Analysis of pulsatile hormone concentration profiles with nonconstant Basal concentration: a bayesian approach

Many challenges arise in the analysis of pulsatile, or episodic, hormone concentration time series data. Among these challenges is the determination of the number and location of pulsatile events and the discrimination of events from noise. Analyses of these data are typically performed in two stages. In the first stage, the number and approximate location of the pulses are determined. In the second stage, a model (typically a deconvolution model) is fit to the data conditional on the number of pulses. Any error made in the first stage is carried over to the second stage. Furthermore, current methods, except two, assume that the underlying basal concentration is constant. We present a fully Bayesian deconvolution model that simultaneously estimates the number of secretion episodes, as well as their locations, and a nonconstant basal concentration. This model obviates the need to determine the number of events a priori. Furthermore, we estimate probabilities for all "candidate" event locations. We demonstrate our method on a real data set.     

Semiparametric Bayesian analysis of case-control data under conditional gene-environment independence

In case-control studies of gene-environment association with disease, when genetic and environmental exposures can be assumed to be independent in the underlying population, one may exploit the independence in order to derive more efficient estimation techniques than the traditional logistic regression analysis (Chatterjee and Carroll, 2005, Biometrika92, 399-418). However, covariates that stratify the population, such as age, ethnicity and alike, could potentially lead to nonindependence. In this article, we provide a novel semiparametric Bayesian approach to model stratification effects under the assumption of gene-environment independence in the control population. We illustrate the methods by applying them to data from a population-based case-control study on ovarian cancer conducted in Israel. A simulation study is conducted to compare our method with other popular choices. The results reflect that the semiparametric Bayesian model allows incorporation of key scientific evidence in the form of a prior and offers a flexible, robust alternative when standard parametric model assumptions do not hold.     

Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP)

A timely determination of the risk of post‐traumatic stress disorder (PTSD) is a prerequisite for efficient service delivery and prevention. We provide a risk estimate tool allowing a calculation of individuals’ PTSD likelihood from early predictors. Members of the International Consortium to Predict PTSD (ICPP) shared individual participants’ item‐level data from ten longitudinal studies of civilian trauma survivors admitted to acute care centers in six countries. Eligible participants (N=2,473) completed an initial clinical assessment within 60 days of trauma exposure, and at least one follow‐up assessment 4‐15 months later. The Clinician‐Administered PTSD Scale for DSM‐IV (CAPS) evaluated PTSD symptom severity and diagnostic status at each assessment. Participants’ education, prior lifetime trauma exposure, marital status and socio‐economic status were assessed and harmonized across studies. The study's main outcome was the likelihood of a follow‐up PTSD given early predictors. The prevalence of follow‐up PTSD was 11.8% (9.2% for male participants and 16.4% for females). A logistic model using early PTSD symptom severity (initial CAPS total score) as a predictor produced remarkably accurate estimates of follow‐up PTSD (predicted vs. raw probabilities: r=0.976). Adding respondents’ female gender, lower education, and exposure to prior interpersonal trauma to the model yielded higher PTSD likelihood estimates, with similar model accuracy (predicted vs. raw probabilities: r=0.941). The current model could be adjusted for other traumatic circumstances and accommodate risk factors not captured by the ICPP (e.g., biological, social). In line with their use in general medicine, risk estimate models can inform clinical choices in psychiatry. It is hoped that quantifying individuals’ PTSD risk will be a first step towards systematic prevention of the disorder.     

Organoid-Induced Differentiation of Conventional T Cells from Human Pluripotent Stem Cells

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Does Pattern Scan Laser (PASCAL) photocoagulation really induce less VEGF expression in murine retina than conventional laser treatment?

To investigate the differences in the mRNA and protein expression levels of vascular endothelial growth factor (VEGF) in murine retina between mice subjected to conventional laser (AG) and those subjected to Pattern Scan Laser (PASCAL) system. Male C57BL/6 mice were randomly assigned to one of three groups: Group 1 (G1) receiving retinal scatter laser photocoagulation using with AG photocoagulator (n = 16), Group 2 (G2) receiving retinal scatter laser photocoagulation using with PASCAL (n = 16) and Group 3 (G3) served as an untreated control group (n = 6). Molecular and morphological analyses of VEGF were performed on days 1, 2 and 5 by ELISA, real-time PCR and immuno-histochemical analysis. In samples which underwent AG (G1), when compared with the control group (G3), VEGF mRNA level increased 2.4 folds on day 2, whereas it decreased on day 5 (p □ 0.001). In samples which underwent PASCAL (G2), on the other hand, VEGF mRNA level increased 1.8 folds on day 1 and 2.2 folds on day 5 when compared with the control group (G3). In samples which underwent AG (G1), when compared with the control group (G3), VEGF protein level increased significantly on day 2, whereas it decreased on day 5 (p □ 0.001). In group G2, the VEGF levels in the sensory retina significantly increased as compared to control groups at both 2 and 5 days after laser photocoagulation using PASCAL laser (p = 0.012, both time points). The peak expressions of VEGF protein in samples which underwent PASCAL and conventional laser were found on day 5 and day 2 respectively. In retinas of PASCAL-treated mice, VEGF immunoreactivity gradually increased during the 5-day follow-up. However, in argon laser group, the strongest VEGF immunoreactivity was detected on day 2, then started to decrease on day 5. In summary, the expression of VEGF protein and mRNA gradually increase during a 5-day follow-up period in PASCAL-treated mouse eyes, whereas in AG group they reach their peak levels on the second day of follow-up and started decreasing after then. These results may also explain why the PASCAL system is less effective in regressing neovascularization in the clinic.     

Determining the effective sample size of a parametric prior

Summary .   We present a definition for the effective sample size of a parametric prior distribution in a Bayesian model, and propose methods for computing the effective sample size in a variety of settings. Our approach first constructs a prior chosen to be vague in a suitable sense, and updates this prior to obtain a sequence of posteriors corresponding to each of a range of sample sizes. We then compute a distance between each posterior and the parametric prior, defined in terms of the curvature of the logarithm of each distribution, and the posterior minimizing the distance defines the effective sample size of the prior. For cases where the distance cannot be computed analytically, we provide a numerical approximation based on Monte Carlo simulation. We provide general guidelines for application, illustrate the method in several standard cases where the answer seems obvious, and then apply it to some nonstandard settings.     

Objective Bayesian analysis for the Student-t regression model

We develop a Bayesian analysis based on two different Jeffreyspriors for the Student-t regression model with unknown degreesof freedom. It is typically difficult to estimate the numberof degrees of freedom: improper prior distributions may leadto improper posterior distributions, whereas proper prior distributionsmay dominate the analysis. We show that Bayesian analysis witheither of the two considered Jeffreys priors provides a properposterior distribution. Finally, we show that Bayesian estimatorsbased on Jeffreys analysis compare favourably to other Bayesianestimators based on priors previously proposed in the literature.