Segmented generation mortality

For a clear understanding of the inherent changes in mortality with advance in age, it is necessary to observe the experience of generations rather than the cross-section of period experience. In fact, the latter may produce a misleading picture as can be illustrated in the case of tuberculosis. Period experience pointed to a rise in tuberculosis death rates with advance in age, whereas generation experience indicates a decline with aging, The present paper proposes the study of mortality changes of all generations existing at one period to a subsequent period. Some characteristics and trends in the changes in generation mortality of white males and white females since 1900 are discussed. It is pointed out, in particular, that since World War II white males have been experiencing a rapidly increasing rise in generation mortality with the approach of midlife. A comparison of generation mortality changes from 1950 to 1960 among countries of traditionally low mortality shows that only males in the other English-speaking countries may be undergoing the same experience as white males in the United States upon the approach of midlife. It is not evident in the Scandinavian countries and the Netherlands and in the other countries. The reason for these differences is not apparent.     

Can efficiency be gained by correcting for misclassification?

This paper considers 2×2 tables arising from case–control studies in which the binary exposure may be misclassified. We found circumstances under which the inverse matrix method provides a more efficient odds ratio estimator than the naive estimator. We provide some intuition for the findings, and also provide a formula for obtaining the minimum size of a validation study such that the variance of the odds ratio estimator from the inverse matrix method is smaller than that of the naive estimator, thereby ensuring an advantage for the misclassification corrected result. As a corollary of this result, we show that correcting for misclassification does not necessarily lead to a widening of the confidence intervals, but, rather, in addition to producing a consistent estimate, can also produce one that is more efficient.     

Multivariate meta-analysis for data consortia,individual patient meta-analysis,and pooling projects

We discuss maximum likelihood and estimating equations methods for combining results from multiple studies in pooling projects and data consortia using a meta-analysis model, when the multivariate estimates with their covariance matrices are available. The estimates to be combined are typically regression slopes, often from relative risk models in biomedical and epidemiologic applications. We generalize the existing univariate meta-analysis model and investigate the efficiency advantages of the multivariate methods, relative to the univariate ones. We generalize a popular univariate test for between-studies homogeneity to a multivariate test. The methods are applied to a pooled analysis of type of carotenoids in relation to lung cancer incidence from seven prospective studies. In these data, the expected gain in efficiency was evident, sometimes to a large extent. Finally, we study the finite sample properties of the estimators and compare the multivariate ones to their univariate counterparts.     

Conclusions: Adolescents' Preparation for the Future

The future of societies depends on their success in providing pathways whereby young people develop and prepare themselves to be contributing adults to their communities. When these pathways are well marked, stable, supported by the community, and inviting to adolescents, a society can be confident that new generations will join the ranks of adulthood well prepared. When these paths are hazardous, unpredictable, uninviting, or ill fitted to the demands of adulthood in that society, the future is insecure, both for adolescents and for society. This volume and the additional two volumes of this study group (Brown, Larson, && Saraswathi, 2002; Mortimer && Brown, 2002) suggest the following observations about the experiences of youth and how well they are being prepared for adulthood in the 21st century.     

One-Hit Models of Carcinogenesis: Conservative or Not?

One-hit formulas are widely believed to be "conservative" when used to analyze carcinogenesis bioassays, in the sense that they will rarely underestimate risks of cancer at low exposures. Such formulas are generally applied to the lifetime incidence of cancer at a specific site, with risks estimated from animal data at zero dose (control), and two or more additional doses that are appreciable fractions of a maximum tolerated dose. No empirical study has demonstrated that the one-hit formula is conservative in the sense described. The Carcinogenesis Bioassay Database System contains data on 1212 separate bioassays of 308 chemical substances tested at exactly three evaluable doses. These provided sufficient data to examine 8432 specific combinations of cancer site with sex, species, and chemical. For each of these we fitted a one-hit formula to the zero and maximum dose data points, then examined the relation of the fitted curve to the incidence rate observed at the mid-dose, with and without adjustment for intercurrent mortality. Both underestimates and overestimates of risk at mid-dose occurred substantially more often than expected by chance. We cannot tell whether such underestimates would occur at lower doses, but offer six biological reasons why underestimates might be expected. In a high percentage of animal bioassays, the one-hit formula is not conservative when applied in the usual way to animal data. It remains possible that the one-hit formula may indeed be conservative at sufficiently low doses (below the observational range), but the usual procedure, applied to the usual dose range, can be nonconservative in estimating the slope of the formula at such low doses. Risk assessments for regulation of carcinogens should incorporate some measure of additional uncertainty.