Yesterday in my "rant" about the BBC (and other media) coverage of the recent correlational alcohol-cancer study (below), I mentioned Gerd Gigerenzer's work on how commonly-used conventions about the reporting of medical statistics misleads many people (including doctors) about the real risks involved. (Indeed, there is evidence that some pharmaceutical companies intentionally manipulate the format of statistics to maximize the appearance of benefit and minimize the appearance of risk.)
For anyone interested, I have a pdf of Gigerenzer's latest and most detailed publication in this vein: "Helping Doctors and Patients Make Sense of Health Statistics" (/Psychological Science in the Public Interest/, 2008). I've attached the abstract of the article below. It is longish (44 pp.) but it is so good that I have been thinking about basing an entire course around it. The widespread misunderstanding of cancer and AIDS rates (and the tests that are supposed to detect them) are used as examples throughout. Although I normally teach the standard statistics course in my department (t, r, F, etc.), a course based on this information would be of much greater benefit to much wider range of students. Because the file is 1.8Mb, I don't want to clog up the entire list with it, but I would be happy to forward a copy to anyone who asks me (off list, please). Regards, Chris -- Christopher D. Green Department of Psychology York University Toronto, ON M3J 1P3 Canada 416-736-2100 ex. 66164 [email protected] http://www.yorku.ca/christo/ ========================== SUMMARY Many doctors, patients, journalists, and politicians alike do not understand what health statistics mean or draw wrong conclusions without noticing. Collective statistical illiteracy refers to the widespread inability to understand the meaning of numbers. For instance, many citizens are unaware that higher survival rates with cancer screening do not imply longer life, or that the statement that mammography screening reduces the risk of dying from breast cancer by 25% in fact means that 1 less woman out of 1,000 will die of the disease. We provide evidence that statistical illiteracy (a) is common to patients, journalists, and physicians; (b) is created by nontransparent framing of information that is sometimes an unintentional result of lack of understanding but can also be a result of intentional efforts to manipulate or persuade people; and (c) can have serious consequences for health. The causes of statistical illiteracy should not be attributed to cognitive biases alone, but to the emotional nature of the doctor--patient relationship and conflicts of interest in the healthcare system. The classic doctor--patient relation is based on (the physician's) paternalism and (the patient's) trust in authority, which make statistical literacy seem unnecessary; so does the traditional combination of determinism (physicians who seek causes, not chances) and the illusion of certainty (patients who seek certainty when there is none). We show that information pamphlets, Web sites, leaflets distributed to doctors by the pharmaceutical industry, and even medical journals often report evidence in nontransparent forms that suggest big benefits of featured interventions and small harms. Without understanding the numbers involved, the public is susceptible to political and commercial manipulation of their anxieties and hopes, which undermines the goals of informed consent and shared decision making. What can be done? We discuss the importance of teaching statistical thinking and transparent representations in primary and secondary education as well as in medical school. Yet this requires familiarizing children early on with the concept of probability and teaching statistical literacy as the art of solving real-world problems rather than applying formulas to toy problems about coins and dice. A major precondition for statistical literacy is transparent risk communication. We recommend using frequency statements instead of single-event probabilities, absolute risks instead of relative risks, mortality rates instead of survival rates, and natural frequencies instead of conditional probabilities. Psychological research on transparent visual and numerical forms of risk communication, as well as training of physicians in their use, is called for. Statistical literacy is a necessary precondition for an educated citizenship in a technological democracy. Understanding risks and asking critical questions can also shape the emotional climate in a society so that hopes and anxieties are no longer as easily manipulated from outside and citizens can develop a better-informed and more relaxed attitude toward their health. -------- Original Message -------- >>> On 2/25/2009 at 11:57 AM, Christopher Green <[email protected]> wrote: > Amadio, Dean wrote: >> How else is one to study this issue? Much of health research is precisely > the same, due to obvious ethical concerns, including the research on alcohol > and heart disease. > Which is precisely why medical research is, in general, so lousy and its > (often overheated) conclusions keep changing from study to study, > resulting ultimately in lowered public respect for science at large. > > What one should do is draw conclusions that are appropriate to the > evidence they are drawn from. If those conclusions are too bland to be > interesting, that doesn't justify falsely strengthening them to make > them more interesting. And that seems to be what has happened here. > (Note that "causes" is feature[d] in the very first sentence of the article.) >> This is also old news (there are previous studies which find the same > relationship among women). I've been advising my students for several years, > especially females, to consider the cancer studies whenever they hear the > research that alcohol is heart healthy. > If those cancer studies are like this one, then I would recommend that > they ignore them, for they can tell [the students] nothing about the effect > on > their health of drinking moderate amounts of alcohol. They only tell us > that the global genetics and lifestyles of people who choose to abstain > from alcohol altogether do not result in cancer quite as much as those > of people who do not adhere to such a prohibition. > > Also, they use global percentages in their presentation of risk, which > almost inevitably misleads people about the actual increase in risk of > low base-rate conditions like cancer. (See, e.g., the recent writings of > Gerd Gigerenzer). For instance, the article says that 5,000 of the > 45,000 annual cases of breast cancer are due to alcohol -- an increase > of 11% they say. The population of the UK is about 60 million. Half of > the those are female -- 30 million. About 20% of those are children -- > leaving 24 million. (see > http://www.statistics.gov.uk/cci/nugget.asp?ID=6). 45,000 out of 24 > million = .0019: 19 in ten thousand women are diagnosed with breast > cancer in any given year. Even if the alcohol-cancer causal link were, > in fact true, the number of cancer cases would drop to 40,000 which, > against a vulnerable population of 24 million is .0017: 17 in ten > thousand. Now ask yourself the question: Would you change you lifestyle > dramatically to reduce a risk by 2 in 10,000? And that's if the causal > link had been established, which it hasn't been. > > Regards, > Chris --- To make changes to your subscription contact: Bill Southerly ([email protected])
