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At least 433 records · Page 24Linked to original sources
PubMed2001

Numerical modelling of radon-222 entry into houses: an outline of techniques and results.

Numerical modelling is a powerful tool for studies of soil gas and radon-222 entry into houses. It is the purpose of this paper to review some main techniques and results. In the past, modelling has focused on Darcy flow of soil gas (driven by indoor-outdoor pressure differences) and combined diffusive and advective transport of radon. Models of different complexity have been used. The simpler ones are finite-difference models with one or two spatial dimensions. The more complex models allow for full three-dimensional and time dependency. Advanced features include: soil heterogeneity, anisotropy, fractures, moisture, non-uniform soil temperature, non-Darcy flow of gas, and flow caused by changes in the atmospheric pressure. Numerical models can be used to estimate the importance of specific factors for radon entry. Models are also helpful when results obtained in special laboratory or test structure experiments need to be extrapolated to more general situations (e.g. to real houses or even to other soil-gas pollutants). Finally, models provide a cost-effective test bench for improved designs of radon prevention systems. The paper includes a summary of transport equations and boundary conditions. As an illustrative example, radon entry is calculated for a standard slab-on-grade house.

Air Pollutants, Radioactive↗
PubMed2001

Population pharmacokinetics and pharmacokinetic-pharmacodynamic relationships for docetaxel.

The population approach has been implemented prospectively in the clinical development of docetaxel (Taxotere). Overall 640 patients were evaluable for the population PK/PD analysis. The PK analysis evidenced significant covariates explaining the inter-patient variability of docetaxel clearance and the PK/PD analysis demonstrated that the variability in clearance was a significant predictor of several safety endpoints. In patients with clinical chemistry suggestive of mild to moderate liver function impairment (SGOT and/or SGPT > 1.5 x ULN concomitant with alkaline phosphatase >2.5 x ULN), total body clearance was lowered by an average of 27%. Specific safety analyses demonstrated that these patients are at a significantly higher risk than others for the development of severe docetaxel-induced side effects. Population PK/PD data were fully integrated into the regulatory dossier and in the labeling of docetaxel worldwide. Population PK/PD models are being used to elaborate a simulation model to predict the survival of patients with non-small cell lung cancer treated with docetaxel.

Antineoplastic Agents, Phytogenic↗
PubMed2002

Residential radon exposure and lung cancer: variation in risk estimates using alternative exposure scenarios.

The most direct way to derive risk estimates for residential radon progeny exposure is through epidemiologic studies that examine the association between residential radon exposure and lung cancer. However, the National Research Council concluded that the inconsistency among prior residential radon case-control studies was largely a consequence of errors in radon dosimetry. This paper examines the impact of applying various epidemiologic dosimetry models for radon exposure assessment using a common data set from the Iowa Radon Lung Cancer Study (IRLCS). The IRLCS uniquely combined enhanced dosimetric techniques, individual mobility assessment, and expert histologic review to examine the relationship between cumulative radon exposure, smoking, and lung cancer. The a priori defined IRLCS radon-exposure model produced higher odds ratios than those methodologies that did not link the subject's retrospective mobility with multiple, spatially diverse radon concentrations. In addition, the smallest measurement errors were noted for the IRLCS exposure model. Risk estimates based solely on basement radon measurements generally exhibited the lowest risk estimates and the greatest measurement error. The findings indicate that the power of an epidemiologic study to detect an excess risk from residential radon exposure is enhanced by linking spatially disparate radon concentrations with the subject's retrospective mobility.

Air Pollutants, Radioactive↗
PubMed2005

Nonparametric tests of association of multiple genes with human disease.

The genetic basis of many common human diseases is expected to be highly heterogeneous, with multiple causative loci and multiple alleles at some of the causative loci. Analyzing the association of disease with one genetic marker at a time can have weak power, because of relatively small genetic effects and the need to correct for multiple testing. Testing the simultaneous effects of multiple markers by multivariate statistics might improve power, but they too will not be very powerful when there are many markers, because of the many degrees of freedom. To overcome some of the limitations of current statistical methods for case-control studies of candidate genes, we develop a new class of nonparametric statistics that can simultaneously test the association of multiple markers with disease, with only a single degree of freedom. Our approach, which is based on U-statistics, first measures a score over all markers for pairs of subjects and then compares the averages of these scores between cases and controls. Genetic scoring for a pair of subjects is measured by a "kernel" function, which we allow to be fairly general. However, we provide guidelines on how to choose a kernel for different types of genetic effects. Our global statistic has the advantage of having only one degree of freedom and achieves its greatest power advantage when the contrasts of average genotype scores between cases and controls are in the same direction across multiple markers. Simulations illustrate that our proposed methods have the anticipated type I-error rate and that they can be more powerful than standard methods. Application of our methods to a study of candidate genes for prostate cancer illustrates their potential merits, and offers guidelines for interpretation.

Case-Control Studies↗
PubMed2002

The potential for bias in Cohen's ecological analysis of lung cancer and residential radon.

Cohen's ecological analysis of US lung cancer mortality rates and mean county radon concentration shows decreasing mortality rates with increasing radon concentration (Cohen 1995 Health Phys. 68 157-74). The results prompted his rejection of the linear-no-threshold (LNT) model for radon and lung cancer. Although several authors have demonstrated that risk patterns in ecological analyses provide no inferential value for assessment of risk to individuals, Cohen advances two arguments in a recent response to Darby and Doll (2000 J. Radiol. Prot. 20 221-2) who suggest Cohen's results are and will always be burdened by the ecological fallacy. Cohen asserts that the ecological fallacy does not apply when testing the LNT model, for which average exposure determines average risk, and that the influence of confounding factors is obviated by the use of large numbers of stratification variables. These assertions are erroneous. Average dose determines average risk only for models which are linear in all covariates, in which case ecological analyses are valid. However, lung cancer risk and radon exposure, while linear in the relative risk, are not linearly related to the scale of absolute risk, and thus Cohen's rejection of the LNT model is based on a false premise of linearity. In addition, it is demonstrated that the deleterious association for radon and lung cancer observed in residential and miner studies is consistent with negative trends from ecological studies, of the type described by Cohen.

Air Pollutants, Radioactive↗
PubMed2003

Insights on bias and information in group-level studies.

Ecological and aggregate data studies are examples of group-level studies. Even though the link between the predictors and outcomes is not preserved in these studies, inference about individual-level exposure effects is often a goal. The disconnection between the level of inference and the level of analysis expands the array of potential biases that can invalidate the inference from group-level studies. While several sources of bias, specifically due to measurement error and confounding, may be more complex in group-level studies, two sources of bias, cross-level and model specification bias, are a direct consequence of the disconnection. With the goal of aligning inference from individual versus group-level studies, I discuss the interplay between exposure and study design. I specify the additional assumptions necessary for valid inference, specifically that the between- and within-group exposure effects are equal. Then cross-level inference is possible. However, all the information in the group-level analysis comes from between-group comparisons. Models where the group-level analysis provides even a small percentage of information about the within-group exposure effect are most susceptible to model specification bias. Model specification bias can be even more serious when the group-level model isn't derived from an individual-level model.

Analysis of Variance↗