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Biomedical subjects

A P Dempster

Publications and source records attributed to A P Dempster.

5 recordsLinked to original sources

A stochastic differential equation model of diurnal cortisol patterns.

Circadian modulation of episodic bursts is recognized as the normal physiological pattern of diurnal variation in plasma cortisol levels. The primary physiological factors underlying these diurnal patterns are the ultradian timing of secretory events, circadian modulation of the amplitude of secretory events, infusion of the hormone from the adrenal gland into the plasma, and clearance of the hormone from the plasma by the liver. Each measured plasma cortisol level has an error arising from the cortisol immunoassay. We demonstrate that all of these three physiological principles can be succinctly summarized in a single stochastic differential equation plus measurement error model and show that physiologically consistent ranges of the model parameters can be determined from published reports. We summarize the model parameters in terms of the multivariate Gaussian probability density and establish the plausibility of the model with a series of simulation studies. Our framework makes possible a sensitivity analysis in which all model parameters are allowed to vary simultaneously. The model offers an approach for simultaneously representing cortisol's ultradian, circadian, and kinetic properties. Our modeling paradigm provides a framework for simulation studies and data analysis that should be readily adaptable to the analysis of other endocrine hormone systems.

Adrenal Glands↗

A Bayesian approach to the multiplicity problem for significance testing with binomial data.

Statistical analyses of simple tumor rates from an animal experiment with one control and one treated group typically consist of hypothesis testing of many 2 X 2 tables, one for each tumor type or site. The multiplicity of significance tests may cause excessive overall false-positive rates. This paper presents a Bayesian approach to the problem of multiple significance testing. We develop a normal logistic model that accommodates the incidences of all tumor types or sites observed in the current experiment simultaneously as well as their historical control incidences. Exchangeable normal priors are assumed for certain linear terms in the model. Posterior means, standard deviations, and Bayesian P-values are computed for an average treatment effect as well as for the effects on individual tumor types or sites. Model assumptions are checked using probability plots and the sensitivity of the parameter estimates to alternative priors is studied. The method is illustrated using tumor data from a chronic animal experiment.

Analysis of Variance↗

A Bayesian approach to bioequivalence for the 2 x 2 changeover design.

Bioequivalence trials are carried out to compare two or more formulations of a drug containing the same active ingredient, in order to determine whether the different formulation give rise to comparable blood levels. We consider the 2 x 2 changeover experiment the compares two formulations, one of which is considered the standard. For a single univariate characteristic of the plasma concentration--time curve, a criterion for bioequivalence is proposed based on the posterior probability that the difference in formulation means is less than a specific percentage of the mean of the standard. The sensitivity of this posterior probability to alternative priors is investigated. Differences in carry-over effects can be incorporated within the Bayesian framework without restoring to the "all-or-nothing" approach implied by a preliminary test. The use of sequential experimentation is discussed.

Bayes Theorem↗