Monthly Archives: August 2013

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  Have you every wondered why it is that some drugs get withdrawn years after, and millions of prescriptions after, they were first approved?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

Source: Pickering et al

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.

Papanui Campus closes!

917 days; 131 weeks; 2.5 years.  However you look at it, it’s a long time to be temporary.  Today the Papanui Campus of the University of Otago Christchurch, a.k.a the Versatile workshop in my front yard, closed.  The sole permanent occupant (moi) became the last of the academics to return to the “main building” of the University of Otago Christchurch after we were unceremoniously evicted on 22 February 2011 (about time someone came up with a better name than “Main Building”). I’ve written elsewhere of that day when I commemorated two years since the earthquake and of the value of the Papanui campus when it turned  800 days. I’ll miss having the family close (perhaps not the dog), impromptu games of basketball (only 5 minutes boss…honest), and being on hand during that time when we went through all that shaking.  I shan’t miss the cramped space, the expense, or the loss of a workshop (maybe my son will be able to have his train set up again!). Today marks a new era for me as I return to an office in the centre of the Christchurch campus … I hope to discover some colleagues here that exist in the flesh and not only as words or images in cyberspace. In the process I hope to continue those incremental discoveries which will lead to better health for many.  My department has just endorsed a new plan for that… but that is the topic of a future post.

My 10 Commandments of a Data Culture

Thou shalt have no data but ethical data.

Thou shalt protect the identity of thy subjects with all thy heart, soul, mind and body.

Thou shalt back-up.

Thou shalt honour thy data and tell its story, not thy own.

Thou shalt always visualise thy data before testing.

Thou shalt share thy results even if negative.

Thou shalt not torture thy data (but thou may interrogate it).

Thou shalt not bow down to P<0.05 nor claim significance unless it is clinically so.

Thou shalt not present skewed data as mean±SD.

Thou shalt not covet thy neighbour’s P value.