Tag Archives: drugs

A new entity is born: CDaR

Have you ever been told the blood test is positive and the disease in question is shocking – Cancer, an STD (but you don’t sleep around!), MS?  Have you every wondered why it is that some drugs get withdrawn years after, and millions of prescriptions after, they were first approved?  Surely, you’ve read a headline that coffee is good for you and chocolate bad, or was that chocolate good and coffee bad or were they both good, or both bad? Probably you’ve read all those headlines.  What does it all mean?  Am I sick or not (I heard some tests falsely give positive results)? Does it matter if I’ve been taking that drug or drinking three cups a day?  The answer to all those questions depends on one thing – clinical data research.  That is, it depends on how we collect the numbers, and what story those numbers are telling us.  Today, I am thrilled to announce that I have had my department’s (Department of Medicine, University of Otago Christchurch) endorsement to establish a new group, Clinical Data Research (CDaR), which will focus on the stories numbers in medicine tell us.

Source: Pickering et al http://ccforum.com/content/17/1/R7

Source: Pickering et al http://ccforum.com/content/17/1/R7

My recent expertise, as readers of this blog may have picked up, is in Kidney Attack (or Acute Kidney Injury). My contribution, as someone with a physics background, has been in data analysis and mathematical modeling.  It has been a privilege to have been involved with many discoveries and helping bring to light the stories of the biomarkers of that disease and the results of a unique randomised controlled trial.  Kidney Attack is a notoriously difficult to detect, and, partly because of that, one that has no effective treatment.  I’m currently working on the story of the association of Kidney Attack with death following surgery with cardiopulmonary bypass.  I am now looking to take those skills and work with other researchers in other medical specialties who generate data and are looking to tell its story (although I will still work on the kidney data!).  I’m particularly keen to engage with more students and pass on some of the data analysis skills I have acquired.  Moves towards open data as well as collection of data in large databases is providing more opportunities to assess the efficacy of health interventions and detect disease risk factors. The prospect of personalised medicine is one of both hope and hype. To sort fact from fantasy in all these areas will require development of new analytical techniques and careful assessment of evidence. This is what I wish to devote the rest of my career to, and to inspire others along the way. John Ioannidis, a highly respected biostatistician, once wrote an essay entitled “Why most research findings are false”  It is a scary thought that many interventions and diagnostic techniques in medicine may be based on biased studies (usually inadvertently biased!). More data will help reduce the bias, if it is treated nicely.  I promise to do my best to treat my data nicely, after all it is your and my health that is at stake.

I posted a few weeks ago my ten commandments of a data culture.  This is the ethos of CDaR.  Below is the lay summary of the new entity.

Group Name:            Clinical Data Research (CDaR)

Department:             Department of Medicine

Institution:                University of Otago Christchurch

Aim: To provide transparent evidence, with the lowest possible risk of bias, of the utility of biomarkers and efficacy of treatments in health or disease.

Lay summary of our aim: We aim to save lives and reduce the burden of disease by applying new ways to collect and analyse clinical data to better diagnose diseases, to predict the course and outcomes of diseases, and to assess how well treatments work.  We do this because we all want the best possible health outcomes for our communities, our families, and ourselves, with the least possible harm done along the way.  We are excited by the new ways scientists, including those at the University of Otago Christchurch, have come up with to measure disease, disease risk, and treatment outcomes. We are also living in an age of unprecedented data generation. To discover both benefits and harm in all this data and to make those discoveries available to all those making clinical decisions requires people dedicated to analysing this data in a transparent and open fashion that exposes both the good and the bad. That is who we want to be and who we want our students to become.

Definition:  A biomarker is any measureable quantity related to disease risk or diagnosis, or disease or health outcomes.

Are we getting safe medicines?

Do you read the small print about side effects?  Does your doctor tell you?  What are the chances that taking a medicine will kill you or make you ill in an entirely new way?

This last question is one reason why trials are run before a medicine is approved for use.  However, there is an inherent flaw in the system.  No matter how many people are in a trial there is a chance that a side effect will be missed.  Consider this: Imagine choosing one school class out of all the classes in the country to check the prevalence of albinism.  Given only about 1 in 17,000 people have albinism then you can imagine that it is unlikely that you will find an albino person.  However, you may find a red haired child because the prevalence is much higher.  If, though, you check a whole school you may still not find albinism.  Can you, then, conclude there is none?  No, because even if the school has 2000 pupils there is only a small chance of finding the condition.  Quite simply, the rarer the condition the more pupils need to be checked.  In terms of drugs, the rarer the side effect the more people need testing.

However, the difference between new drugs and our analogy is that trials are looking for unknown side effects.  What this means, is that a statistician can turn things around and say that for a trial of a given size if a side-effect is not seen what is the maximum prevalence of that side effect.  If, for example, you only cared if the drug increased the risk of a side-effect by more than 5 times (Relative Risk = 5) compared with those not taking the drug, and the event was relatively rare (say 1 in 5000), then you would need to have a trial with 14,707 people taking the drug and another 14,707 in a control group (e.g. taking a drug already used to treat the disease) in order to be reasonably certain that the new drug did not increase the risk by more than 5 times (see the table).  The side-effect you are interested in may be serious (eg kidney cancer), but if the drug is saving many lives in the first place (eg a drug that suppresses the immune system and allows transplants to take place), then it may be an “acceptable” risk.  The point is that trials must be of sufficient size to measure an “acceptable risk.”

SampleSizeIn a recent article published in PLOS Medicine (see here) researchers looked at the number of participants in trials of drugs approved by the European Medicines Agency over the last decade.  Quite shocking is that for medicines intended for long term use (Chronic diseases) nearly 20% were approved even though they did not meet the Agency’s own criteria for numbers of patients in studies, and these were very very low numbers indeed (300 over 6 months)!  Only about 10% of studies had sufficient participants to pick up on a risk of greater than 5 fold with an incidence of more than 1 in 1000.  This highlights why it is absolutely imperative that there are further ongoing studies monitoring side effects of drugs once the drug has been approved.  Anyone who has read Ben Goldacre’s book “Bad Pharma” will know such studies are often poorly done if done at all. How do we ensure such studies are done?  Do we legislate that drug companies do them (potential bias here), or do we make sure we have a well trained, adequately funded independent group of scientists able to do this?  If you think the latter, let your MP know!  In the meantime, let the medicated beware.