Poisson regression with misclassified counts: application to cervical cancer

"Errors in disease classification can give misleading inferences for covariate effects when the probability of error is itself related to the covariates. More accurate inferences are possible using supplemental data on both true and fallible disease counts at various covariate levels. We present a method for incorporating such supplemental data into disease rate regression and use it to show that, although observed intercountry differences in European cervical cancer mortality rates are exaggerated by errors in death certification, they are not completely explained by such errors." The data concern Belgium, England and Wales, France, and Italy.     

Objective Testing Procedures in Linear Models: Calibration of the p-values

Abstract.  An optimal Bayesian decision procedure for testing hypothesis in normal linear models based on intrinsic model posterior probabilities is considered. It is proven that these posterior probabilities are simple functions of the classical F -statistic, thus the evaluation of the procedure can be carried out analytically through the frequentist analysis of the posterior probability of the null. An asymptotic analysis proves that, under mild conditions on the design matrix, the procedure is consistent. For any testing hypothesis it is also seen that there is a one-to-one mapping – which we call calibration curve – between the posterior probability of the null hypothesis and the classical bi p -value. This curve adds substantial knowledge about the possible discrepancies between the Bayesian and the p -value measures of evidence for testing hypothesis. It permits a better understanding of the serious difficulties that are encountered in linear models for interpreting the p -values. A specific illustration of the variable selection problem is given.     

Analyzing Cohort Mortality Data

Several methods for analyzing data from mortality studies of occupationally or environmentally exposed cohorts are shown to be special cases of a single procedure. The procedure assumes a proportional hazards model for exposure effects and represents the log-likelihood kernel for the data as that of N independent Poisson variates, where N is the total number of person-units of mortality observation time in the study. It formalizes and justifies the epidemiological techniques of classifying deaths and person-months of study time into categories defined by exposure and other covariates, and of computing standardized mortality ratios and indirectly standardized death rates. Parameters representing exposure effects can be estimated by using standard software packages. Special cases and applications are described in the context of lung cancer mortality among U.S. uranium miners.     

Long distance intimacy: class,gender and intergenerational relations between mothers and children in Filipino transnational families

Abstract In this article I address transnational intergenerational relations between Filipino migrant mothers and their young adult children and examine how families achieve intimacy across great distances. I do this by identifying and examining the transnational communication methods Filipino migrant families use to develop intimacy, in other words familiarity, across borders. In my analysis, I address how political economy and gender shape the dynamics of transnational communication. By showing how economic conditions and gender shape transnational family communication, I provide a socially thick lens through which to understand the formation of transnational intimacy and emphasize how larger systems of inequality shape the lives of the children left behind by the global migration of women.     

Use of Biological Markers in Risk Assessment1

Measurements of intermediate end points in the carcinogenic process may reduce uncertainty in human risk assessment from bioassay data, by identifying sources of interspecies variation and dose nonlinearity. This paper describes desirable properties of such markers: persistence, predictive power, temporal relevance, and consistency across dose rate and species. We illustrate these properties by evaluating markers for squamous cell nasal carcinoma in rodents exposed to formaldehyde. We also discuss design issues for bioassays that evaluate markers and tumors simultaneously at necropsy.     

Censored survival data with misclassified covariates: a case study of breast-cancer mortality

A number of alternative models are used to examine the relationship of survival among breast-cancer patients to the time since diagnosis and to the stage of the disease at diagnosis. The data concern 2,495 women aged 55-64 diagnosed with breast cancer in the San Francisco Bay area of California. In particular, the authors examine the extent to which the bad fit of simple models for breast-cancer survival is due to measurement error in the covariates.     

A Bayesian False Discovery Rate for Multiple Testing

Case-control studies of genetic polymorphisms and gene-environment interactions are reporting large numbers of statistically significant associations, many of which are likely to be spurious. This problem reflects the low prior probability that any one null hypothesis is false, and the large number of test results reported for a given study. In a Bayesian approach to the low prior probabilities, Wacholder et al. (2004) suggest supplementing the p-value for a hypothesis with its posterior probability given the study data. In a frequentist approach to the test multiplicity problem, Benjamini & Hochberg (1995) propose a hypothesis-rejection rule that provides greater statistical power by controlling the false discovery rate rather than the family-wise error rate controlled by the Bonferroni correction. This paper defines a Bayes false discovery rate and proposes a Bayes-based rejection rule for controlling it. The method, which combines the Bayesian approach of Wacholder et al. with the frequentist approach of Benjamini & Hochberg, is used to evaluate the associations reported in a case-control study of breast cancer risk and genetic polymorphisms of genes involved in the repair of double-strand DNA breaks.     

Errors-in-Variables Regression Using Stein Estimates

A method is proposed for estimating regression parameters from data containing covariate measurement errors by using Stein estimates of the unobserved true covariates. The method produces consistent estimates for the slope parameter in the classical linear errors-in-variables model and applies to a broad range of nonlinear regression problems, provided the measurement error is Gaussian with known variance. Simulations are used to examine the performance of the estimates in a nonlinear regression problem and to compare them with the usual naive ones obtained by ignoring error and with other estimates proposed recently in the literature.