Dataset Information

Frequentist and Bayesian approaches for food allergen risk assessment: risk outcome and uncertainty comparisons.

ABSTRACT: Peer-reviewed probabilistic methods already predict the probability of an allergic reaction resulting from an accidental exposure to food allergens, however, the methods calculate it in different ways. The available methods utilize the same three major input parameters in the risk model: the risk is estimated from the amount of food consumed, the concentration of allergen in the contaminated product and the distribution of thresholds among allergic persons. However, consensus is lacking about the optimal method to estimate the risk of allergic reaction and the associated uncertainty. This study aims to compare estimation of the risk of allergic reaction and associated uncertainty using different methods and suggest improvements. Four cases were developed based on the previous publications and the risk estimations were compared. The risk estimation was found to agree within 0.5% with the different simulation cases. Finally, an uncertainty analysis method is also presented in order to evaluate the uncertainty propagation from the input parameters to the risk.

SUBMITTER: Birot S

PROVIDER: S-EPMC6890679 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Frequentist and Bayesian approaches for food allergen risk assessment: risk outcome and uncertainty comparisons.

Birot Sophie S Crépet Amélie A Remington Benjamin C BC Madsen Charlotte B CB Kruizinga Astrid G AG Baumert Joseph L JL Brockhoff Per B PB

Scientific reports 20191203 1

Peer-reviewed probabilistic methods already predict the probability of an allergic reaction resulting from an accidental exposure to food allergens, however, the methods calculate it in different ways. The available methods utilize the same three major input parameters in the risk model: the risk is estimated from the amount of food consumed, the concentration of allergen in the contaminated product and the distribution of thresholds among allergic persons. However, consensus is lacking about th ...[more]

PMID: 31796875

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Project description:In order to have confidence in model-based phylogenetic methods, such as maximum likelihood (ML) and Bayesian analyses, one must use an appropriate model of molecular evolution identified using statistically rigorous criteria. Although model selection methods such as the likelihood ratio test and Akaike information criterion are widely used in the phylogenetic literature, model selection methods lack the ability to reject all models if they provide an inadequate fit to the data. There are two methods, however, that assess absolute model adequacy, the frequentist Goldman-Cox (GC) test and Bayesian posterior predictive simulations (PPSs), which are commonly used in conjunction with the multinomial log likelihood test statistic. In this study, we use empirical and simulated data to evaluate the adequacy of common substitution models using both frequentist and Bayesian methods and compare the results with those obtained with model selection methods. In addition, we investigate the relationship between model adequacy and performance in ML and Bayesian analyses in terms of topology, branch lengths, and bipartition support. We show that tests of model adequacy based on the multinomial likelihood often fail to reject simple substitution models, especially when the models incorporate among-site rate variation (ASRV), and normally fail to reject less complex models than those chosen by model selection methods. In addition, we find that PPSs often fail to reject simpler models than the GC test. Use of the simplest substitution models not rejected based on fit normally results in similar but divergent estimates of tree topology and branch lengths. In addition, use of the simplest adequate substitution models can affect estimates of bipartition support, although these differences are often small with the largest differences confined to poorly supported nodes. We also find that alternative assumptions about ASRV can affect tree topology, tree length, and bipartition support. Our results suggest that using the simplest substitution models not rejected based on fit may be a valid alternative to implementing more complex models identified by model selection methods. However, all common substitution models may fail to recover the correct topology and assign appropriate bipartition support if the true tree shape is difficult to estimate regardless of model adequacy.

Project description:BackgroundRapid increase in life expectancy in low- and middle-income countries including the World Health Organization's Southeast Asia Region (SEAR) has resulted in an increase in the global burden of dementia, which is expected to become a leading cause of morbidity. Accurate burden estimates are key for informing policy and planning. Given the paucity of data, estimates were developed using both a Bayesian methodology and as well as a traditional frequentist approach to gain better insights into methodological approaches for disease burden estimates.MethodsSeven databases were searched for studies published between 2010-2018 regarding dementia prevalence in SEAR, generating 8 relevant articles. A random-effects model (REM) and a Bayesian normal-normal hierarchical model (NNHM) were used to obtain the pooled prevalence estimate of dementia for people aged 60 and above in SEAR. The latter model was also developed to estimate age-specific dementia prevalence. Using UN population estimates for SEAR, total and age-specific projections of the burden of dementia in 2015, 2020 and 2030 were calculated.ResultsThe prevalence of dementia in SEAR was found to be 3% (95% confidence interval (CI) = 2-6%) in those above age 60 based on REM, and 3.1% (95% credible interval = 1.5-5.0%) based on the NNHM. The estimated prevalence varies with age, increasing from 1.6% (95% credible interval = 0.8-2.5%) in people aged 60-69 to 12.4% (95% credible interval = 5.6-20%) in people above the age of 80. The risk of developing dementia increased exponentially with age. The number of people living with dementia in SEAR in 2015 was estimated at 5.51 million (95% credible interval = 2.66-8.82), with projections of 6.66 million (95% credible interval = 3.21-10.7) in 2020 and 9.6 million (95% credible interval = 4.62-15.36) in 2030.ConclusionThe burden of dementia in SEAR is substantial and will continue to increase rapidly by 2030. The lack of research focusing on dementia in SEAR points to a significant under-recognition of this disease. The projected rise in dementia cases in the future should prompt urgent governmental response to address this growing public health issue. We also argue that given the overall paucity of data for the region, the Bayesian approach offers a promising methodology for improved estimates of disease prevalence and burden and should continue to be explored.

Dataset Information

Frequentist and Bayesian approaches for food allergen risk assessment: risk outcome and uncertainty comparisons.

Publications

Frequentist and Bayesian approaches for food allergen risk assessment: risk outcome and uncertainty comparisons.

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