Comment: Citation Statistics

Comment on "Citation Statistics" [arXiv:0910.3529]Comment: Published in at http://dx.doi.org/10.1214/09-STS285C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

How old are you, really? Communicating chronic risk through 'effective age' of your body and organs.

In communicating chronic risks, there is increasing use of a metaphor that can be termed 'effective-age': the age of a 'healthy' person who has the same risk profile as the individual in question. Popular measures include 'real-age', 'heart-age', 'lung-age' and so on.Here we formally define this concept, and illustrate its use in a variety of areas. We explore conditions under which the years lost or gained that are associated with exposure to risk factors depends neither on current chronological age, nor the period over which the risk is defined. These conditions generally hold for all-cause adult mortality, which enables a simple and vivid translation from hazard-ratios to years lost or gained off chronological age. Finally we consider the attractiveness and impact of this concept.Under reasonable assumptions, the risks associated with specific behaviours can be expressed in terms of years gained or lost off your effective age. The idea of effective age appears a useful and attractive metaphor to vividly communicate risks to individuals.David and Claudia Harding Foundatio

Simple and Multistate Survival Curves

Objective and Sample: This investigation assessed the comprehension of survival curves in a community sample of 88 young and middle-aged adults when several aspects of good practice for graphical communication were implemented, and it compared comprehension for alternative presentation formats. Design, Method, and Measurements: After reading worked examples of using survival curves that provided explanation and answers, participants answered questions on survival data for pairs of treatments. Study 1 compared presenting survival curves for both treatments on the same figure against presentation via 2 separate figures. Study 2 compared presenting data for 3 possible outcome states via a single “multistate” figure for each treatment against presenting each outcome on a separate figure (with both treatments on the same figure). Both studies compared alternative forms of questioning (e.g., “number alive” versus “number dead”). Numeracy levels (self-rated and objective measures) were also assessed. Results: Comprehension was generally good—exceeding 90% correct answers on half the questions—and was similar across alternative graphical formats. Lower accuracy was observed for questions requiring a calculation but was significantly lower only when the requirement for calculation was not explicit (13%–28% decrements in performance). In study 1, this effect was most acute for those with lower levels of numeracy. Subjective (self-rated) numeracy and objective (measured) numeracy were both moderate positive predictors of overall task accuracy (r ≈ 0.3). Conclusions: A high degree of accuracy in extracting information from survival curves is possible, as long as any calculations that are required are made explicit (e.g., finding differences between 2 survival rates). Therefore, practitioners need not avoid using survival curves in discussions with patients, although clear and explicit explanations are important </jats:p

Communicating statistics through the media in the time of Covid-19

Professor Kevin McConway and Professor Sir David Spiegelhalter discuss their experiences of communicating statistical research to the media and offer 12 tips for researchers to effectively engage with the media. The coronavirus pandemic has brought an unprecedented demand from the media for statistical commentary. Whereas a trip to a studio for a radio or TV interview was ... Continue

What Can Education Learn from Real-World Communication of Risk and Uncertainty?

Probability is a difficult topic to teach, not least because it is rather unclear what it actually means. Modern risk communication has tackled general public incomprehension of probability statements by using the metaphor of ‘expected frequencies’ – for example, “of 100 people like you, we would expect 10 to have a heart attack or stroke in the next 10 years.” We show how these ideas can be taken into the classroom as the basis for teaching probability, using frequency tree diagrams as the fundamental representation. Empirical frequency trees can be used to summarise a series of classroom experiments, and then expected frequency trees naturally provide a basis for deriving the rules of probability, and make complex conditional probability calculations reasonably straightforward

COVID in statistics: numbers do not speak for themselves

David Spiegelhalter (University of Cambridge) and Anthony Masters (Royal Statistical Society) highlight some of the key findings from their book, COVID by Numbers: Making Sense of the Pandemic with Data

How well did experts and laypeople forecast the size of the COVID-19 pandemic?

Throughout the COVID-19 pandemic, social and traditional media have disseminated predictions from experts and nonexperts about its expected magnitude. How accurate were the predictions of 'experts'-individuals holding occupations or roles in subject-relevant fields, such as epidemiologists and statisticians-compared with those of the public? We conducted a survey in April 2020 of 140 UK experts and 2,086 UK laypersons; all were asked to make four quantitative predictions about the impact of COVID-19 by 31 Dec 2020. In addition to soliciting point estimates, we asked participants for lower and higher bounds of a range that they felt had a 75% chance of containing the true answer. Experts exhibited greater accuracy and calibration than laypersons, even when restricting the comparison to a subset of laypersons who scored in the top quartile on a numeracy test. Even so, experts substantially underestimated the ultimate extent of the pandemic, and the mean number of predictions for which the expert intervals contained the actual outcome was only 1.8 (out of 4), suggesting that experts should consider broadening the range of scenarios they consider plausible. Predictions of the public were even more inaccurate and poorly calibrated, suggesting that an important role remains for expert predictions as long as experts acknowledge their uncertainty

How well did experts and laypeople forecast the size of the COVID-19 pandemic?

Throughout the COVID-19 pandemic, social and traditional media have disseminated predictions from experts and nonexperts about its expected magnitude. How accurate were the predictions of 'experts'-individuals holding occupations or roles in subject-relevant fields, such as epidemiologists and statisticians-compared with those of the public? We conducted a survey in April 2020 of 140 UK experts and 2,086 UK laypersons; all were asked to make four quantitative predictions about the impact of COVID-19 by 31 Dec 2020. In addition to soliciting point estimates, we asked participants for lower and higher bounds of a range that they felt had a 75% chance of containing the true answer. Experts exhibited greater accuracy and calibration than laypersons, even when restricting the comparison to a subset of laypersons who scored in the top quartile on a numeracy test. Even so, experts substantially underestimated the ultimate extent of the pandemic, and the mean number of predictions for which the expert intervals contained the actual outcome was only 1.8 (out of 4), suggesting that experts should consider broadening the range of scenarios they consider plausible. Predictions of the public were even more inaccurate and poorly calibrated, suggesting that an important role remains for expert predictions as long as experts acknowledge their uncertainty

Impact of plain packaging of tobacco products on smoking in adults and children: an elicitation of international experts' estimates.

BACKGROUND: Governments sometimes face important decisions in the absence of direct evidence. In these cases, expert elicitation methods can be used to quantify uncertainty. We report the results of an expert elicitation study regarding the likely impact on smoking rates in adults and children of plain packaging of tobacco products. METHODS: Thirty-three tobacco control experts were recruited from the UK (n = 14), Australasia (n = 12) and North America (n = 7). Experts' estimates were individually elicited via telephone interviews, and then linearly pooled. Elicited estimates consisted of (1) the most likely, (2) the highest possible, and (3) the lowest possible value for the percentage of (a) adult smokers and (b) children trying smoking, two years after the introduction of plain packaging (all other things being constant) in a target country in the expert's region of residence. RESULTS: The median estimate for the impact on adult smoking prevalence was a 1 percentage point decline (99% range 2.25 to 0), and for the percentage of children trying smoking was a 3 percentage point decline (99% range 6.1 to 0), the latter estimated impact being larger than the former (P < 0.001, sign test). There were no differences in either estimate by region (I2: Adults: 0; Children: 0) but there was considerable variability between experts' estimates within regions (I2: Adults: 0.91; Children: 0.89). CONCLUSIONS: In the absence of direct evidence for the impact of introducing plain packaging on smoking rates in adults and children, this study shows that tobacco control experts felt the most likely outcomes would be a reduction in smoking prevalence in adults, and a greater reduction in the numbers of children trying smoking, although there was substantial variability in the estimated size of these impacts. No experts judged an increase in smoking as a likely outcome.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are