K Claxton — Page 2 | PubMed Health

PubMedDate not supplied

Hydrogeologic assessment of exposure to solvent-contaminated drinking water: pregnancy outcomes in relation to exposure.

We recently concluded that exposure to solvent-contaminated drinking water was an unlikely explanation for observed excesses of adverse pregnancy outcomes during 1980-1981 in the Los Paseos neighborhood of Santa Clara County, California, because these excesses were not observed in an adjacent exposed area. The validity of this conclusion depends on the assumption that the two areas had comparable exposure. Using quantitative methods to model movement of the solvent leak plume and water flow within the distribution system, we estimated that women with adverse outcomes were no more likely to have received contaminated water than women with normal live births. These results strengthen the conclusion that exposures to water from the contaminated well were not responsible for the excess of adverse outcomes observed in the Los Paseos area.

Abnormalities, Drug-Induced↗

PubMedDate not supplied

Expected value of sample information calculations in medical decision modeling.

There has been an increasing interest in using expected value of information (EVI) theory in medical decision making, to identify the need for further research to reduce uncertainty in decision and as a tool for sensitivity analysis. Expected value of sample information (EVSI) has been proposed for determination of optimum sample size and allocation rates in randomized clinical trials. This article derives simple Monte Carlo, or nested Monte Carlo, methods that extend the use of EVSI calculations to medical decision applications with multiple sources of uncertainty, with particular attention to the form in which epidemiological data and research findings are structured. In particular, information on key decision parameters such as treatment efficacy are invariably available on measures of relative efficacy such as risk differences or odds ratios, but not on model parameters themselves. In addition, estimates of model parameters and of relative effect measures in the literature may be heterogeneous, reflecting additional sources of variation besides statistical sampling error. The authors describe Monte Carlo procedures for calculating EVSI for probability, rate, or continuous variable parameters in multi parameter decision models and approximate methods for relative measures such as risk differences, odds ratios, risk ratios, and hazard ratios. Where prior evidence is based on a random effects meta-analysis, the authors describe different ESVI calculations, one relevant for decisions concerning a specific patient group and the other for decisions concerning the entire population of patient groups. They also consider EVSI methods for new studies intended to update information on both baseline treatment efficacy and the relative efficacy of 2 treatments. Although there are restrictions regarding models with prior correlation between parameters, these methods can be applied to the majority of probabilistic decision models. Illustrative worked examples of EVSI calculations are given in an appendix.

Algorithms↗

PubMedDate not supplied

The FDA's regulation of health economic information.

Section 114 of the Food and Drug Administration Modernization Act of 1997 was intended to increase the flow of health economic information from pharmaceutical manufacturers to managed care decisionmakers. But the legislation raises a host of complex questions and has provoked diverse opinions from inside and outside the pharmaceutical industry. Moreover, the Food and Drug Administration (FDA) has yet to issue interpretative guidance on the subject. The challenge in implementing Section 114 lies in developing a policy that improves health economic information exchange while protecting consumers from misleading claims and preserving incentives for manufacturers to conduct rigorous studies.

Drug Industry↗