Environmentalism, pre-environmentalism, and public policy

In the last decade, thousands of new grassroots groups have formed to oppose environmental pollution on the basis that it endangers their health. These groups have revitalized the environmental movement and enlarged its membership well beyond the middle class. Scientists, however, have been unable to corroborate these groups' claims that exposure to pollutants has caused their diseases. For policy analysts this situation appears to pose a choice between democracy and science. It needn't. Instead of evaluating the grassroots groups from the perspective of science, it is possible to evaluate science from the perspective of environmentalism. This paper argues that environmental epidemiology reflects ‘pre-environmentalist’ assumptions about nature and that new ideas about nature advanced by the environmental movement could change the way scientists collect and interpret data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45449/1/11077_2005_Article_BF01006494.pd

Estimation of a failure time distribution based on imperfect diagnostic tests

Sequentially-administered diagnostic tests used to determine the occurrence of a silent event are sometimes subject to error, leading to false positive and false negative test results. In such cases, standard methods for interval censored data do not give valid estimates of the distribution of the time to the event.We present methods for estimating the distribution of the time to the event that account for multiple types of imperfect diagnostic test, as well as differing periods at risk. We illustrate the methods with simulated data and results from a clinical trial for the prevention of mother-to-infant transmission of HIV in Tanzania

Estimating HIV Incidence Based on Combined Prevalence Testing

Knowledge of incidence rates of HIV and other infectious diseases is important in evaluating the state of an epidemic as well as for designing interventional studies. Estimation of disease incidence from longitudinal studies can be expensive and time consuming. Alternatively, Janssen et al. (1998, Journal of the American Medical Association 280, 42–48) proposed the estimation of HIV incidence at a single point in time based on the combined use of a standard and “detuned” antibody assay. This article frames the problem from a longitudinal perspective, from which the maximum likelihood estimator of incidence is determined and compared with the Janssen estimator. The formulation also allows estimation for general situations, including different batteries of tests among subjects, inclusion of covariates, and a comparative evaluation of different test batteries to help guide study design. The methods are illustrated with data from an HIV interventional trial and a seroprevalence survey recently conducted in Botswana

Estimation of timing of mother-infant transmission of HIV

Knowledge of the timing of perinatal transmission of HIV would be valuable for the determination and evaluation of preventive treatments and would shed light on the mechanism of transmission. Estimation of the distribution of the time of perinatal transmission is difficult, however, because tests of infection status can only be undertaken after birth. DNA and RNA polymerase chain reaction (PCR) assays and HIV culture have been the most commonly used diagnostic tests for perinatal HIV infection. Such tests have high sensitivity and specificity, except when they are given shortly after infection. In this paper we use the time-dependent sensitivity of these diagnostic tests to make nonparametric and semiparametric inferences about the distribution of the time of perinatal HIV transmission as well as the cumulative probability of perinatal transmission. The methods are illustrated with data from a clinical trial conducted by the AIDS Clinical Trials group

Analyzing Time-to-Event Data in a Clinical Trial When an Unknown Proportion of Subjects Has Experienced the Event at Entry

In some clinical trials, where the outcome is the time until development of a silent event, an unknown proportion of subjects who have already experienced the event will be unknowingly enrolled due to the imperfect nature of the diagnostic tests used to screen potential subjects.F or example, commonly used diagnostic tests for evaluating HIV infection status in infants, such as DNA PCR and HIV Culture, have low sensitivity when given soon after infection.This can lead to the inclusion of an unknown proportion of HIV-infected infants into clinical trials aimed at the prevention of transmission from HIV-positive mothers to their infants through breastfeeding.The infection status of infants at the end of the trial, when they are more than a year of age, can be determined with certainty. For those infants found to be infected with HIV at the end of the trial, it cannot be determined whether this occurred during the study or whether they were already infected when they were enrolled.In these settings, estimates of the cumulative risk of the event by the end of the study will overestimate the true probability of event during the study period and hypothesis tests comparing two or more intervention strategies can also be biased.W e present inference methods for the distribution of time until the event of interest in these settings, and investigate issues in the design of such trials when there is a choice of using both imperfect and perfect diagnostic tests

On the Relationship between Directional and Omnibus Statistical Tests

A common statistical problem involves the testing of a "K"-dimensional parameter vector. In both parametric and semiparametric settings, two types of directional tests - linear combination and constrained tests - are frequently used instead of omnibus tests in hopes of achieving greater power for specific alternatives. In this paper, we consider the relationship between these directional tests, as well as their relationship to omnibus tests. Every constrained directional test is shown to be asymptotically equivalent to a specific linear combination test under a sequence of contiguous alternatives and vice versa. Even when the direction of the alternative is known, the constrained test in general will not be optimal unless the objective function used to derive it is efficient. For an arbitrary alternative, insight into the power characteristics of directional tests in comparison to omnibus tests can be gained by a chi-square partition of the omnibus test. Copyright 2006 Board of the Foundation of the Scandinavian Journal of Statistics..

Statistical considerations when using a composite endpoint for comparing treatment groups

When comparing two treatment groups in a time-to-event analysis, it is common to use a composite event consisting of two or more distinct outcomes. The goal of this paper is to develop a statistical methodology to derive efficiency guidelines for deciding whether to expand a study primary endpoint from E1 (for example, non-fatal myocardial infarction and cardiovascular death) to the composite of E1 and E2 (for example, non-fatal myocardial infarction, cardiovascular death or revascularisation). We investigate this problem by considering the asymptotic relative efficiency of a log-rank test for comparing treatment groups with respect to a primary relevant endpoint E1 versus the composite primary endpoint, say E , of E1 and E2, where E2 is some additional endpointPeer Reviewe