More accurate cancer-related excess mortality through correcting background mortality for extra variables

Touraine, Célia; Grafféo, Nathalie; Giorgi, Roch

doi:10.1177/0962280218823234

Cited by 16 publications

(15 citation statements)

References 27 publications

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“…obtained by removing a variable from a real complete table), Model 3 proved to be useful; it performed better than Models 1 and 2 vs. gold-standard estimates obtained with a "complete" life table. Note that our results differ slightly from those of Touraine et al work[26] simply because of minor modifications in the choice of criteria for the inclusion of patients in our study. In the second application (SPC as additional variable), Models 1 and 3 had the same AIC.…”

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confidence: 99%

Section: ____________________________________________________________mentioning

confidence: 99%

“…Touraine et al [26] proposed a model that allows the background mortality of the studied population to differ from that of the general population using a multiplicative parameter.…”

Section: Touraine's Model (Model 2)mentioning

confidence: 99%

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Correcting inaccurate background mortality in excess hazard models through breakpoints

Darlin

Goungounga

Grafféo

et al. 2020

Preprint

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Background : Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available. Methods : In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer. Results: The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine’s proportional hazards model. Conclusion: Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

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confidence: 99%

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confidence: 99%

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Correcting inaccurate background mortality in excess hazard models through breakpoints

Darlin

Goungounga

Grafféo

et al. 2020

Preprint

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“…Using a robust estimator of the variance for this additional parameter may be an option to reach a better coverage, as may be calculating profile likelihood intervals. Another extension of model (2) consists of modelling the correction parameters γ and µ in terms of a set of covariates, say w. A related approach has recently been studied in Touraine et al [2019]. Possible limitations include the inferential challenges in estimating q ≥ 2 (the dimension of w) when the sample size is not large enough.…”

Section: Further Researchmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Furthermore, this scale parameter was common to all patients and allowed the mortality from other causes to differ between the studied group and the general population. Therefore, Touraine et al [ 26 ] proposed a model where the population hazard is modelled using life table mortality rates and multiplicative parameters that depend on the level of an additional variable. However, this model relies on an assumption of proportional hazards; i.e., the background mortality differs from the life table mortality in a multiplicative way.…”

Section: Introductionmentioning

confidence: 99%

Correcting inaccurate background mortality in excess hazard models through breakpoints

Darlin

Goungounga

Grafféo

et al. 2020

BMC Med Res Methodol

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Background Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available. Methods In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer. Results The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine’s proportional hazards model. Conclusion Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

show abstract

More accurate cancer-related excess mortality through correcting background mortality for extra variables

Cited by 16 publications

References 27 publications

Correcting inaccurate background mortality in excess hazard models through breakpoints

Correcting inaccurate background mortality in excess hazard models through breakpoints

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

Correcting inaccurate background mortality in excess hazard models through breakpoints

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