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Estimation of excess hazard using compound Poisson frailty model

Authors: Mahmood Sheikh-Fathollahi; Mahmood Mahmoodi; Kazem Mohammad; Hojjat Zeraati; Arash Jalali;

Estimation of excess hazard using compound Poisson frailty model

Abstract

Background & Aim: The excess hazard rate proposed by Andersen and Vaeth may underestimate the long-term excess hazard rate for cancer survival. Zahl explained the phenomenon by continuous selection of the most robust individuals after diagnosis. He applied correlated inverse Gaussian and gamma frailty models to estimate excess intensity and reached a better estimate of the rate and called it the corrected excess hazard. The compound Poisson distribution has more parameters and therefore owns more flexibility and includes gamma and inverse Gaussian distributions as special cases. Therefore, the aim of this study was to estimate the excess hazard using compound poisson frailty model Methods & Materials: Both shared and correlated frailty (CF) variables based on compound Poisson distribution were used to model unobserved common covariates. A data set of patients diagnosed with localized or regional gastrointestinal tract cancer collected at the Mazandaran province of Iran was studied. As registration systems in Iran are so affected by omission and various errors, a number of five West Coale- Demeny life tables for men and four for women were constructed corresponding to each birth cohort, which was considered as the reference life tables. Thus, population-based mortality rates [h1(t)] were simply replaced by the appropriate values of the West tables depending on the sex (male or female) and birth cohort of the patient. Results: The CF model with unequal variances could best estimate the long-term excess hazard. Conclusion: This study advocates the CF models can best estimate the long-term excess hazard rates regardless of the distribution of the frailty variable.

Keywords

excess hazard, frailty models, compound Poisson frailty model, QH301-705.5, Mazandaran province of Iran, shared and correlated Gaussian frailty models, Biology (General), Probabilities. Mathematical statistics, QA273-280, Coale–Demeny life table models

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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Related to Research communities
Cancer Research