Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .

Dilution and redistribution effects of rapid 2-litre infusions of 0.9% (w/v) saline and 5% (w/v) dextrose on haematological parameters and serum biochemistry in normal subjects: a double-blind crossover study

Although hypoalbuminaemia after injury may result from increased vascular permeability, dilution secondary to crystalloid infusions may contribute significantly. In this double-blind crossover study, the effects of bolus infusions of crystalloids on serum albumin, haematocrit, serum and urinary biochemistry and bioelectrical impedance analysis were measured in healthy subjects. Ten male volunteers received 2-litre infusions of 0.9% (w/v) saline or 5% (w/v) dextrose over 1 h; infusions were carried out on separate occasions, in random order. Weight, haemoglobin, serum albumin, serum and urinary biochemistry and bioelectrical impedance were measured pre-infusion and hourly for 6 h. The serum albumin concentration fell in all subjects (20% after saline; 16% after dextrose) by more than could be explained by dilution alone. This fall lasted more than 6 h after saline infusion, but values had returned to baseline 1 h after the end of the dextrose infusion. Changes in haematocrit and haemoglobin were less pronounced (7.5% after saline; 6.5% after dextrose). Whereas all the water from dextrose was excreted by 2 h after completion of the infusion, only one-third of the sodium and water from the saline had been excreted by 6 h, explaining its persistent diluting effect. Impedances rose after dextrose and fell after saline (P<0.001). Subjects voided more urine (means 1663 and 563 ml respectively) of lower osmolality (means 129 and 630 mOsm/kg respectively) and sodium content (means 26 and 95 mmol respectively) after dextrose than after saline (P<0.001). While an excess water load is excreted rapidly, an excess sodium load is excreted very slowly, even in normal subjects, and causes persistent dilution of haematocrit and serum albumin. The greater than expected change in serum albumin concentration when compared with that of haemoglobin suggests that, while dilution is responsible for the latter, redistribution also has a role in the former. Changes in bioelectrical impedance may reflect the electrolyte content rather than the volume of the infusate, and may be unreliable for clinical purposes.

Identifiability of diffusion and sorption parameters from in situ diffusion experiments by using simultaneously tracer dilution and claystone data

In situ diffusion experiments are performed in geological formations at underground research laboratories to overcome the limitations of laboratory diffusion experiments and investigate scale effects. Tracer concentrations are monitored at the injection interval during the experiment (dilution data) and measured from host rock samples around the injection interval at the end of the experiment (overcoring data). Diffusion and sorption parameters are derived from the inverse numerical modeling of the measured tracer data. The identifiability and the uncertainties of tritium and Na-22(+) diffusion and sorption parameters are studied here by synthetic experiments having the same characteristics as the in situ diffusion and retention (DR) experiment performed on Opalinus Clay. Contrary to previous identifiability analyses of in situ diffusion experiments, which used either dilution or overcoring data at approximate locations, our analysis of the parameter identifiability relies simultaneously on dilution and overcoring data, accounts for the actual position of the overcoring samples in the claystone, uses realistic values of the standard deviation of the measurement errors, relies on model identification criteria to select the most appropriate hypothesis about the existence of a borehole disturbed zone and addresses the effect of errors in the location of the sampling profiles. The simultaneous use of dilution and overcoring data provides accurate parameter estimates in the presence of measurement errors, allows the identification of the right hypothesis about the borehole disturbed zone and diminishes other model uncertainties such as those caused by errors in the volume of the circulation system and the effective diffusion coefficient of the filter. The proper interpretation of the experiment requires the right hypothesis about the borehole disturbed zone. A wrong assumption leads to large estimation errors. The use of model identification criteria helps in the selection of the best model. Small errors in the depth of the overcoring samples lead to large parameter estimation errors. Therefore, attention should be paid to minimize the errors in positioning the depth of the samples. The results of the identifiability analysis do not depend on the particular realization of random numbers. (C) 2012 Elsevier B.V. All rights reserved.