An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.

Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression

On a general structure for hazard-based regression models: An application to population-based cancer research

The proportional hazards model represents the most commonly assumed hazard structure when analysing time to event data using regression models. We study a general hazard structure which contains, as particular cases, proportional hazards, accelerated hazards, and accelerated failure time structures, as well as combinations of these. We propose an approach to apply these different hazard structures, based on a flexible parametric distribution (exponentiated Weibull) for the baseline hazard. This distribution allows us to cover the basic hazard shapes of interest in practice: constant, bathtub, increasing, decreasing, and unimodal. In an extensive simulation study, we evaluate our approach in the context of excess hazard modelling, which is the main quantity of interest in descriptive cancer epidemiology. This study exhibits good inferential properties of the proposed model, as well as good performance when using the Akaike Information Criterion for selecting the hazard structure. An application on lung cancer data illustrates the usefulness of the proposed model

On a general structure for hazard-based regression models: An application to population-based cancer research.

The proportional hazards model represents the most commonly assumed hazard structure when analysing time to event data using regression models. We study a general hazard structure which contains, as particular cases, proportional hazards, accelerated hazards, and accelerated failure time structures, as well as combinations of these. We propose an approach to apply these different hazard structures, based on a flexible parametric distribution (exponentiated Weibull) for the baseline hazard. This distribution allows us to cover the basic hazard shapes of interest in practice: constant, bathtub, increasing, decreasing, and unimodal. In an extensive simulation study, we evaluate our approach in the context of excess hazard modelling, which is the main quantity of interest in descriptive cancer epidemiology. This study exhibits good inferential properties of the proposed model, as well as good performance when using the Akaike Information Criterion for selecting the hazard structure. An application on lung cancer data illustrates the usefulness of the proposed model

Performance of two formal tests based on martingales residuals to check the proportional hazard assumption and the functional form of the prognostic factors in flexible parametric excess hazard models.

: Net survival, the one that would be observed if the disease under study was the only cause of death, is an important, useful, and increasingly used indicator in public health, especially in population-based studies. Estimates of net survival and effects of prognostic factor can be obtained by excess hazard regression modeling. Whereas various diagnostic tools were developed for overall survival analysis, few methods are available to check the assumptions of excess hazard models. We propose here two formal tests to check the proportional hazard assumption and the validity of the functional form of the covariate effects in the context of flexible parametric excess hazard modeling. These tests were adapted from martingale residual-based tests for parametric modeling of overall survival to allow adding to the model a necessary element for net survival analysis: the population mortality hazard. We studied the size and the power of these tests through an extensive simulation study based on complex but realistic data. The new tests showed sizes close to the nominal values and satisfactory powers. The power of the proportionality test was similar or greater than that of other tests already available in the field of net survival. We illustrate the use of these tests with real data from French cancer registries.<br/

Estimation of screening test (Hemoccult®) sensitivity in colorectal cancer mass screening

3 controlled cohorts of mass-screening for colorectal cancer using a biennial faecal occult blood (HemoccultII®) test on well-defined European populations have demonstrated a 14% to 18% reduction in specific mortality. We aimed to estimate the sensitivity (S) of this HemoccultII®test and and also mean sojourn time (MST) from French colorectal mass-screening programme data. 6 biennial screening rounds were performed from 1988 to 1998 in 45 603 individuals aged 45–74 years in Saône-et-Loire (Burgundy, France). The prevalent/incidence ratio was calculated in order to obtain a direct estimate of the product S.MST. The analysis of the proportional incidence and its modelling was used to derive an indirect estimate of S and MST. The product S.MST was higher for males than females and higher for left colon than either the right colon or rectum. The analysis of the proportional incidence confirmed the result for subsites but no other significant differences were found. The sensitivity was estimated at 0.57 and the MST at 2.56 years. This study confirms that the sensitivity of the Hemoccult test is relatively low and that the relatively short sojourn time is in favour of annual screening. © 2001 Cancer Research Campaign http://www.bjcancer.co

Describing the association between socioeconomic inequalities and cancer survival: methodological guidelines and illustration with population-based data.

BACKGROUND: Describing the relationship between socioeconomic inequalities and cancer survival is important but methodologically challenging. We propose guidelines for addressing these challenges and illustrate their implementation on French population-based data. METHODS: We analyzed 17 cancers. Socioeconomic deprivation was measured by an ecological measure, the European Deprivation Index (EDI). The Excess Mortality Hazard (EMH), ie, the mortality hazard among cancer patients after accounting for other causes of death, was modeled using a flexible parametric model, allowing for nonlinear and/or time-dependent association between the EDI and the EMH. The model included a cluster-specific random effect to deal with the hierarchical structure of the data. RESULTS: We reported the conventional age-standardized net survival (ASNS) and described the changes of the EMH over the time since diagnosis at different levels of deprivation. We illustrated nonlinear and/or time-dependent associations between the EDI and the EMH by plotting the excess hazard ratio according to EDI values at different times after diagnosis. The median excess hazard ratio quantified the general contextual effect. Lip-oral cavity-pharynx cancer in men showed the widest deprivation gap, with 5-year ASNS at 41% and 29% for deprivation quintiles 1 and 5, respectively, and we found a nonlinear association between the EDI and the EMH. The EDI accounted for a substantial part of the general contextual effect on the EMH. The association between the EDI and the EMH was time dependent in stomach and pancreas cancers in men and in cervix cancer. CONCLUSION: The methodological guidelines proved efficient in describing the way socioeconomic inequalities influence cancer survival. Their use would allow comparisons between different health care systems

Explained variation of excess hazard models.

The availability of longstanding collection of detailed cancer patient information makes multivariable modelling of cancer-specific hazard of death appealing. We propose to report variation in survival explained by each variable that constitutes these models. We adapted the ranks explained (RE) measure to the relative survival data setting, ie, when competing risks of death are accounted for through life tables from the general population. RE is calculated at each event time. We introduce weights for each death reflecting its probability to be a cancer death. RE varies between -1 and +1 and can be reported at given times in the follow-up and as a time-varying measure from diagnosis onward. We present an application for patients diagnosed with colon or lung cancer in England. The RE measure shows reasonable properties and is comparable in both relative and cause-specific settings. One year after diagnosis, RE for the most complex excess hazard models reaches 0.56, 95% CI: 0.54 to 0.58 (0.58 95% CI: 0.56-0.60) and 0.69, 95% CI: 0.68 to 0.70 (0.67, 95% CI: 0.66-0.69) for lung and colon cancer men (women), respectively. Stage at diagnosis accounts for 12.4% (10.8%) of the overall variation in survival among lung cancer patients whereas it carries 61.8% (53.5%) of the survival variation in colon cancer patients. Variables other than performance status for lung cancer (10%) contribute very little to the overall explained variation. The proportion of the variation in survival explained by key prognostic factors is a crucial information toward understanding the mechanisms underpinning cancer survival. The time-varying RE provides insights into patterns of influence for strong predictors

Sheep Feed and Scrapie, France

Proprietary concentrates and milk replacers were linked to risk for scrapie