Biomedical subjects

Timothy Ramsay

Publications and source records attributed to Timothy Ramsay.

3 recordsLinked to original sources

PubMed2005

The effect of censoring on cancer risk estimates based on the Canadian National Dose Registry of occupational radiation exposure.

Cohort studies represent an important epidemiological tool for exploring the potential adverse health effects of low-dose exposure to ionizing radiation in the workplace. Analyses of data from the National Dose Registry of Canada have suggested that occupational radiation exposure leads to increased risk of several specific types of cancer, as well as increased overall risk of cancer. An important aspect of such studies is the censoring in recorded exposures induced by dosimetry detection limits. Such a censoring effect can lead to significant underestimation of cumulative doses which, in turn, can result in overestimation of the excess cancer risk associated with occupational radiation exposure. In this article, we present analytic results, supported by a simulation study, on the magnitude of overestimation of risk based on the additive relative risk model used in the analysis of the NDR data that can occur due to censoring. Our results indicate that overestimation of risk is modest, being less than 20% in all situations considered here. Because censoring also results in ovestimation of the precision of the risk estimates, the significance levels of Wald-type statistical tests for increased risk based on the ratio of the estimate to its standard error are virtually unaffected by censoring. These results suggest that although the application of the additive excess relative risk model in the presence of censoring may lead to some overestimation of risk, the model does not lead to invalid conclusions regarding the association between occupational radiation exposure and cancer risk based on data from the NDR.

Canada↗

PubMed2005

A comparison of four different methods for outlier detection in bioequivalence studies.

Bioequivalence studies, required by law whenever a new formulation of an existing drug product is introduced to the market, are designed to test whether the bioavailability, defined as the rate and extent to which a substance reaches systemic circulation, is equivalent for each of two or more formulations. Detection and treatment of outlying data in bioequivalence studies are practically important, because inclusion or deletion of potential outlying data may lead to a different conclusion concerning bioequivalence. A review of the literature reveals that four different methods have been proposed for detecting outliers in bioavailability/bioequivalence studies. We present the results of an extensive computer simulation testing the small sample performance of these four testing methods, the results of which indicate that one of these, the estimates distance test, is substantially more powerful than the alternatives.

Clinical Trials as Topic↗

PubMed2003

Exploring bias in a generalized additive model for spatial air pollution data.

During the past few years, the generalized additive model (GAM) has become a standard tool for epidemiologic analysis exploring the effect of air pollution on population health. Recently, the use of the GAM has been extended from time-series data to spatial data. Still more recently, it has been suggested that the use of GAMs to analyze time-series data results in air pollution risk estimates being biased upward and that concurvity in the time-series data results in standard error estimates being biased downward. We show that concurvity in spatial data can lead to underestimation of the standard error of the estimated air pollution effect, even when using an asymptotically unbiased standard error estimator. We also show that both the magnitude and direction of the bias in the air pollution effect depend, at least in part, on the nature of the concurvity. We argue that including a nonparametric function of location in a GAM for spatial epidemiologic data can be expected to result in concurvity. As a result, we recommend caution in using the GAM to analyze this type of data.

Air Pollution↗