A UK-based solicitor’s office is urgently seeking a statistician to help in a “shaken baby” case. Each of the parties involved have had medical reports conducted, however the results differ. A statistician is needed to write a report on the limitations of the different sets of statistical approaches. If you are interested in this opportunity, email firstname.lastname@example.org for more information.
Steve Lohr, writer for the NY Times, seems to think so. He says that statisticians “are finding themselves increasingly in demand — and even cool”. Read his article based around Carrie Grimes – a statistician for Google.
Carrie Grimes conducts statistical analysis to research ways to improve the search engine. The article focuses on how and why she became a statistician, and why statistics seems to be increasingly popular.
Need to report the results of a clinical trial?
Unsure what your results are actually telling you?
Read Pocock and Ware’s short comment for guidance.
Translating statistical findings into plain English – published in the Lancet, June 2009 (subscription content).
Pocock and Ware use real trial results to illustrate the “dos and don’ts” of trial reporting. It does not tell you exactly what to write – that depends on your particular trial – but it will point you in the right direction.
The London School of Hygiene and Tropical Medicine (LSHTM) MSc Medical Statistics web page sums up the skills of a medical statistician:
- select appropriate study designs to address questions of medical relevance
- select and apply appropriate statistical techniques for managing common types of medical data
- use various software packages for statistical analysis and data management
- interpret the results of statistical analyses and critically evaluate the use of statistics in the medical literature
- communicate effectively with statisticians and the wider medical community, in writing and orally through presentation of results of statistical analyses
- explore current and anticipated developments in medical statistics
Medical statistics is the application of statistical knowledge and methods to the field of medicine and medical practice.
Although medical statistics has been a recognised branch of statistics in the UK for more than 40 years, the term does not appear to have come into general use in North America, where the wider term ‘biostatistics’ is used and encompasses the application of statistics (the branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters) to medical-related data as well as those in the wider field of biology.
My preferred definition of medical statistics is the one I have coined above. The current (June 2009) definition of medical statistics in Wikipedia is, like many things in Wikipedia, an unsatisfactory work in progress. It says that medical statistics is “the field of medicine dealing with applications of statistics to the field of health and medicine.”
An entry in Answers.com throws some interesting light on the history of what we would now call medical statistics, quoting sources that trace its roots to the eighteenth century:
“One tradition [which flowed from Graunt’s and Petty’s early work] was medical statistics, which developed most fully in England during the eighteenth century. Physicians such as James Jurin (1684–1750) and William Black (1749–1829) advocated the collection and evaluation of numerical information about the incidence and mortality of diseases. Jurin pioneered the use of statistics in the 1720s to evaluate medical practice in his studies of the risks associated with smallpox inoculation. William Black coined the term medical arithmetic to refer to the tradition of using numbers to analyze the comparative mortality of different diseases. New hospitals and dispensaries such as the London Smallpox and Inoculation Hospital, established in the eighteenth century, provided institutional support for the collection of medical statistics; some treatments were evaluated numerically.”
A search for the term ‘biostatistics’ or ‘biometrics’ returns many definitions, of which the following are a selection:
“The theory and techniques for describing, analyzing, and interpreting health data.” Johns Hopkins Bloomberg School of Public Health.
“The use of statistical tests to analyze biological data” Duke Clinical Research Institute.
“The science of statistics applied to the analysis of biological or medical data.” The American Heritage Medical Dictionary (2004) Published by Houghton Mifflin Company.
“Numeric data on births, deaths, diseases, injuries, and other factors affecting the general health and condition of human populations. Also called vital statistics” Mosby’s Medical Dictionary, 8th edition. 2009, Elsevier
“Biostatistics (a combination of the words biology and statistics; sometimes referred to as biometry or biometrics) is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine and agriculture; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results.” Wikipedia.
“A branch of biology that studies biological phenomena and observations by means of statistical analysis.” WordNet.
“The science of collecting and analyzing biologic or health data using statistical methods. Biostatistics may be used to help learn the possible causes of a cancer or how often a cancer occurs in a certain group of people. Also called biometrics and biometry.” National Cancer Institute.
Everyone should have a quick play around with Spinning the Risk as it is an excellent example of how presenting risk in different ways can make things seem completely harmless or entirely life-threatening.
On the excellent website Understanding Uncertainty, David Spiegelhalter and his team have created a wonderful tool. Spinning the Risk allows anyone to look at the different ways that health risks can be presented. At the moment it is just for the risk from bacon sandwiches and statins, but may be expanded.
Further explanation on the Understanding Uncertainty website…
Spiegelhalter spoke passionately about Spinning the Risk at the ‘Statistical Methods and Medical Research: New Challenges for an Old Marriage’ conference at the London School of Hygiene and Tropical Medicine. The two-day conference was to celebrate the 40th anniversary of the MSc in Medical Statistics and it was a fantastic success. Amongst the wonderful presentations and intelligent discussions were, thankfully, a multitude of nerdy statistical jokes to lighten the mood. Unsurprisingly though, Spiegelhalter’s talk was one of the highlights and everyone should try to attend any of his future presentations.
A short article in the March 2009 issue of the statistical magazine Significance pointed out a fantastic piece of research done into how not to be eaten by a puma. The research by R. G. Coss and others found that people who did not run away from pumas had the greatest frequency of being severely injured (43%) and the lowest likelihood of escaping injury (26%). In other words, if a puma is approaching you… run!
It is natural to wonder why this type of research is conducted. What benefit or insight does it provide? There is actually more to the paper, for example they looked into the effect of age and the number of people in the group on survival, so it probably is valuable research. However I would like to use it to illustrate a point I have been making for years.
Each day in the newspapers and TV news items and in every day discussions that people have, there are stories of the latest methods for preventing a disease or condition. For example recently there was an article on the benefit of taking vitamin D supplements to prevent bone fractures. Whilst it is always great to research the prevention or treatment of disease, are people supposed to change their habits in accordance with the latest research? There is always a lot of conflicting advice which can leave people confused about what to do. But the point is: no one knows what is best, there is just a growing body of evidence one way or the other.
Until there is a large enough body of evidence one way or the other, for example we are now pretty certain that smoking is an unhealthy habit, perhaps we ought to just go with out instincts. Logic tells us that we are more likely to survive by running away from a puma than standing still. It is difficult to imagine that taking vitamin D supplements could harm you, so maybe it is best, if you can afford it, to take supplements. Until we are pretty certain of something, as is the case with smoking and eating plenty of fruit and veg, perhaps it is best to go with instinct rather than the selective reporting of research in the press.
Coss, R.G., Fitzhugh, L.E., Schmid-Holmes, S., Kenyon, M.W. and Etling, K. (2009) The effects of human age, group composition, and behavior on the likelihood of being injured by attacking pumas. Anthrozoos: A Multidisciplinary Journal Of The Interactions Of People & Animals 22 77-87
Trying to teach a class about the misuse and abuse of statistics? Need examples?
There are many researchers who are passionate about exposing poor studies but, unfortunately, the incorrect use of statistics is still common. Many researchers still shy away from the rigorous application of statistical methods or, worse, use them incorrectly.
However, the good news is that there are people who are great at publicising statistical abuses for the general public. One of the best known of these in the UK is Dr. Ben Goldacre, writer of the weekly Bad Science column in the Guardian newspaper and online.
More specifically for medical statistics, cardiologist Dr. Eric Roehm, has a great site called Improving Medical Statistics on which he gives insightful examples of studies that have gone wrong.
Imagine we are playing monopoly. When it is my turn I roll the die five times in a row then choose the roll I prefer, the one which places my piece on a winning square. Wouldn’t you tell me I cannot do that, accuse me of cheating and walk off? It stands to reason that we should not tolerate the equivalent, the changing of endpoints to gain better results, in clinical trials.
What are “endpoints”?
Before a clinical trial is conducted, endpoints should be specified. These are the outcome measures of interest, for example in a trial of a smoking cessation therapy the primary endpoint would be smoking status and a secondary endpoint might be a reduction in the number of cigarettes.
What is wrong with changing endpoints?
Changing from the pre-specified endpoints once a trial has begun can introduce bias. As in the monopoly analogy, if the endpoints of interest are changed to get ‘better’ results, i.e. more ‘significant’ or publishable results, the trial will produce biased results which do not inform research. This includes the selection of new endpoints which display a trend towards ‘significance’ or endpoints that have been investigated but not reported because they fail to display the desired trend. This increases the chance of false positive (type 1) errors.
Further discussion of problems created by changing endpoints can be found in the very clear and helpful essay by Scott Evans. Also discussed are the reasons why changing endpoints may be appropriate, for example if more accurate biomarkers or outcome measures are discovered which could contribute more up-to-date knowledge. Scott Evans proposes a series of issues that need to be considered in order to assess and handle changes in endpoints in clinical trials.
Are many trials guilty?
Chan et al. assessed the selective reporting of outcomes in 102 trials and, through comparing the published results and their protocols, found that 62% of trials had at least one primary outcome that was changed, introduced or omitted without explanation. Furthermore, Chan et al. sent a questionnaire to the lead investigators and found that 86% (42 out of 49 who responded) denied the existence of unreported outcomes despite evidence otherwise.
This, plus evidence from other reports and examples, suggests that many clinical trials are guilty of changing endpoints during a trial without justification.
What can be done?
Any changes in endpoints should be declared and explained to the registry of the trial and to any journals that manuscripts are submitted to. Some measures are already in place. For example, some journals now require the protocol to be submitted along with the manuscript, but we need more. This should be better ‘policed’, perhaps with people who are employed solely to investigate the selective reporting in trials.
Researchers and trialists should be made more aware of the dangers of changing endpoints. We should all be better informed of the problems that can arise and of the few situations where changing endpoints can be appropriate.
Please read Scott Evans’ short article to further your awareness of why changing trial endpoints is problematic.
See here for definition of Medical Statistics.
The UK-based animal health division of a company is urgently seeking a medical statistician to perform a meta-analysis of 26 published papers on the efficacy of a vaccine. If you are interested in this opportunity, email .