Canada needs you!

Millions of dollars are being invested to attract biomedical researchers into Canada. The Canada Foundation for Innovation (CFI) are investing C$45.5 million to support, and therefore hope to attract, scientists working in key areas such as biomedicine, genomics, genetics, environment, natural resources and health research.

The Canadian Minister of Industry, Honourable Tony Clement, acknowledged that “the Government of Canada understands that advances in science and technology are essential to strengthen the competitiveness of Canada’s economy”. The investment will therefore provide ‘state-of-the-art’ laboratories and equipment to 312 researchers in 44 universities across Canada in order to ‘jump-start’ 251 research projects. The investment is intended to attract more researchers to the country by demonstrating that Canadian research institutions remain world class. Perhaps now is a good time to move to Canada!


Click here to read the CFI news release.

Capabilities of global health statistics

Are ‘developed’ and ‘developing’ countries really that different nowadays?

Professor Hans Rosling, a professor of global health at Sweden’s Karolinska Institutet, explores the differences between countries and gives a fantastic demonstration of the capabilities of global health statistics. A very lively video that should interest everyone.



Definitions of medical statistics and biostatistics.

Water fluoridation: will medical statisticians get their teeth into the issue?

An on-going public health debate has been re-ignited: should fluoride be put into water supplies? NHS South Central, a strategic health authority in the UK, will decide in February 2009 whether fluoride ought to be introduced into the Southampton and south west Hampshire water supplies. Around 195,000 people would be directly affected, but many more would be indirectly affected as other health authorities and primary care trusts watch the final decision closely.1

The water fluoridation debate has been going on a long time. Does fluoride improve dental health? Does fluoride damage other aspects of health? Would it be cost-efficient? Is it ethical for the state to take personal choice away?

Currently around 5 million (11%) people in England receive artificially fluoridated water, mainly in the Birmingham, Tyneside and West Midlands areas.2

NHS South Central is involving as many people as possible in the debate. For example, they have commissioned reports from economists and clinical experts, and asked residents to share their views. As of 12th December 2008, 8,000 residents had responded, suggesting that many people have passionate views about fluoridation.3

Whilst involving so many people is bound to be informative and help the final decision, how many medical statisticians, if any, have been involved? There is a great scope for medical statisticians to inform public debates such as this, especially ones who can communicate research effectively to the many people involved from a wide variety of backgrounds.

Read more about the debate.  


  1. NHS South Central. Accessed on 16/01/09 from NHS South Central website.
  2. British Fluoridation Society. Accessed on 16/01/09 from BFS website.
  3. NHS South Central. Accessed on 16/01/09 from NHS South Central website.
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Is anyone using Google Flu Trends?

Google Flu Trends was launched in November 2008, but what has become of it since?

Google Flu Trends uses people’s Google searches to assess influenza activity across the US. It is a daily update of the number of people in different areas who use particular search terms, indicating they might be suffering from flu. At the moment it is just in the US, but it may be developed for other countries.

Google claim that it can “accurately estimate current flu levels one to two weeks faster than published Centers for Disease Control and Prevention (CDC) reports”. (Google Flu Trends website) Whilst this is impressive, what has the information actually been used for?

Most of the media attention focused on the ethical issues surrounding Flu Trends, such as whether it is an invasion of privacy. Now that people seem to have accepted Flu Trends, perhaps the focus should be on how it can actually be used in reality, not just in theory.

There was a lot of talk of how the information could theoretically be used, for example Google say that it “may enable public health officials and health professionals to better respond to seasonal epidemics”. Are there any professionals using it? Or are any individuals using it?

It is hard to imagine anyone in the US regularly checking Flu Trends in their state to decide whether or not to be in contact with people. Theoretically they could use it to advise them whether or not to get a flu vaccine, but will anyone actually do this?

It would be a real shame if such an impressive resource goes to waste. Please inform us if you use Flu Trends.

Google Flu Trends:

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A comment on comments

Personally, flicking through a journal such as The Lancet or the British Medical Journal, the most interesting section is the ‘comments’ or ‘letter’ section. The articles there are generally shorter than in other sections, and therefore it is easier to get a picture of new developments or current concerns. Most importantly, they usually involve some evaluation of previously published articles or developments. Not only are the ‘comments/letters’ articles interesting, but they are very important for future research.

Recently (November 2008) a comment was published by Boys et al. in The Lancet which commented on a TV programme and an editorial on prenatal screening for Down’s syndrome.1 The editorial and the TV program concluded that after prenatal serum or ultrasound screening for Down’s syndrome, two healthy babies are miscarried for every three Down’s syndrome births that are prevented.2-3 The Boys et al. comment evaluated both the methods used to get this and other statistics, and the appropriateness of the early online publication (according to Boys et al. it was published early to coincide with the television broadcast). In their opinion, the editorial would have benefited from an independent review process and submission to a peer-reviewed academic journal.

Aside from how interesting the commentary is (both the subject matter and the passion with which it is written make it a very stimulating read), it serves as a great example of how important these articles in the ‘comments/letters’ sections are. The article (similar to many others) evaluated the statistical methods and conduct of previous publications, and called research into question. Perhaps the more this happens, the more researchers will strive to improve the quality of their investigations.


1. Boys, C., Cunningham, C., McKenna, D., Robertson, P., Weeks, D.J., Wishart, J. (2008) Prenatal screening for Down’s syndrome: editorial responsibilities. Lancet 372 (9652) 1789-91
2. Channel 4 News. Exclusive: research suggests Down’s screening risk is ‘unacceptable’. Sept 16, 2008. Accessed on 12 Dec 2008 from Channel 4 website.
3. Buckley, F. & Buckley, S. Wrongful deaths and rightful lives – screening for Down syndrome. Down Syndrome Res Pract 2008; published online Sept 16. DOI: 10.3104/editorials.2087 (accessed Dec 12, 2008)


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NHS permission

In November 2008 a new system was launched in the UK: The National Institute for Health Research (NIHR) Coordinated System for gaining NHS Permission (CSP). This aims to standardise and streamline the process for gaining NHS permission to conduct clinical research.

The NIHR claim that it will reduce any duplication in the NHS review process, provide a single point for applications, and clarify the roles and responsibilities of those involved (sponsors, investigators, etc). Many researchers will be welcoming this initiative!


To find out more visit the NIHR website.

New qualification

A new statistics qualification has been created by Quintiles, a “pharmaceutical services company” (a CRO), and SAS. According to Quintiles the “pharma and biotech industries have statistical requirements that our standard certification exams don’t measure”. The new qualification will create specialists in SAS programming for evaluating clinical trial results.

They will be taught the skills necessary to organise, analyse and report clinical trial results. The program is expected to be ready by May 2009.

The introduction of training of any kind is a great thing. Especially in these times where many people have very good general skills but need more specialised ones. However, hopefully the new program will prepare the “specialists” for work in clinical trials outside of CROs or industry, and at least give some mention that there are other statistical packages available, not just SAS. 

Quintiles’ news release for this qualification can be found on their website

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Beware of meta-analysis manipulation!

Many people suspect that statistics are manipulated to suit an investigator’s motives.

They might be right! How can you tell?

Surely meta-analyses help provide reliable information?

Actually, there is potential for manipulation in meta-analyses too…

What is a meta-analysis?
Briefly, meta-analyses attempt to pool data from different sources on a particular topic. For example, if you were interested in how effective ibuprofen is for treating a headache compared with paracetamol, you could get data from relevant previously published trials and summarise what they all found. That sounds like a systematic review… There is a clear difference between a review and a meta-analysis, however it is often not understood or not remembered! In a review the results from each source are discussed. In a meta-analysis the actual data (summary measures from each trial e.g. sample size and number of events) are used to obtain ‘pooled’ or ‘combined’ or ‘common’ estimates.

Two main methods for manipulation
[Note: The following commentary is not intended to imply that any motives are not honorable. It is merely an up-to-date example of how results from meta-analyses can differ.]

First, the results of a meta-analysis will differ depending on which studies are included. For example, to investigate ibuprofen, one meta-analysis could be performed using all studies which suggest that ibuprofen is better than paracetamol, and one meta-analysis could be performed with all studies which suggest that paracetamol is better. Clearly, these meta-analyses would produce different results. It is quite common to find more than one group researching the same topic with a meta-analysis, and it can be difficult to select which studies to use. To take an up-to-date example, a recent article on the PharmaTimes website described two meta-analyses conducted to investigate Spiriva and Atrovent medications for chronic obstructive pulmonary disease (COPD).1 One meta-analysis, which was published in the Journal of the American Medical Association, concluded that the drugs were associated with an increased risk of cardiovascular death, myocardial infarction and stroke. 2 The second meta-analysis concluded that there was no evidence of an association.3 The results of this analysis are currently being reviewed by regulatory authorities and are therefore not published yet. These two analyses are a great example of how, depending on which studies a meta-analysis includes, different results can be gained. Both analyses used controlled clinical trials and both included a large number of patients (more than 14,000). Although there could be many differences in their designs, which cannot be evaluated until the second analysis is published, it is most likely that the main difference between the analyses are the studies that were included. The first meta-analysis used 17 trials identified from systematic searches, and the second used 30 trials, which we do not currently know where from.4

The second main method for manipulation is the actual method of analysis. In brief, there are two main types of model that can be used: a fixed or a random effects model. What is supposed to happen, as with most research, is that a plan is developed for the analysis before conducting any. The type of model to use should depend on the distributional assumptions and the amount of heterogeneity (how different the studies are). For example, fixed effect models assume there is no heterogeneity in effect sizes, therefore if there is evidence of heterogeneity, random effect models are normally more appropriate. However, this does not seem to be well understood by investigators conducting meta-analyses as often they run every type of analysis then chose which statistic most suits them. The second main method for manipulation in meta-analyses is therefore not performing the most appropriate analysis. Perhaps this attitude arises from misinterpretation of respectable literature, such as the learning material on the Cochrane Collaboration website, which suggests running the different models to investigate heterogeneity.

Do not be put off!
Do not let all this put you off conducting or reading meta-analyses. This article was only intended to make people be more cautious of results from meta-analyses. Commonly people believe that meta-analyses are trust-worthy because they incorporate lots of information or because they calculate statistics (as opposed to reviews). Whilst meta-analyses do have many advantages, moderate trust should only be put into results from meta-analyses which give you special reason to trust them, for example they were conducted by respectable statisticians or their methodology looks to have been well-planned in advance. If you are thinking of conducting a meta-analysis, make sure you know how to do it correctly!

How can I correctly conduct a meta-analysis?
There are many books dedicated to meta-analyses, a quite good list is available from

There are some useful online resources, such as the previously mentioned learning material on the Cochrane Collaboration website.

Find yourself a statistician that has experience in meta-analyses. However, beware of the statisticians that have just learnt from other researchers instead of learning from qualified professionals!

Better still would be to enrol yourself on a course, however courses that are convenient for your location can be difficult to find, and then you have to get a place on the course…

1. Grogan, K. Boehringer leaps to defence of Spiriva over cardiovascular risk claim. September 2008. Accessed on 26/11/08 from Pharmatimes website.
2. Singh, S., Loke, Y.K., Furberg, C.D. (2008) Inhaled anticholinergics and risk of major adverse cardiovascular events in patients with chronic obstructive pulmonary disease. Journal of the American Medical Association 300 1439-1450
3. Established safety profile of Spiriva confirmed by 30 rigorously controlled clinical trials. September 2008. Accessed on 26/11/08 from Boehringer-Ingelheim website.

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Q1: What is Medical Statistics?

Two non-scientific friends of mine once tried to figure out what a medical statistician does. After much consideration they came to me with these two things:

1. “If someone’s discovered an antidote [I think they meant a treatment!] to a disease, you test it and see if it is any good?”

2. “You come up with those figures you see in newspapers, things like ‘eating sausages doubles your risk of a heart attack’?”

They actually summarised medical statistics quite well!

My slightly more technical answer would be that medical statistics is the application of statistics to medical and health matters. A medical statistician should have training in the theory and practice of statistics and be able to apply that knowledge to improve global health. For example, a medical statistician should be able to select and use appropriate statistical techniques to analyse medical data, such as data from a clinical trial or an epidemiological study. I.e. we test “antidotes” and come up with figures about the population’s risk… so far I have not investigated sausages though.

Related item:

Medical Statistics and Biostatistics defined