Changes in Risk Perceptions Prospectively Predict Changes in Self‐Reported Speeding

Risk perception theories posit that changes in risk perception prompt subsequent changes in risk behavior. Prospective studies using observations made at three time‐points offer the capacity to test this hypothesis by observing sequential changes in both risk perceptions and behavior. A telephone survey was administered by random‐digit dialing to 255 adult Australian drivers at baseline (T1), 6 weeks (T2), and 14 weeks (T3). During weeks 2–5, a risk‐perception‐based anti‐speeding mass media campaign was conducted. The survey assessed risk perception, operationalized as the proportion of time that driving at 70 km/h (43 mph) was perceived to be dangerous, and self‐reported speeding behavior, defined as the frequency of respondents driving 5 km (3 mph) faster than the legal speed limit in built up areas. Higher T2 risk perception predicted lower T3 self‐reports of speeding after controlling T1 risk perception and T1 and T2 self‐reported speeding. This can be interpreted as changes in risk perceptions between T1 and T2 predicting changes in speeding between T2 and T3. Further analyses showed that increases in risk perception predicted lower subsequent self‐reported speeding changes, but decreases in risk perception were unrelated to those changes. Risk perception changes were unrelated to recall of exposure to the media campaign. These findings support a dynamic view of the relationship between risk perception and self‐reported behavior, and that risk perception theories can be applied to speeding.     

Proposed best practice for statisticians in the reporting and publication of pharmaceutical industry‐sponsored clinical trials

In this paper we set out what we consider to be a set of best practices for statisticians in the reporting of pharmaceutical industry‐sponsored clinical trials. We make eight recommendations covering: author responsibilities and recognition; publication timing; conflicts of interest; freedom to act; full author access to data; trial registration and independent review. These recommendations are made in the context of the prominent role played by statisticians in the design, conduct, analysis and reporting of pharmaceutical sponsored trials and the perception of the reporting of these trials in the wider community. Copyright © 2010 John Wiley & Sons, Ltd.     

The Prospects for Digital Radio

Digital audio broadcasting is a major innovation in radio, one that is at its most advanced in Europe. It has the potential to deliver high-quality audio reception and to increase significantly the capacity of the radio spectrum, with the possibility of an expansion of both the range and diversity of radio programming. Nevertheless, in the UK and elsewhere it remains relatively unknown and under-adopted in comparison with other consumer technologies like digital television. This article examines the origins of digital radio, and considers how this technology is expected to become a mass communications technology, eventually supplanting analogue radio. However, in its present form, there is little that is novel currently being offered on digital radio, and the economic and political contexts in which it is being developed may encourage further concentration of ownership and reduce diversity of choice in listening. Unlike previous innovations then, such as FM broadcasting, there appear to be few compelling advantages of digital radio that will persuade listeners to adopt this new technology. If this new technological system is to succeed, alternative uses must be found for it, and one area for which it might be suited is mobile data communications. The article concludes by suggesting that this might mean that radio becomes of secondary importance to this potentially lucrative application of digital audio broadcasting technology.     

Markov Chain Monte Carlo in small worlds

As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get trapped within one mode of a multi-modal space. Moreover, it may be difficult to determine if a chain is only sampling a certain area of the space or if it has indeed reached stationarity. In this paper, we show how a simple modification of the proposal mechanism results in faster convergence of the chain and helps to circumvent the problems described above. This mechanism, which is based on an idea from the field of “small-world” networks, amounts to adding occasional “wild” proposals to any local proposal scheme. We demonstrate through both theory and extensive simulations, that these new proposal distributions can greatly outperform the traditional local proposals when it comes to exploring complex heterogenous spaces and multi-modal distributions. Our method can easily be applied to most, if not all, problems involving MCMC and unlike many other remedies which improve the performance of MCMC it preserves the simplicity of the underlying algorithm.     

Sharp tongues and bitter pills

How bad is big pharma? An American medical journal has taken draconian measures to vet analyses of clinical trials sponsored by the pharmaceutical industry. Are their statistics so bad? Stephen Senn has a look at the controversy.     

Comparing Estimates of the Effects of Air Pollution on Human Mortality Obtained Using Different Regression Methodologies

Studies using regression techniques report their results using a variety of statistics. Evaluation of the consistency of findings, such as in a metaanalysis, requires calculating the statistical estimates of the effect reported in each study in a comparable manner. In this paper, we consider multiple linear regression, multiple Poisson regression, and logistic regression estimates. We present results that are needed to calculate, on a common basis, the slope of the regression function at a specified value, the elasticity function of the regression function at a specified value, the relative risk at a specified value, and the odds ratio at a specified value. We apply these results to studies of the association of daily mortality in an area to the daily air pollution level of ozone and PM₁₀. We calculate the estimated slope of the number of deaths per billion population associated with an increase of 1 ppb of ozone level in studies of daily mortality in three urban areas. These studies, in Los Angeles, New York, and St. Louis, produced very comparable results on a common basis, especially when compared to the coefficients as reported. We also calculated the estimated elasticity function of the daily mortality and daily PM₁₀ level for eight areas and found that the elasticities varied within a factor of roughly two, much less than the variability in the coefficients as reported.     

Development and Consideration of Global Policies for Managing the Future Risks of Poliovirus Outbreaks: Insights and Lessons Learned Through Modeling

The success of the Global Polio Eradication Initiative promises to bring large benefits, including sustained improvements in quality of life (i.e., cases of paralytic disease and deaths avoided) and costs saved from cessation of vaccination. Obtaining and maintaining these benefits requires that policymakers manage the transition from the current massive use of oral poliovirus vaccine (OPV) to a world without OPV and free of the risks of potential future reintroductions of live polioviruses. This article describes the analytical journey that began in 2001 with a retrospective case study on polio risk management and led to development of dynamic integrated risk, economic, and decision analysis tools to inform global policies for managing the risks of polio. This analytical journey has provided several key insights and lessons learned that will be useful to future analysts involved in similar complex decision-making processes.