Concordance of Carcinogenic Response between Rodent Species: Potency Dependence and Potential Underestimation

The use of average qualitative concordance between two bioassay endpoints is considered, with emphasis directed at agreement between rats and mice from results of long-term carcinogenicity studies. It is noted that concordance varies as a function of the underlying potency or toxicity of the chemicals over which the averaging is performed. Thus, the averaging process dilutes large observed concordances from potent chemicals, and possibly inflates lower observed concordances from weakly active chemicals. Stratification over some measure of potency is suggested as a method for taking these effects into account. Statistical simulations of concordance analyses limited to low-potency ranges are employed to examine the concordance measure in greater detail. It is seen that at low potencies, observed concordance is consistently underestimated, reaching maximum levels of only about 80%.     

The timing of motherhood: The challenge to social workers

The timing of the first birth has become a critical issue, to be addressed by the social work profession. This paper suggests that women who have timed the birth of their first child at two different stages in adulthood have responded to the challenges of motherhood in divergent ways. In order to work as effectively as possible with mothers and children, social workers need to understand these differences related to the timing of motherhood.Dr. Walter is an Assistant Professor of Social Work at Widener University and is in private practice.     

Evaluation of Developmental Toxicity Data: A Discussion of Some Pertinent Factors and a Proposal

There is currently no well-accepted standard method for evaluation of developmental toxicity data. This paper presents one approach to the evaluation of developmental toxicity data. We initially identify some pertinent factors that influence the interpretation of animal data and summarize the literature pertaining to these factors. Such factors include the quality and quantity of data and the relationship between maternal and developmental toxicity. We proceed with a discussion of quantitative assessment of data and propose schemes for qualitative and quantitative developmental hazard assessments.     

Transfers in Kind: Why They Can Be Efficient and Nonpaternalistic – Comment

Bruce and Waldman make an important contribution regarding the Samaritan dilemma: the expectations of inheritance motivates the younger generation at the margin in the direction of welfare reducing behavior. However, these authors misinterpret the standard analysis on this matter as prediction instead of explanation; confuse governmental subsidies with private charitable giving, and uncritically examine Headstart and statist job training programs.     

Redistribution and compensation

In a model where individual incomes depend on the agents' characteristics, we provide characterizations of several redistribution mechanisms. These mechanisms are designed to eliminate the effects of characteristics that are to be considered irrelevant, while preserving the influence of relevant characteristics on individual incomes. The mechanisms discussed here are egalitarian-equivalent and conditionally egalitarian mechanisms, as well as averaging versions of these rules.An earlier version of the paper was presented at the 1994 Canadian Economic Theory Conference in Toronto. We thank John Roemer and a referee for their comments. Financial support through a grant from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged.     

Quality out-of-school care in Aotearoa/New Zealand

The out-of-school-care sector, a reasonably unregulated industry, has grown quickly despite minimal government investment in comparison to early childhood care. In 2006, the New Zealand government released a draft five-year strategic plan committing an extra $17.4 million per year to this area. This article examines the impact on providers, recipients, and quality standards with the influx of resources and attention.     

Survival models and health sequences

Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique—reverse alignment—for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.     

Response to discussants of “survival models and health sequences”

Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique—reverse alignment—for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.