Designing for a Response Transformation Parameter

We investigate D -optimum designs for experiments in which a linear model holds after an unknown power transformation of the univariate response variable. This is a departure from standard D -optimal design in which an appropriate scale for the response is assumed known before data collection. The design problem that we formulate is intrinsically non-linear, requiring characterization of likely parameter values. Several applications are presented to illustrate the importance of recognizing the role of response transformations at the design stage.     

Multimedia Benchmarking Analysis for Three Risk Assessment Models: RESRAD, MMSOILS, and MEPAS

This paper is one in a series that describes results of a benchmarking analysis initiated by the Department of Energy (DOE) and the United States Environmental Protection Agency (EPA). An overview of the study is provided in a companion paper by Laniak et al. presented in this journal issue. The three models used in the study—RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)—represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. Both single media and multimedia benchmarking scenarios were developed and executed. In this paper, the multimedia scenario is examined. That scenario consists of a hypothetical landfill that initially contained uranium-238 and methylene chloride. The multimedia models predict the fate of these contaminants, plus the progeny of uranium-238, through the unsaturated zone, saturated zone, surface water, and atmosphere. Carcinogenic risks are calculated from exposure to the contaminants via multiple pathways. Results of the tests show that differences in model endpoint estimates arise from both differences in the models' mathematical formulations and assumptions related to the implementation of the scenarios.     

Consumer evaluation of community treatment

Development of community programs for offenders requires innovation in program design and evaluation. The Complex Offender Project (COP) provided intensive community treatment, and its clients' opinions were used as a component of its evaluation. Treated clients perceived the same needs for services as did other offenders, but more of them reported receiving the services. Compared to probation, COP was perceived to be more helpful in resolving problems, more continuously helpful, and of helping more to learn useful skills and achieve meaningful goals. The Complex Offender Project provides a model for program development, and the techniques of consumer evaluation are recommended as a means of evaluating such programs.     

The Benefits of Probabilistic Exposure Assessment: Three Case Studies Involving Contaminated Air,Water, and Soil1

Probabilistic risk assessments are enjoying increasing popularity as a tool to characterize the health hazards associated with exposure to chemicals in the environment. Because probabilistic analyses provide much more information to the risk manager than standard “point” risk estimates, this approach has generally been heralded as one which could significantly improve the conduct of health risk assessments. The primary obstacles to replacing point estimates with probabilistic techniques include a general lack of familiarity with the approach and a lack of regulatory policy and guidance. This paper discusses some of the advantages and disadvantages of the point estimate vs. probabilistic approach. Three case studies are presented which contrast and compare the results of each. The first addresses the risks associated with household exposure to volatile chemicals in tapwater. The second evaluates airborne dioxin emissions which can enter the food-chain. The third illustrates how to derive health-based cleanup levels for dioxin in soil. It is shown that, based on the results of Monte Carlo analyses of probability density functions (PDFs), the point estimate approach required by most regulatory agencies will nearly always overpredict the risk for the 95^th percentile person by a factor of up to 5. When the assessment requires consideration of 10 or more exposure variables, the point estimate approach will often predict risks representative of the 99.9^th percentile person rather than the 50^th or 95^th percentile person. This paper recommends a number of data distributions for various exposure variables that we believe are now sufficiently well understood to be used with confidence in most exposure assessments. A list of exposure variables that may require additional research before adequate data distributions can be developed are also discussed.     

The effects of demographic factors,family background,and early job achievement on age at marriage

National data for ever-married men aged 20 to 65 in March 1973 are utilized to estimate least squares and log-linear structural equation models of age at marriage. We demonstrate that most characteristics of family background (including both the family structure and its socioeconomic standing) are irrelevant in their effect on age at marriage. Intercohort trends are not explicable with reference to the changing socioeconomic, ethnic, or nativity compositions of the cohorts. Regional differences in age at marriage have persisted over the years in only slightly diminished form and cannot be explained by reference to the nativity, ethnic, or socioeconomic compositions of the regions. Early job status relates only weakly to age at marriage. Only those activities that are time-consuming or otherwise disruptive of the smooth operation of normal life-cycle processes during the transition from adolescence to adulthood (such as college attendance and service in the military) seriously affect the age at which a man marries.