Flexible Tweedie regression models for continuous data

Tweedie regression models provide a flexible family of distributions to deal with non-negative highly right-skewed data as well as symmetric and heavy tailed data and can handle continuous data with probability mass at zero. The estimation and inference of Tweedie regression models based on the maximum likelihood method are challenged by the presence of an infinity sum in the probability function and non-trivial restrictions on the power parameter space. In this paper, we propose two approaches for fitting Tweedie regression models, namely, quasi- and pseudo-likelihood. We discuss the asymptotic properties of the two approaches and perform simulation studies to compare our methods with the maximum likelihood method. In particular, we show that the quasi-likelihood method provides asymptotically efficient estimation for regression parameters. The computational implementation of the alternative methods is faster and easier than the orthodox maximum likelihood, relying on a simple Newton scoring algorithm. Simulation studies showed that the quasi- and pseudo-likelihood approaches present estimates, standard errors and coverage rates similar to the maximum likelihood method. Furthermore, the second-moment assumptions required by the quasi- and pseudo-likelihood methods enables us to extend the Tweedie regression models to the class of quasi-Tweedie regression models in the Wedderburn's style. Moreover, it allows to eliminate the non-trivial restriction on the power parameter space, and thus provides a flexible regression model to deal with continuous data. We provide \texttt{R} implementation and illustrate the application of Tweedie regression models using three data sets.Comment: 34 pages, 8 figure

Extended poisson–tweedie: properties and regression models for count data

We propose a new class of discrete generalized linear models based on the class of Poisson-Tweedie factorial dispersion models with variance of the form mu + phi mu(p), where mu is the mean and phi and p are the dispersion and Tweedie power parameters, respectively. The models are fitted by using an estimating function approach obtained by combining the quasi-score and Pearson estimating functions for the estimation of the regression and dispersion parameters, respectively. This provides a flexible and efficient regression methodology for a comprehensive family of count models including Hermite, Neyman Type A, Polya-Aeppli, negative binomial and Poisson-inverse Gaussian. The estimating function approach allows us to extend the Poisson-Tweedie distributions to deal with underdispersed count data by allowing negative values for the dispersion parameter phi. Furthermore, the Poisson-Tweedie family can automatically adapt to highly skewed count data with excessive zeros, without the need to introduce zero-inflated or hurdle components, by the simple estimation of the power parameter. Thus, the proposed models offer a unified framework to deal with under-, equi-, overdispersed, zero-inflated and heavy-tailed count data. The computational implementation of the proposed models is fast, relying only on a simple Newton scoring algorithm. Simulation studies showed that the estimating function approach provides unbiased and consistent estimators for both regression and dispersion parameters. We highlight the ability of the Poisson-Tweedie distributions to deal with count data through a consideration of dispersion, zero-inflated and heavy tail indices, and illustrate its application with four data analyses. We provide an R implementation and the datasets as supplementary materials

Familial Risk and Heritability of Hematologic Malignancies in the Nordic Twin Study of Cancer

Simple Summary Hematologic malignancies account for 8-9% of all incident cancers. Both genetic and environmental risk factors contribute to cancer development, but it is unclear if there is shared heritability between hematologic malignancies. This study aimed to investigate familial predisposition to hematologic malignancies using the largest twin study of cancer in the world. We compared individual risk in the general population and the risk of cancer in one twin before some age given that the other twin had (another) cancer before that age. Furthermore, by analyzing information about whether the twins were identical or fraternal, we could estimate the relative importance of genetic and environmental influences on the risk for developing hematologic cancers. This study confirmed previous findings of familial predisposition to hematologic malignancies and provides novel evidence that familial predisposition decreases with increasing age. The latter points to the importance of taking age into account in the surveillance of hematological cancers. We aimed to explore the genetic and environmental contributions to variation in the risk of hematologic malignancies and characterize familial dependence within and across hematologic malignancies. The study base included 316,397 individual twins from the Nordic Twin Study of Cancer with a median of 41 years of follow-up: 88,618 (28%) of the twins were monozygotic, and 3459 hematologic malignancies were reported. We estimated the cumulative incidence by age, familial risk, and genetic and environmental variance components of hematologic malignancies accounting for competing risk of death. The lifetime risk of any hematologic malignancy was 2.5% (95% CI 2.4-2.6%), as in the background population. This risk was elevated to 4.5% (95% CI 3.1-6.5%) conditional on hematologic malignancy in a dizygotic co-twin and was even greater at 7.6% (95% CI 4.8-11.8%) if a monozygotic co-twin had a hematologic malignancy. Heritability of the liability to develop any hematologic malignancy was 24% (95% CI 14-33%). This estimate decreased across age, from approximately 55% at age 40 to about 20-25% after age 55, when it seems to stabilize. In this largest ever studied twin cohort with the longest follow-up, we found evidence for familial risk of hematologic malignancies. The discovery of decreasing familial predisposition with increasing age underscores the importance of cancer surveillance in families with hematological malignancies.Peer reviewe

Extended poisson–tweedie: properties and regression models for count data

We propose a new class of discrete generalized linear models based on the class of Poisson-Tweedie factorial dispersion models with variance of the form mu + phi mu(p), where mu is the mean and phi and p are the dispersion and Tweedie power parameters, respectively. The models are fitted by using an estimating function approach obtained by combining the quasi-score and Pearson estimating functions for the estimation of the regression and dispersion parameters, respectively. This provides a flexible and efficient regression methodology for a comprehensive family of count models including Hermite, Neyman Type A, Polya-Aeppli, negative binomial and Poisson-inverse Gaussian. The estimating function approach allows us to extend the Poisson-Tweedie distributions to deal with underdispersed count data by allowing negative values for the dispersion parameter phi. Furthermore, the Poisson-Tweedie family can automatically adapt to highly skewed count data with excessive zeros, without the need to introduce zero-inflated or hurdle components, by the simple estimation of the power parameter. Thus, the proposed models offer a unified framework to deal with under-, equi-, overdispersed, zero-inflated and heavy-tailed count data. The computational implementation of the proposed models is fast, relying only on a simple Newton scoring algorithm. Simulation studies showed that the estimating function approach provides unbiased and consistent estimators for both regression and dispersion parameters. We highlight the ability of the Poisson-Tweedie distributions to deal with count data through a consideration of dispersion, zero-inflated and heavy tail indices, and illustrate its application with four data analyses. We provide an R implementation and the datasets as supplementary materials

Effect of pegylated phosphatidylserine-containing liposomes in experimental chronic arthritis

Background: Phosphatidylserine-containing liposomes (PSL) have been shown to reduce inflammation in experimental models of acute arthritis, by mimicking the apoptotic process. The aim of this study was to evaluate the effect of pegylated PSL (PEG-PSL) on chronic inflammation of collagen induced arthritis (CIA) in DBA/1J mice. Methods: CIA was induced in 24 DBA/1J mice (n = 6/group), which were divided into control (0.9 % saline) or treated with PEG-PSL (5, 10 and 15 mg/kg/day, subcutaneously for 20 days). Clinical score, limb histology and measurement of cytokines in knee joints of animals by ELISA and cytometric bead array (CBA) were evaluated. The in vitro study employed macrophage cultures stimulated with 100 ng/ml of LPS plus 10 ng/ml of PMA and treated with 100 μM PEG-PSL. Results: Resolution of the disease in vivo and the inflammatory process in vitro were not observed. PEG-PSL, in doses of 10 and 15 mg/kg, were not shown to reduce the score of the disease in animals, whereas with the dose of 5 mg/kg, the animals did not show the advanced stage of the disease when compared to the controls. The PEG- PSL 5, 10 and 15 mg/kg treatment groups did not show significant reduction of TNF-α, IL-1β, IL-6, IL-2 and IFN-γ when compared to the controls. Disease incidence and animal weights were not affected by treatment. Regarding the paw histology, PEG-PSL did not yield any reductions in the infiltrating mononuclear, synovial hyperplasia, extension of pannus formation, synovial fibrosis, erosion of cartilage, bone erosion or cartilage degradation. The concentration of 100 μM of PEG-PSL has not been shown to reduce inflammation induced by LPS/PMA in the in vitro study. Treated groups did not show any reduction in inflammatory cytokines in the knee joints of animals affected by the disease compared to the control, although there were higher concentrations of TGF-β1 in all experimental groups. Conclusion: The experimental model showed an expression of severe arthritis after the booster. TGF-β1 as well other pro inflammatory cytokines were presented in high concentrations in all groups. PEG-PSL had no impact on the clinical score, the histopathology from tibial-tarsal joints or the production of cytokines in the knee joints. Other alternatives such as dosage, route of administration, and as an adjunct to a drug already on the market, should be evaluated to support the use of PEG-PSL as a new therapeutic tool in inflammatory diseases