mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect

We present mexhaz, an R package for fitting flexible hazard-based regression models with the possibility to add time-dependent effects of covariates and to account for a twolevel hierarchical structure in the data through the inclusion of a normally distributed random intercept (i.e., a log-normally distributed shared frailty). Moreover, mexhazbased models can be fitted within the excess hazard setting by allowing the specification of an expected hazard in the model. These models are of common use in the context of the analysis of population-based cancer registry data. Follow-up time can be entered in the right-censored or counting process input style, the latter allowing models with delayed entries. The logarithm of the baseline hazard can be flexibly modeled with B-splines or restricted cubic splines of time. Parameters estimation is based on likelihood maximization: in deriving the contribution of each observation to the cluster-specific conditional likelihood, Gauss-Legendre quadrature is used to calculate the cumulative hazard; the cluster-specific marginal likelihoods are then obtained by integrating over the random effects distribution, using adaptive Gauss-Hermite quadrature. Functions to compute and plot the predicted (excess) hazard and (net) survival (possibly with cluster-specific predictions in the case of random effect models) are provided. We illustrate the use of the different options of the mexhaz package and compare the results obtained with those of other available R packages

Estimation of the adjusted cause-specific cumulative probability using flexible regression models for the cause-specific hazards.

In competing risks setting, we account for death according to a specific cause and the quantities of interest are usually the cause-specific hazards (CSHs) and the cause-specific cumulative probabilities. A cause-specific cumulative probability can be obtained with a combination of the CSHs or via the subdistribution hazard. Here, we modeled the CSH with flexible hazard-based regression models using B-splines for the baseline hazard and time-dependent (TD) effects. We derived the variance of the cause-specific cumulative probabilities at the population level using the multivariate delta method and showed how we could easily quantify the impact of a covariate on the cumulative probability scale using covariate-adjusted cause-specific cumulative probabilities and their difference. We conducted a simulation study to evaluate the performance of this approach in its ability to estimate the cumulative probabilities using different functions for the cause-specific log baseline hazard and with or without a TD effect. In the scenario with TD effect, we tested both well-specified and misspecified models. We showed that the flexible regression models perform nearly as well as the nonparametric method, if we allow enough flexibility for the baseline hazards. Moreover, neglecting the TD effect hardly affects the cumulative probabilities estimates of the whole population but impacts them in the various subgroups. We illustrated our approach using data from people diagnosed with monoclonal gammopathy of undetermined significance and provided the R-code to derive those quantities, as an extension of the R-package mexhaz

Age disparities in lung cancer survival in New Zealand: The role of patient and clinical factors.

OBJECTIVE: Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in lung cancer excess mortality hazard by stage at diagnosis in New Zealand. MATERIALS AND METHODS: We extracted 22 487 new lung cancer cases aged 50-99 (median age = 71, 47.1 % females) diagnosed between 1 January 2006 and 31 July 2017 from the New Zealand population-based cancer registry and followed up to December 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on the excess mortality hazard by stage at diagnosis, and we derived corresponding lung cancer net survival. RESULTS: The age difference in net survival was particularly marked for localised and regional lung cancers, with a sharp decline in survival from the age of 70. No identified factors influenced age disparities in patients with localised cancer. However, for other stages, females had a greater difference in survival between middle-age and older-age than males. Comorbidity and emergency presentation played a minor role. Ethnicity and deprivation did not influence age disparities in lung cancer survival. CONCLUSION: Sex and stage at diagnosis were the most important factors of age disparities in lung cancer survival in New Zealand

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

The impact of timely cancer diagnosis on age disparities in colon cancer survival

Objective We described the role of patient-related and clinical factors on age disparities in colon cancer survival among patients aged 50-99 using New Zealand population-based cancer registry data linked to hospitalisation data. Method We included 21,270 new colon cancer cases diagnosed between 1 January 2006 and 31 July 2017, followed up to end 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on colon cancer survival by stage at diagnosis using flexible excess hazard regression models. Results The excess mortality in older patients was minimal for localised cancers, maximal during the first six months for regional cancers, the first eighteen months for distant cancers, and over the three years for missing stages. The age pattern of the excess mortality hazard varied according to sex for distant cancers, emergency presentation for regional and distant cancers, and comorbidity for cancer with missing stages. Ethnicity and deprivation did not influence age disparities in colon cancer survival. Conclusion Factors reflecting timeliness of cancer diagnosis most affected age-related disparities in colon cancer survival, probably by impacting treatment strategy. Because of the high risk of poor outcomes related to treatment in older patients, efforts made to improve earlier diagnosis in older patients are likely to help reduce age disparities in colon cancer survival in New Zealand

Impact of population aging on trends in diabetes prevalence : A meta-regression analysis of 160,000 Japanese adults

Aims/IntroductionTo provide age- and sex-specific trends, age-standardized trends, and projections of diabetes prevalence through the year 2030 in the Japanese adult population. Materials and MethodsIn the present meta-regression analysis, we included 161,087 adults from six studies and nine national health surveys carried out between 1988 and 2011 in Japan. We assessed the prevalence of diabetes using a recorded history of diabetes or, for the population of individuals without known diabetes, either a glycated hemoglobin level of 6.5% (48mmol/mol) or the 1999 World Health Organization criteria (i.e., a fasting plasma glucose level of 126mg/dL and/or 2-h glucose level of 200mg/dL in the 75-g oral glucose tolerance test). ResultsFor both sexes, prevalence appeared to remain unchanged over the years in all age categories except for men aged 70years or older, in whom a significant increase in prevalence with time was observed. Age-standardized diabetes prevalence estimates based on the Japanese population of the corresponding year showed marked increasing trends: diabetes prevalence was 6.1% among women (95% confidence interval [CI] 5.5-6.7), 9.9% (95% CI 9.2-10.6) among men, and 7.9% (95% CI 7.5-8.4) among the total population in 2010, and was expected to rise by 2030 to 6.7% (95% CI 5.2-9.2), 13.1% (95% CI 10.9-16.7) and 9.8% (95% CI 8.5-12.0), respectively. In contrast, the age-standardized diabetes prevalence using a fixed population appeared to remain unchanged. ConclusionsThis large-scale meta-regression analysis shows that a substantial increase in diabetes prevalence is expected in Japan during the next few decades, mainly as a result of the aging of the adult population.Peer reviewe

Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend analysis.

Cancer survival trend analyses are essential to describe accurately the way medical practices impact patients' survival according to the year of diagnosis. To this end, survival models should be able to account simultaneously for non-linear and non-proportional effects and for complex interactions between continuous variables. However, in the statistical literature, there is no consensus yet on how to build such models that should be flexible but still provide smooth estimates of survival. In this article, we tackle this challenge by smoothing the complex hypersurface (time since diagnosis, age at diagnosis, year of diagnosis, and mortality hazard) using a multidimensional penalized spline built from the tensor product of the marginal bases of time, age, and year. Considering this penalized survival model as a Poisson model, we assess the performance of this approach in estimating the net survival with a comprehensive simulation study that reflects simple and complex realistic survival trends. The bias was generally small and the root mean squared error was good and often similar to that of the true model that generated the data. This parametric approach offers many advantages and interesting prospects (such as forecasting) that make it an attractive and efficient tool for survival trend analyses

Expected impact of a public health intervention in the presence of synergistic risk factors.

OBJECTIVE: Elaborate and test a method to extrapolate the population attributable fraction (benefit of an intervention to reduce the exposure of a given population to a given risk factor) to another population allowing for effects of synergistic factors. STUDY DESIGN AND SETTING: Using data from the Systolic Hypertension in the Elderly Program, the present study investigated the impact of a reduction of blood pressure on the occurrence of stroke accounting for the age of the targeted population. RESULTS: A reduction of blood pressure in populations differing by their age distributions showed that the preventable proportion of strokes increased with age. A 20-mmHg reduction of blood pressure in a population with mean age 60 years was associated with a 14% reduction of strokes and 18% in a population with mean age 70 years. The difference between these two proportions can be interpreted as the proportion of cases due to the synergistic actions of age and high blood pressure on the occurrence of stroke. CONCLUSION: The presented example illustrates how the method may be used by public health practitioners to transpose the potential benefits of interventions estimated in a study population to other populations with different exposures to synergistic risk factors

The attributable risk : from the quantification of the impact of risk factors at the population level to the measure of the relative importance of biomarkers

Le risque attribuable est un outil épidémiologique apparu dans les années 1950 aujourd’hui encore assez peu utilisé. Il permet d’estimer la proportion de cas d’une maladie potentiellement évitable par suppression ou réduction de l’exposition d’une population à un facteur de risque. Son principal intérêt réside dans la prise en compte concomitante de l’ampleur d’effet du facteur de risque et de la distribution de ce facteur au sein de la population. Après une présentation des caractéristiques essentielles du risque attribuable et des principes de son estimation à partir d’une étude cas-témoins, nous proposons un cadre conceptuel qui permet d’estimer l’impact d’une intervention de santé publique dans une nouvelle population dont l’exposition à certains facteurs de risque diffère de celle observée dans la population d’étude. Une décomposition du risque attribuable permet alors de prendre en compte l’action combinée, ou synergie, des facteurs de risque dans la survenue de la maladie. Parce qu’il donne une dimension populationnelle à l’estimation de l’effet d’une variable, le risque attribuable est particulièrement intéressant pour quantifier l’importance relative des différentes variables explicatives d’un modèle de régression. La question de l’importance relative des biomarqueurs classiques et de ceux issus des technologies à haut débit dans les modèles diagnostiques est actuellement centrale pour établir les apports respectifs de ces deux niveaux d’information. À partir de simulations, nous montrons comment l’apport des nouvelles technologies, quantifié en termes de risque attribuable, peut être faussé par l’utilisation de méthodologies inadaptéesThe attributable risk is an epidemiologic tool that dates back to the fifties but is still relatively seldom used. It estimates the proportion of cases of a given disease that could be avoided if the exposure to a specific risk factor was removed or reduced. Its major interest is that it combines the magnitude of the effect of the risk factor to the distribution of this factor within the population. After a review of the attributable risk main features and the principles of its estimation from case-control studies data, we propose a conceptual framework that allows estimating the impact of a public health intervention in a new population with different exposure to certain risk factors than those observed in the study population. To reach this goal, we used a splitting of the attributable risk that takes into account the combined action –or synergy– of the risk factors on the occurrence of the disease. Because the attributable risk allows estimating the effect of a variable at the population level, it is particularly interesting to quantify the relative importance of the covariates of a regression model. In diagnostic models, the estimation of the relative importance of classic biomarkers and biomarkers obtained from high-throughput technologies is currently crucial in establishing the contribution of each of these two levels of information. Using simulations we have demonstrated the way the role of high-throughput-technologies –quantified in terms of attributable risk– may be wrongly assessed through the use of unsuitable methodolog

Estimation de la probabilité brute de décès lié au cancer (application à des données de registres (réseau Francim))

LYON1-BU Santé (693882101) / SudocSudocFranceF