Generalized Additive Models for Location Scale and Shape (GAMLSS) in R

GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to belong to the exponential family and includes highly skew and kurtotic continuous and discrete distribution. GAMLSS allows all the parameters of the distribution of the response variable to be modelled as linear/non-linear or smooth functions of the explanatory variables. This paper starts by defining the statistical framework of GAMLSS, then describes the current implementation of GAMLSS in R and finally gives four different data examples to demonstrate how GAMLSS can be used for statistical modelling.

Generalized Additive Models for Location Scale and Shape (GAMLSS) in R

GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to belong to the exponential family and includes highly skew and kurtotic continuous and discrete distribution. GAMLSS allows all the parameters of the distribution of the response variable to be modelled as linear/non-linear or smooth functions of the explanatory variables. This paper starts by defining the statistical framework of GAMLSS, then describes the current implementation of GAMLSS in R and finally gives four different data examples to demonstrate how GAMLSS can be used for statistical modelling

Estimating regional centile curves from mixed data sources and countries

Regional or national growth distributions can provide vital information on the health status of populations. In most resource poor countries, however, the required anthropometric data from purpose-designed growth surveys are not readily available. We propose a practical method for estimating regional (multi-country) age-conditional weight distributions based on existing survey data from different countries. We developed a two-step method by which one is able to model data with widely different age ranges and sample sizes. The method produces references both at the country level and at the regional (multi-country) level. The first step models country-specific centile curves by Box-Cox t and Box-Cox power exponential distributions implemented in generalized additive model for location, scale and shape through a common model. Individual countries may vary in location and spread. The second step defines the regional reference from a finite mixture of the country distributions, weighted by population size. To demonstrate the method we fitted the weight-for-age distribution of 12 countries in South East Asia and the Western Pacific, based on 273 270 observations. We modeled both the raw body weight and the corresponding Z score, and obtained a good fit between the final models and the original data for both solutions. We briefly discuss an application of the generated regional references to obtain appropriate, region specific, age-based dosing regimens of drugs used in the tropics. The method is an affordable and efficient strategy to estimate regional growth distributions where the standard costly alternatives are not an option. Copyright (c) 2009 John Wiley & Sons, Ltd

Randomly stopped sum models: a hydrological application

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Mean and dispersion modelling for policy claims costs

A model for the statistical analysis of the total amount of insurance paid out on a policy is developed and applied. The model simultaneously deals with the number of claims (zero or more) and the amount of each claim. The number of claims is from a Poisson-based discrete distribution. Individual claim sizes are from a continuous right skewed distribution. The resulting distribution of total claim size is a mixed discrete-continuous model, with positive probability of a zero claim. The means and dispersions of the claim frequency and claim size distribution are modeled in terms of risk factors. The model is applied to a car insurance data set.12 page(s

Developing regional weight-for-age growth references to optimize age-based dosing of antimalarials

Objective To derive regional weight-for-age growth references to help optimize age-based dosing of antimalarials in malaria-endemic areas of Africa, Latin America and Asia by using existing anthropometric data. Methods A weight-for-age database was constructed from pre-existing population-based anthropometric data obtained from household surveys and research groups. It contained data collected between 1995 and 2012 on 1 263 119 individuals (909 368 female, 353 751 male) aged over 14 days and under 50 years in 64 malaria-endemic countries. Regional growth references were generated using a generalized additive model for location, scale and shape by combining multisource data with varying distributions and subsequently pooling the data. Countries were weighted by their population at risk of malaria to enable references to be used in optimizing the dosing of antimalarials. Findings Large differences in weight-for-age distributions existed between Africa, Latin America and Asia and between these regions and global growth standards. For example, the average adult Latin American male weighed 68.1 kg – 6 kg more than males in Asia (average: 62.1 kg). For adult women, the difference was over 10 kg: the average was 60.4 kg in Latin America and 50.0 kg in Asia. Conclusion There were substantial variations in weight-for-age growth curves between malaria-endemic areas of Africa, Latin America and Asia. The growth reference charts derived here can be used to guide the evidence-based optimization of aged-based dosing regimens for antimalarials and of other drugs often prescribed by age

Developing regional weight-for-age growth references for malaria-endemic countries to optimize age-based dosing of antimalarials

Methods: A weight-for-age database was constructed from pre-existing population-based anthropometric data obtained from household surveys and research groups. It contained data collected between 1995 and 2012 on 1 263 119 individuals (909 368 female, 353 751 male) older than 14 days and younger than 50 years in 64 malaria-endemic countries. Regional growth references were generated using a generalized additive model for location, scale and shape by combining data with varying distributions from a range of sources. Countries were weighted by their population at risk of malaria to enable references to be used in optimizing the dosing of antimalarials. Findings: Large differences in weight-for-age distributions existed between the regions and between the regions and global growth standards. For example, the average adult male from the Americas weighed 68.1 kg – 6.0 kg more than males in South-East Asia and the Western Pacific (average: 62.1 kg). For adult women, the difference was over 10.4 kg: the average was 60.4 kg in the Americas and 50.0 kg in South-East Asia and the Western Pacific. Conclusion: There were substantial variations in weight-for-age growth curves between malaria-endemic areas. The growth reference charts derived here can be used to guide the evidence-based optimization of aged-based dosing regimens for antimalarials and other drugs often prescribed by age. Objective: To derive regional weight-for-age growth references to help optimize age-based dosing of antimalarials in Africa, the Americas, South-East Asia and the Western Pacific