A Bayesian Methodology for Scaling Radiation Studies from Animals to Man

Abstract

This paper describes a Bayesian methodology for integrating studies in experimental animals and humans to obtain a risk estimate for a radionuclide for which no data or very limited human data are available. The method is quite general and is not limited to radiation studies. In fact, it was first developed for chemical toxicants. The methodology is illustrated using studies with rats, beagles, and humans exposed to isotopes of Ra and Pu. The goal is a quantitative risk estimate for bone cancer in humans exposed to internally deposited Pu. The choice of bone cancer as an end point and of Pu as the source of exposure was made partially because of its inherent interest but also because of issues of data availability and suitability. We performed Poisson regression analyses on 13 of 15 data sets. These analyses form the basis for the unifying method of interpreting the entire ensemble of studies. Each of the studies is summarized by the estimated dose-response slope and its estimated standard error. These summary statistics are combined with other available biological and physical information about species differences, physical and metabolic characteristics of isotopes, disease mechanisms, and the like. This information enters the analysis in the form of prior assumptions about the parameters of the Bayesian model combining the studies. The posterior distribution for the bone cancer rate in man from the Bayesian analysis of the 13 studies is updated with the limited data on Pu in humans. This update gives the final probability density for the bone cancer rate in humans exposed to internally deposited Pu. This density has a median of about three cancers per 100 Gy and has a 95% probability interval from 0.8 to 11 bone cancers per 100 Gy.

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

“…Similarly, interventions E + FE + F and FE were not trialled by any of the included studies on possession of a PCC number. To estimate the full set of 24 basic intervention effects relative to usual care from 9 interventions on 3 outcomes, we applied methods originally proposed by DuMouchel and Harris [29] and revisited by DuMouchel and Groer [45] and Jones et al [30]. We assume that the pooled effects of treatment k relative to usual care intervention d ( Ak ) m , can be expressed as a sum of treatment-specific effect α k and outcome-specific effect γ m .…”

Development of an internet based system for modeling biotin metabolism using Bayesian networks

“…The exception is the parameters related to urine biotin levels (shaded node) with which we intend to show that connections could be made to experiments in other species (mouse, rat, etc.) with certain assumptions on parameters by utilizing cross-design synthesis techniques [15]. This feature might make superfluous costly studies in human subjects and also reflects another advantage of Bayesian networks, i.e., the network can be grown gradually with availability of new data sources or expanded interest of researchers.…”

Bayesian Methods for the Cross-Design Synthesis of Epidemiological and Toxicological Evidence

“…There has been relatively little exploration of methods for quantitative synthesis of evidence from human and animal studies, or even of toxicological studies alone (Roberts et al , 2002b; Sandercock and Roberts, 2002). However, DuMouchel and Harris (1983) and DuMouchel and Groër (1989) have investigated alternative Bayesian models for combining dose–response slopes from animal and human studies.…”