compared to the promotion time cure model.
  • The model offers a realistic interpretation of the biological mechanism involving destructive processes of risk factors.
  • The model accounts for events occurring only from the 'undamaged' portion of initial risk factors.

Conclusions:

  • The compound weighted Poisson distribution provides a flexible framework for cure rate survival analysis with competing risks.
  • This model enhances the understanding of biological mechanisms underlying event occurrence in survival data.
  • The proposed model offers a valuable alternative for analyzing complex survival data with cure fractions and competing events.