Bayesian estimates of the incidence of rare cancers in Europe

Botta, Laura; Capocaccia, Riccardo; Trama, Annalisa; Herrmann, Christian; Salmerón, Diego; Angelis, Roberta De; Mallone, Sandra; Bidoli, Ettore; Marcos‐Gragera, Rafael; Dudek-Godeau, Dorota; Gatta, Gemma; Clèries, Ramón

doi:10.1016/j.canep.2018.04.003

Cited by 13 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results also show that patients from the most disadvantaged quintile have significantly poorer five-year relative survival compared to those from the least disadvantaged quintile. This supports previous studies that have found socioeconomic disadvantage is a predictor of patient cancer outcomes [ 36 – 38 ].…”

Section: Discussionsupporting

confidence: 92%

“…The classification of rare cancers may not reflect the true proportion of rare cancers in the population due to the difficulty in diagnosing rare cancers [ 29 ]. It is also difficult to derive stable and accurate estimates for rare cancer incidence and survival in a setting of low case numbers [ 38 ]. A strength of our study stems from the legislative requirement in WA that all cancer diagnoses are reported to the WACR [ 42 ], which minimises count underestimates by providing a comprehensive snapshot of cancer cases diagnosed in the WA population each year.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Descriptive epidemiological study of rare, less common and common cancers in Western Australia

Bilkey

Trevithick

Coles³

et al. 2021

BMC Cancer

View full text Add to dashboard Cite

Background There are no epidemiological studies describing rare cancers in Western Australia (WA). We aimed to fill this gap by estimating the incidence and five-year survival of rare, less common and common cancers in WA, based on definitions for rarity used by the Australian Institute of Health and Welfare and cancer groupings from the project on Surveillance of Rare Cancers in Europe (RARECARE). This research will enable policy- and decision-makers to better understand the size and nature of the public health problem presented by rare cancers in WA. It is anticipated that this study will inform improved health service design and delivery for all WA cancer patients, but particularly those with rare and less common cancers. Methods We estimated incidence and five-year survival rates of rare, less common and common cancers in WA using data sourced from the WA Cancer Registry for the 2013–2017 period. Cancers were defined as rare (< 6), less common (6–12), or common (> 12) based on their crude incidence rate per 100,000 people per year. Results Rare cancers make up 21.5% of all cancer diagnoses in WA, with a significantly poorer five-year survival of 58.2% (95% confidence interval (CI) 57.3–59.1%), compared to patients diagnosed with a common cancer, whose five-year survival was 87.8% (95% CI 87.3–88.3%). Survival for less common cancers was significantly poorer than both rare and common cancers, at 48.1% (95% CI 47.3–49.0%). Together, rare and less common cancers represent 48.4% of all cancer diagnoses in WA. Conclusions While rare cancers are individually scarce, collectively over one in five cancer patients in WA are diagnosed with a rare cancer. These patients experience significantly worse prognoses compared to patients with common cancers.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Descriptive epidemiological study of rare, less common and common cancers in Western Australia

Bilkey

Trevithick

Coles³

et al. 2021

BMC Cancer

View full text Add to dashboard Cite

show abstract

“…Histological variables were not included in the multivariable analyses as they were considered to be intermediate variables in the carcinogenic process, as previously described 17 . Bayesian methodology was chosen in all analyses with non-informative prior distribution due to lack of data on the distribution of the parameters 18 , 19 and, moreover, because Bayesian methodology is useful to (1) avoid model fitting problems in parameter estimation due to small counts, and (2) produce robust estimators 20 .…”

Section: Methodsmentioning

confidence: 99%

Epidemiology of human papillomavirus-related oropharyngeal cancer in a classically low-burden region of southern Europe

Mena

Frias-Gomez

Cequier

et al. 2020

Sci Rep

View full text Add to dashboard Cite

The incidence of human papillomavirus (HPV)-related oropharyngeal cancer is increasing in some regions. Nevertheless, the epidemiology of this disease has not been extensively investigated in southern europe. We conducted a retrospective cohort study of patients diagnosed with primary oropharyngeal cancer from 1991 to 2016. Cancer tissues underwent histopathological evaluation, DNA quality control, HPV-DNA detection and p16 INK4a immunohistochemistry. Data were collected from medical records. Factors associated with HPV positivity and time trends were evaluated with multivariable Bayesian models. The adjusted prevalence of HPV-related cases in 864 patients with a valid HPV-DNA result was 9.7%, with HPV-DNA/p16 INK4a double positivity being considered. HPVrelated oropharyngeal cancer was likely to occur in non-smokers and non-drinkers, to be located in the tonsil or diagnosed at advanced stages. Time-trend analysis showed an increasing risk of HPV-related oropharyngeal cancer in the most recent periods (5-year period increase of 30%). This increase was highest and with a clear increasing trend only in the most recent years (2012-2016). The prevalence of HPV-related oropharyngeal cancer started to sharply increase in the most recent years in our setting,

show abstract

“…Since many countries could only provide data for cancer cases in some regional areas, not at a national level, RARECARENet investigators estimated country-specific IRs (number of cases per 100,000 person-years) for RCs on the basis of cases recorded by 83 population-based cancer registries across 27 European countries ( 3 ) . More recently, in the context of the Joint Action on Rare Cancers, which is generating policy recommendations on RC that can be implemented by European Union member states ( 1 – 3 ), the burden of RC incidence counts in Europe was compared with another burden derived by a model-based approach that used a simple Poisson random-effects model under the Bayesian framework ( 4 ), through integrated nested Laplace approximations (INLAs) implemented in the INLA platform ( 5 ). Evidence suggests that INLA is appropriate for estimating the distribution of fixed-effect parameters, but it could fail to yield good estimates in a random-effects model ( 6 ).…”

Section: Abbreviationmentioning

confidence: 99%

“…Evidence suggests that INLA is appropriate for estimating the distribution of fixed-effect parameters, but it could fail to yield good estimates in a random-effects model ( 6 ). This last shortcoming could be related to the numerical method used in INLA to estimate the posterior distribution: Laplace approximation ( 4 , 6 ). This approximation is good for models close to a Gaussian distribution, but this may underestimate the variance of the random effects when modeling Poisson or binary data ( 4 , 6 ).…”

Section: Abbreviationmentioning

confidence: 99%

Estimating Country-Specific Incidence Rates of Rare Cancers: Comparative Performance Analysis of Modeling Approaches Using European Cancer Registry Data

Salmerón¹,

Botta

Martı́nez

et al. 2021

American Journal of Epidemiology

Self Cite

View full text Add to dashboard Cite

Estimating incidence for rare cancers (RC) is challenging for exceptionally rare entities and in small populations. In a previous study, the RARECAREnet project provided Bayesian estimates of the expected number of RCs and their 95% credible intervals for 27 European countries using data collected by population-based cancer registries. In that study, slightly different results were found by implementing a Poisson model in INLA/WinBUGS platforms. This paper has assessed the performance of a Poisson modelling approach for estimating RC incidence rates, oscillating around an overall European average and using small counts data in different scenarios/computational platforms. First, we compared the performance of Frequentist, Empirical Bayes and Bayesian approaches for providing 95% confidence/credible intervals for the expected rates in each country. Second, we carried out an empirical study using 190 RCs for assessing different lower/upper bounds of a uniform prior distribution for the standard deviation of the random effects. To obtain a reliable measure of variability for country-specific incidence rates, our results suggest the suitability of using 1 as lower bound for that prior distribution and selecting the random effects model through an averaged indicator derived from two Bayesian model selection criterions, the Deviance and Watanabe-Akaike Information Criterions (DIC and WAIC, respectively).

show abstract

Bayesian estimates of the incidence of rare cancers in Europe

Cited by 13 publications

References 12 publications

Descriptive epidemiological study of rare, less common and common cancers in Western Australia

Descriptive epidemiological study of rare, less common and common cancers in Western Australia

Epidemiology of human papillomavirus-related oropharyngeal cancer in a classically low-burden region of southern Europe

Estimating Country-Specific Incidence Rates of Rare Cancers: Comparative Performance Analysis of Modeling Approaches Using European Cancer Registry Data

Contact Info

Product

Resources

About