Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data

This article describes the R package BinaryEPPM and its use in determining maximum likelihood estimates of the parameters of extended Poisson process models for grouped binary data. These provide a Poisson process family of flexible models that can handle unlimited under-dispersion but limited over-dispersion in such data, with the binomial distribution being a special case. Within BinaryEPPM, models with the mean and variance related to covariates are constructed to match a generalized linear model formulation. Combining such under-dispersed models with standard over-dispersed models such as the beta binomial distribution provides a very general form of residual distribution for modeling grouped binary data. Use of the package is illustrated by application to several data-sets

INSTITUT ZA TRAVNJAŠTVO I KRMNO BILJE

Context: Tumors producing insulin-like growth factor 2 (IGF-2oma) are a major cause of spontaneous hypoglycemia. The treatment mainstay is surgical resection. Many case reports note resolution of hypoglycemia after IGF-2oma resection; however, outcomes are variable according to tumor type. We report a case of resolving hypoglycemia, observed on continuous glucose monitoring (CGM), after resection of an IGF-2-producing solitary fibrous tumor, of pleura and review the current literature. Case Report: A 69-year-old woman presented with impaired consciousness because of hypoglycemia. An IGF-2oma was diagnosed as the cause for hypoglycemia because of decreased serum insulin and IGF-1, presence of a pleural tumor, and a high-molecular-weight form of serum IGF-2 detected by western immunoblot. Surgical resection was performed; pathological examination demonstrated a solitary fibrous tumor with low-grade malignancy. CGM showed reversal of hypoglycemia after tumor resection. Approximately 2 years after resection, the patient has no signs of tumor recurrence or hypoglycemia. Conclusions: An IGF-2-producing solitary fibrous tumor of pleura in this case caused hypoglycemia. From a search of the literature of 2004–2014, 32 cases of IGF-2oma with hypoglycemia that underwent radical surgery were identified; in 19 (59%) patients, hypoglycemia was reversed and there was no subsequent recurrence. The remaining 13 (41%) experienced tumor recurrence or metastasis and recurrence of hypoglycemia average 43 months after initial tumor resection. The tumor of the present case was a low-grade malignancy. Regular follow-up with biomarker-monitoring of glucose metabolism and assessment of hypoglycemic symptomatology, in conjunction with imaging tests, is important for detecting possible tumor recurrence and metastasis.PostprintPeer reviewe

Approximate Bayesian computation using indirect inference

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms

Flow Structure in a Model of Aircraft Trailing Vortices

We consider a model of incompressible trailing vortices consisting of an array of counter-rotating structures in a doubly periodic domain, infinite in the vertical direction. The two-dimensional vortex array of Mallier and Maslowe is combined with an axial velocity profile chosen proportional to the initial axial vorticity to provide an initial condition for the vortex wake. This base flow is a weak solution of the three component steady Euler equations in two dimensions thus allowing its linear stability properties to be investigated. These are used to interpret several stages in the development of vortex structure observed in fully three-dimensional DNS at Reynolds numbers Gamma/(2 pi nu)=O(1000). For sufficiently high axial velocity, itseffect can be seen, in that each vortex in the linear array first develops helical structures before undergoing a period of relaminarization. At later times the more slowly growing co-operative elliptical instabilities become apparent; however, the helical structure persists and the observed vortical structures remain coherent for longer periods than in the absence of axial velocity. Using the stretched vortex subgrid model, large-eddy simulation runs are performed at higher Reynolds numbers and a mixing transition identified at about Re = 1-2 x 10⁴. Similar phenomena are observed in these simulations as are seen in the DNS. Next the spatial nature of the true aircraft wake is compared to the temporal approximation commonly employed to simplify the computational complexity of the problem. A model is formulated to acount for the average axial pressure gradients that develops in the spatial wake but is absent from the temporal simulation. The model enables jet- and wake-like axial flows to be distinguished and the subtle differences in the ensuing turbulent states investigated. Finally, the model is used to investigate co-rotating vortex merger, the new thrust term providing a mechanism to enhance the axial flow further destabilizing the base flow

A fully Bayesian approach to inference for Coxian phase-type distributions with covariate dependent mean

Phase-type distributions represent the time to absorption for a finite state Markov chain in continuous time, generalising the exponential distribution and providing a flexible and useful modelling tool. We present a new reversible jump Markov chain Monte Carlo scheme for performing a fully Bayesian analysis of the popular Coxian subclass of phase-type models; the convenient Coxian representation involves fewer parameters than a more general phase-type model. The key novelty of our approach is that we model covariate dependence in the mean whilst using the Coxian phase-type model as a very general residual distribution. Such incorporation of covariates into the model has not previously been attempted in the Bayesian literature. A further novelty is that we also propose a reversible jump scheme for investigating structural changes to the model brought about by the introduction of Erlang phases. Our approach addresses more questions of inference than previous Bayesian treatments of this model and is automatic in nature. We analyse an example dataset comprising lengths of hospital stays of a sample of patients collected from two Australian hospitals to produce a model for a patient's expected length of stay which incorporates the effects of several covariates. This leads to interesting conclusions about what contributes to length of hospital stay with implications for hospital planning. We compare our results with an alternative classical analysis of these data

Approximate bayesian computation using auxiliary model based estimates

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data