Evaluation of coronary angiographic projections to balance the clinical yield with the radiation risk.

OBJECTIVE: Radiation safety principles dictate that imaging procedures should minimise the radiation risks involved, without compromising diagnostic performance. This study aims to define a core set of views that maximises clinical information yield for minimum radiation risk. Angiographers would supplement these views as clinically indicated. METHODS: An algorithm was developed to combine published data detailing the quality of information derived for the major coronary artery segments through the use of a common set of views in angiography with data relating to the dose-area product and scatter radiation associated with these views. RESULTS: The optimum view set for the left coronary system comprised four views: left anterior oblique (LAO) with cranial (Cr) tilt, shallow right anterior oblique (AP-RAO) with caudal (Ca) tilt, RAO with Ca tilt and AP-RAO with Cr tilt. For the right coronary system three views were identified: LAO with Cr tilt, RAO and AP-RAO with Cr tilt. An alternative left coronary view set including a left lateral achieved minimally superior efficiency (<5%), but with an ~8% higher radiation dose to the patient and 40% higher cardiologist dose. CONCLUSION: This algorithm identifies a core set of angiographic views that optimises the information yield and minimises radiation risk. This basic data set would be supplemented by additional clinically determined views selected by the angiographer for each case. The decision to use additional views for diagnostic angiography and interventions would be assisted by referencing a table of relative radiation doses for the views being considered.

Localization of a quantitative trait locus via a Bayesian approach.

A Bayesian approach to the direct mapping of a quantitative trait locus (QTL), fully utilizing information from multiple linked gene markers, is presented in this paper. The joint posterior distribution (a mixture distribution modeling the linkage between a biallelic QTL and N gene markers) is computationally challenging and invites exploration via Markov chain Monte Carlo methods. The parameter's complete marginal posterior densities are obtained, allowing a diverse range of inferences. Parameters estimated include the QTL genotype probabilities for the sires and the offspring, the allele frequencies for the QTL, and the position and additive and dominance effects of the QTL. The methodology is applied through simulation to a half-sib design to form an outbred pedigree structure where there is an entire class of missing information. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

Environmental tobacco smoke and ischaemic heart disease: a case study in applying causal criteria.

BACKGROUND: Whether ischaemic heart disease (IHD) is caused by exposure to environmental tobacco smoke (ETS), commonly known as "passive smoking", has been debated from both epidemiological and biological perspectives. METHODS AND RESULTS: In this paper we use Bradford Hill criteria to synthesize results from the biological and epidemiological literature in a formal assessment of the strength of support for such a relationship. Although we find that these criteria, designed for clinical trials, do not give an ideal framework for assessment of epidemiological and biological studies, nevertheless they do provide systematic guidance for this assessment. For the general population, of the nine tests proposed by Hill we find that one (biological plausibility) seems to be supported, though not unarguably; three (strength, consistency. specificity) appear to fail by accepted standards; and the remaining five have insufficient data for a clear evaluation (biological gradient, experimental evidence, temporality, coherence, analogy). Overall, this provides at best weak support for a causal association between ETS and IHD across the general community. Conversely, there appears to be more support, especially in the biology studies, for an association between ETS and IHD for those with preexisting disease, although epidemiological studies are limited in this area. CONCLUSIONS: One of the outcomes of this review is the identification of areas of focus for future epidemiological and biological research. First, we find that stronger associations may be found in the particular subpopulation with pre-existing IHD. In this case, more convincing biological plausibility and experimental evidence indicate a need for relevant epidemiological studies, although individual responses are very variable. Second, we identify the need for further, more detailed evaluations of the nature of vessel wall thickenings occurring in experimental models of ETS exposure. Third, we propose long-term animal studies of initiation of IHD, including direct assessment of effects on the accumulation of lipid in vessel walls, at appropriate ETS exposure levels.

A Bayesian approach to ordering gene markers.

A technique is presented whereby a marker map can be constructed using resource family data with an entire class of missing data. The focus is on a half-sib design where there is only information on a single parent and its progeny. A Bayesian approach is utilised with solutions obtained via a Markov chain Monte Carlo algorithm. Features of the approach include the capacity to determine parameters for the ungenotyped dam population, the ability to incorporate published information and its reliability, and the production of posterior densities and the consequent deduction of a wide range of inferences. These features are demonstrated through the analysis of simulated and experimental data.

Bayesian meta-analysis, with application to studies of ETS and lung cancer.

Meta-analysis enables researchers to combine the results of several studies to assess the information they provide as a whole. It has been used to give a systematic overview of many areas in which data on a possible association between an exposure and an outcome have been collected in a number of studies but where the overall picture remains obscure, both as to the existence or size of the effect. This paper outlines some innovations in meta-analysis, based on using Markov chain Monte Carlo (MCMC) techniques for implementing Bayesian hierarchical models, and compares these with a more well-known random effects (RE) model. The new techniques allow different aspects of variation to be incorporated into descriptions of the association, and in particular enable researchers to better quantify differences between studies. Both the classical and Bayesian methods are applied, in this paper, to the current collection of studies of the association between incidence of lung cancer in female never-smokers and exposure to environmental tobacco smoke (ETS), both in the home through spousal smoking and in the workplace. In this paper it is demonstrated that compared with the RE model, the Bayesian methods: (a) allow more detailed modeling of study heterogeneity to be incorporated; (b) are relatively robust against a wide choice of specifications of such information on heterogeneity; (c) allow for more detailed and satisfactory statements to be made, not only about the overall risk but about the individual studies, on the basis of the combined information. For the workplace exposure data set, the Bayesian methods give a somewhat lower overall estimate of relative risk of lung cancer associated with ETS, indicating the care that needs to be taken in using point estimates based on any one method of analysis. On the larger spousal data set the methods give similar answers. Some of the other concerns with meta-analysis are also considered. These include: consistency between different geographic areas (Asia and the United States), and our studies show that Bayesian methods permit an account of the overall picture to be taken, thus improving the ability to estimate accurately in the subgroups; and publication bias which, as shown with the spousal exposure data, may lead to an inflated excess risk.

Meta-analytic approaches to dose-response relationships, with application in studies of lung cancer and exposure to environmental tobacco smoke.

This paper outlines several meta-analytic approaches to the assessment of quantal dose-response relationships; that is, to the evaluation of an increase in the level of exposure to an agent and the associated relative risk of a disease when this is investigated over a number of different studies. Analysis is developed at two levels: first, a consistent method of evaluating the dose-response relationship is applied to each study, and second, an overall picture is obtained by comparing and combining these relationships. At the first stage, for an individual study, dose-response assessment involves choices of model and appropriate tests for trend, which are influenced by such issues as dose measurement and use of the unexposed group. At the second stage, different methods for pooling results across studies must be considered. These depend on the choices made in the first stage of analysis, with additional attention paid to heterogeneity, and possible bias due to studies included in meta-analysis. We describe these meta-analytic approaches for three methods of evaluating dose response. The approaches are illustrated by evaluating the relationship between lung cancer and levels of exposure to environmental tobacco smoke (ETS). The strength of this relationship has been a point of debate in recent assessment of evidence for an overall carcinogenic effect of ETS exposure. We find little indication of a consistent dose response, a result explained in terms of recent models for cancer and passive smoking developed by Darby and Pike, the current meta-analysis results of overall risk-ratios of current studies in Tweedie and Mengersen, and misclassification models developed by the United States Environmental Protection Agency (EPA).

Passive smoking in the workplace: classical and Bayesian meta-analyses.

There are currently several classical and Bayesian methods of meta-analysis available for combining epidemiological results. We describe and compare these in a consistent framework, and apply them to published studies of the relative risk of lung cancer associated with exposure to environmental tobacco smoke in the workplace. We find that although all methods give reasonably similar combined estimates of relative risk of lung cancer associated with this exposure (none of which is significantly raised above unity, in either a frequentist or a Bayesian sense), the approximations arising from classical methods appear to be nonconservative and should be used with caution. The Bayesian methods, which account more explicitly for possible inhomogeneity in studies, give slightly lower estimates again of relative risk and wider posterior credible intervals, indicating that inference from the non-Bayesian approaches might be optimistic.

Lung cancer and passive smoking: reconciling the biochemical and epidemiological approaches.

The accurate determination of exposure to environmental tobacco smoke is notoriously difficult. There have been to date two approaches to determining this exposure in the study of association of passive smoking and lung cancer: the biochemical approach, using cotinine in the main as a marker, and the epidemiological approach. Typically results of the former have yielded much lower relative risk than the latter, and have tended to be ignored in favour of the latter, although there has been considerable debate as to the logical basis for this. We settle this question by showing that, using the epidemiologically based meta-analysis technique of Wald et al. (1986), and misclassification models in the EPA Draft Review (1990), one arrives using all current studies at a result which is virtually identical with the biochemically-based conclusions of Darby and Pike (1988) or Repace and Lowry (1990). The conduct of this meta-analysis itself raises a number of important methodological questions, including the validity of inclusion of studies, the use of estimates adjusted for covariates, and the statistical significance of estimates based on meta-analysis of the epidemiological data. The best estimate of relative risk from spousal smoking is shown to be approximately 1.05-1.10, based on either of these approaches; but it is suggested that considerable extra work is needed to establish whether this is significantly raised.