Add to ORKGIn this work we consider a joint space-time model for cancer incidence, using data on prostate cancer collected between 1988 and 2005 in a specific area of France. Our aim is to take into account possible non linear effects of some covariates and zero-inflation due to data aggregation for Poisson regression. We assume that counts of cancer cases follow zero-inflated Poisson distribution, where the probability of zero inflation is a monotonic function of the mean. The purpose of our analysis is to check whether the French prostate screening program, which begins in 1994, results in a spatial or a spatial-temporal change of the pattern of the disease
The zero-inflated regression models are a very powerful tool for the analysis of counting data with ...
AbstractThis paper consolidates the zero-inflated Poisson model for count data with excess zeros pro...
Projection of age-specific cancer incidence and mortality data play an integral role in planning and...
In this work we consider a joint space-time model for cancer incidence, using data on prostate can...
Cancer incidence data are typically available as rates or counts for contiguous geographical regions...
In this work we analyze cancer incidence data collected between 1992 and 2005 in a specific area of...
We analyse lymphoid leukemia incidence data collected between 1988 and 2002 from the cancer registry...
Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, i...
This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. ...
This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. ...
Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, i...
Age-period-cohort (APC) models are widely used to analyze population-level rates, particularly cance...
The main aim of this study, using a spatial-temporal model, is to analyse the link between a depriva...
Accurate and precise knowledge about the distribution and evolution of a disease in space and time i...
Based on the example of data on breast cancer survival in a specific area in France, this paper desc...
The zero-inflated regression models are a very powerful tool for the analysis of counting data with ...
AbstractThis paper consolidates the zero-inflated Poisson model for count data with excess zeros pro...
Projection of age-specific cancer incidence and mortality data play an integral role in planning and...
In this work we consider a joint space-time model for cancer incidence, using data on prostate can...
Cancer incidence data are typically available as rates or counts for contiguous geographical regions...
In this work we analyze cancer incidence data collected between 1992 and 2005 in a specific area of...
We analyse lymphoid leukemia incidence data collected between 1988 and 2002 from the cancer registry...
Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, i...
This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. ...
This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. ...
Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, i...
Age-period-cohort (APC) models are widely used to analyze population-level rates, particularly cance...
The main aim of this study, using a spatial-temporal model, is to analyse the link between a depriva...
Accurate and precise knowledge about the distribution and evolution of a disease in space and time i...
Based on the example of data on breast cancer survival in a specific area in France, this paper desc...
The zero-inflated regression models are a very powerful tool for the analysis of counting data with ...
AbstractThis paper consolidates the zero-inflated Poisson model for count data with excess zeros pro...
Projection of age-specific cancer incidence and mortality data play an integral role in planning and...