Research output: Contribution to journal › Article

**On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables.** / Rubio, Francisco Javier; Rachet , Bernard ; Giorgi, Roch; Maringe, Camille; Belot, Aurelien; CENSUR working survival group.

Research output: Contribution to journal › Article

Rubio, FJ, Rachet , B, Giorgi, R, Maringe, C, Belot, A & CENSUR working survival group 2019, 'On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables', *BIOSTATISTICS*, no. 1, pp. 1-17. https://doi.org/10.1093/biostatistics/kxz017

Rubio, F. J., Rachet , B., Giorgi, R., Maringe, C., Belot, A., & CENSUR working survival group (2019). On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables. *BIOSTATISTICS*, (1), 1-17. https://doi.org/10.1093/biostatistics/kxz017

Rubio FJ, Rachet B, Giorgi R, Maringe C, Belot A, CENSUR working survival group. On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables. BIOSTATISTICS. 2019;(1):1-17. https://doi.org/10.1093/biostatistics/kxz017

@article{9117fb8216ea4fda8d0c7469f73d6972,

title = "On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables",

abstract = "In cancer epidemiology using population-based data, regression models for the excess mortality hazard is a useful method to estimate cancer survival and to describe the association between prognosis factors and excess mortality. This method requires expected mortality rates from general population life tables: each cancer patient is assigned an expected (background) mortality rate obtained from the life tables, typically at least according to their age and sex, from the population they belong to. However, those life tables may be insufficiently stratified, as some characteristics such as deprivation, ethnicity, and comorbidities, are not available in the life tables for a number of countries. This may affect the background mortality rate allocated to each patient, and it has been shown that not including relevant information for assigning an expected mortality rate to each patient induces a bias in the estimation of the regression parameters of the excess hazard model. We propose two parametric corrections in excess hazard regression models, including a single-parameter or a random effect (frailty), to account for possible mismatches in the life table and thus misspecification of the background mortality rate. In an extensive simulation study, the good statistical performance of the proposed approach is demonstrated, and we illustrate their use on real population-based data of lung cancer patients. We present conditions and limitations of these methods, and provide some recommendations for their use in practice.",

author = "Rubio, {Francisco Javier} and Bernard Rachet and Roch Giorgi and Camille Maringe and Aurelien Belot and {CENSUR working survival group}",

year = "2019",

doi = "10.1093/biostatistics/kxz017",

language = "English",

pages = "1--17",

journal = "BIOSTATISTICS",

issn = "1465-4644",

publisher = "Oxford University Press",

number = "1",

}

TY - JOUR

T1 - On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables

AU - Rubio, Francisco Javier

AU - Rachet , Bernard

AU - Giorgi, Roch

AU - Maringe, Camille

AU - Belot, Aurelien

AU - CENSUR working survival group

PY - 2019

Y1 - 2019

N2 - In cancer epidemiology using population-based data, regression models for the excess mortality hazard is a useful method to estimate cancer survival and to describe the association between prognosis factors and excess mortality. This method requires expected mortality rates from general population life tables: each cancer patient is assigned an expected (background) mortality rate obtained from the life tables, typically at least according to their age and sex, from the population they belong to. However, those life tables may be insufficiently stratified, as some characteristics such as deprivation, ethnicity, and comorbidities, are not available in the life tables for a number of countries. This may affect the background mortality rate allocated to each patient, and it has been shown that not including relevant information for assigning an expected mortality rate to each patient induces a bias in the estimation of the regression parameters of the excess hazard model. We propose two parametric corrections in excess hazard regression models, including a single-parameter or a random effect (frailty), to account for possible mismatches in the life table and thus misspecification of the background mortality rate. In an extensive simulation study, the good statistical performance of the proposed approach is demonstrated, and we illustrate their use on real population-based data of lung cancer patients. We present conditions and limitations of these methods, and provide some recommendations for their use in practice.

AB - In cancer epidemiology using population-based data, regression models for the excess mortality hazard is a useful method to estimate cancer survival and to describe the association between prognosis factors and excess mortality. This method requires expected mortality rates from general population life tables: each cancer patient is assigned an expected (background) mortality rate obtained from the life tables, typically at least according to their age and sex, from the population they belong to. However, those life tables may be insufficiently stratified, as some characteristics such as deprivation, ethnicity, and comorbidities, are not available in the life tables for a number of countries. This may affect the background mortality rate allocated to each patient, and it has been shown that not including relevant information for assigning an expected mortality rate to each patient induces a bias in the estimation of the regression parameters of the excess hazard model. We propose two parametric corrections in excess hazard regression models, including a single-parameter or a random effect (frailty), to account for possible mismatches in the life table and thus misspecification of the background mortality rate. In an extensive simulation study, the good statistical performance of the proposed approach is demonstrated, and we illustrate their use on real population-based data of lung cancer patients. We present conditions and limitations of these methods, and provide some recommendations for their use in practice.

U2 - 10.1093/biostatistics/kxz017

DO - 10.1093/biostatistics/kxz017

M3 - Article

SP - 1

EP - 17

JO - BIOSTATISTICS

JF - BIOSTATISTICS

SN - 1465-4644

IS - 1

ER -

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