My name is Samantha Santoro, and I am completing my second year in the English and Biology majors at the University of Toronto, St. George. A rather unconventional combination, when reviewing past students of Dr. Tyrrell’s lab. I was a 2017-2018 Research Opportunity Program (ROP) student in Dr. Pascal Tyrrell’s lab, and my work chiefly consisted of evaluating the internal vessel wall volumes of carotid arteries in a particular cohort of patients provided by the ongoing prospective CAIN study. My ROP was in the field of Medical Imaging. I am the co-president of the student club known as Watsi, with the main chapter based in San Francisco. I am also a special contributor to the Rare Disease Review, along with volunteering at an amalgamation of charity walks and fundraisers.
My ROP project was a turbulent experience – although that word is typically associated with a negative connotation, I regard my ROP299Y1 as one of the most humbling, interesting, and educative experiences that I have had thus far – most definitely not negative. However, to say everything went smoothly would be discrediting the lessons I learned from when things were not idyllic and smooth. My project, as aforementioned, statistically analyzed data provided by patients part of the CAIN study (an analysis that could not have existed without Dr. Tyrrell’s generous and unwavering support). My study determined that patients who were found to have IPH, or what is known as intraplaque hemorrhage, when I analyzed their MRIs, were also found to have increased vessel wall volume. This conclusion is incredibly significant, as IPH is a surrogate marker for atherosclerosis and could potentially be an indicator for patients at risk of future cerebrovascular events (namely, ischemic stroke). As strokes are currently the number three killer in the U.S and Canada alone, and heart disease number one, having a potential indicator for patients at risk of stroke would greatly benefit clinicians in their practice, as well as patients themselves.
As aforementioned, studies similar to my own are currently underway by the Canadian Atherosclerosis Imaging Network, furthering the important research in this field. The VBIRG (Vascular Biology Imaging Research Group) was the lab in which I primarily worked throughout the course of my ROP, at Sunnybrook Hospital. Moreover, I also worked on systematic reviews and reports outside of the focus of my project, in the fields of medical ethics and AI in the radiology workplace – both of which were opportunities provided to me by Dr. Tyrrell, and both of which were incredibly valuable experiences, allowing for me to broaden my knowledge of certain areas of medicine and science that are developing and expanding.
Although my project was littered with its own respective difficulties – a substantial number of drafts throughout each step of the program (more than I had ever made, even being an English student); a reluctant, but later fulfilling, acquaintanceship with the post-processing software VesselMass; and several late nights learning about the field of statistics – it is in light of these difficulties, and at present having overcome them throughout my ROP, that I remember Dr. Paul Kalanithi’s words in his memoir When Breath Becomes Air: “It occurred to me that my relationship with statistics changed as soon as I became one”. He, too, had studied Biology and English. I may not have played a lead role in the statistics I had been working with, but I can now say that understanding what they meant and how they were formulated has generated a deep respect in me for the field of statistics.
My poster was on display at the 2018 Research Opportunity Undergraduate Fair. Special thanks to Mariam Afshin, my supervisor at Sunnybrook Hospital; Bowen Zhang, for answering each question I had while at Sunnybrook; John, and the rest of the lab team; and Dr. Pascal Tyrrell, for answering my email last February and holding my interview on the same day as my Chemistry exam. Never before had I met such an – in a word – outstanding professor, and I dare say that I will never meet one like him throughout the rest of my academic journey.
|Or Attributable Risk Reduction…|
First let me wish you all a fantastic New Year! Last year was crazy and I think this year is looking like it will be more of the same…
So in a previous post called Risky Business: Is It All Relative? we started talking about risk. We agreed that in lay terms a risk is generally associated with a bad event. However, a risk in statistical terms refers simply to the probability (usually statistical probability value between 0 and 1) that an event will occur, whether it be a good or a bad event.
We also defined the risk of “smartphone thumb” as the number of new cases of smartphone thumb (the outcome) in a given period of time divided by the total number of people who own a smartphone (the exposure) and are at risk. This was called the cumulative incidence or absolute risk. Now what if we wanted to compare this risk to people who did not receive a smartphone for their birthday or Christmas for that matter? Let’s look at the results in a contingency table:
Let’s say 20% of smartphone users develop smartphone thumb whereas only 10% or non-smartphone users do. The RD is then equal to 10% (0.2 – 0.1 *100). The reduction in the chances of experiencing smartphone thumb who own a smartphone is the AR% which in this case is 50% (0.1/0.2*100).
For now, decompress listening to “Under my Thumb” by the Rolling Stones. Classic…
See you in the blogosphere,
Now this movie takes me back a few years. Tom Cruise’s first big movie Risky Business. His underwear dance scene is pretty famous (haven’t scene it yet? Have a gander here).
So what does Tom Cruise in underwear have anything to do with our blog? Well it is the concept of risk that interests me today. David Streiner was a fantastic professor of mine and is the author of many great stats publications. He talks about risk here. I will endeavor to do the topic justice with his help over the next few posts.
What do we mean when we talk about risk? In lay terms a risk is generally associated with a bad event. However, a risk in statistical terms refers simply to the probability (usually statistical probability value between 0 and 1) that an event will occur, whether it be a good or a bad event.
Now that you are clear on that, you are probably wondering what are the best ways of describing risk or – better yet – comparing estimates or risk between groups (wondering what a statistical estimate is? See my earlier post here).
Let’s say that you have just received the latest and greatest smartphone for your birthday and you can’t wait to text everyone you know to tell them about it. This would be considered the exposure: your smartphone. The outcome would be “smartphone thumb”: a painful thumb resulting from smartphone overuse (don’t believe me? See here). We can define the risk of smartphone thumb as the number of new cases of smartphone thumb (the outcome) in a given period of time divided by the total number of people who own a smartphone (the exposure) and are at risk. This is also called the cumulative incidence or absolute risk.
As you have an inquisitive mind, you are now wondering what would be the difference in levels between conditions: people with a smartphone compared to people without. Well this can be expressed as absolute differences in risk or relative changes in risk and I will have mercy and address this in more detail… next post!
For now, decompress by listening to the Barenaked Ladies singing Pinch me (believe it or not this song has something in common with Tom Cruise from Risky Business. Get it yet?).
See you in the blogosphere,
|Poster to be presented at the Department of Medical Imaging Resident Achievement Day 2016|
Where have a I been you ask? At my desk putting this program together! I apologize for being MIA for the past month or so but I it has been a busy time nurturing this fledgling program of MiNE (pun intended!).
Here is the premise:
Bridging the gap between clinical expertise and the science of managing and analyzing medical imaging data is challenging. To provide direction for data management as well as the analysis and reporting of research findings, we are in the process of introducing a data science unit – MiDATA – offering users an environment geared towards a “soup to nuts” approach to medical imaging research methodology and statistics. The Department of Medical Imaging of the University of Toronto is one of the largest in North America with a clinical faculty of more than 184 faculty, 60 residents and 80 fellows based at nationally and internationally renowned hospitals conducting cutting edge clinical research in the greater Toronto area. The challenge of any successful research and educational program is bridging the “know-do” gap. The goal of MiDATA is to facilitate impactful research through the efficient and creative use of a mentored learning environment.
Shout out to our collaborators the Division of Biostatistics from the Dalla Lana School of Public Health!
Tomorrow is the official unveiling at the 2016 Department of Medical Imaging Resident Achievement Day. I thought I would share with you our poster as a sneak peek…
Now that was a great movie: Interstellar. See the trailer here for a refresher. So this movie talked a lot about worm holes – essentially an area of warped spacetime. Theoretically a worm hole could allow time travel. Want to know more? Grab a large coffee and see here. You may be thinking what all this has to do with medical imaging but, believe it or not, I posted about x-rays in space earlier in the blog (see here).
Now, we have been talking about Bradford Hill’s criteria for causality and today we are addressing the fourth one: temporality. The exposure of your association of interest should always precede in time the outcome. If factor “A” is believed to cause a disease, then factor “A” must necessarily always precede the occurrence of the disease. So for example the act of smoking (or being exposed to second-hand smoke) must precede the development of lung cancer for the relationship to be considered causal. This is the only absolutely essential criterion (out of nine).
Easy one, right? Next time I will be talking about biological gradient.
I am not sure you need time to decompress today as it has not been too taxing… but listen to Bonnie Raitt Nick of Time anyway…
… and I’ll see you in the blogosphere.
Well, if you are relaxed and heading nowhere in particular then I guess you probably won’t be too concerned with showing causality either. In our past few posts we have been discussing Bradford Hill’s criteria for determining causality (see Strength and Consistency for a refresher). If you are stressed out already, have a listen to “Come the morning” from an up and coming Canadian artist from Winnipeg, Manitoba – Sebastian Owl – before reading on.
Today we will talk about the third of the nine Hill criteria: Specificity
Next, post we will talk about the oh-so-important criterium: temporality.
If you are nowhere in particular then you are not being specific to your whereabouts – right? Anyway, why don’t you watch this great Film festival short by Mason Cardiff, Nowhere in particular, to decompress and…
… I’ll see you in the blogosphere.
Do you remember the Rain Man movie with Dustin Hoffman and Tom Cruise? Great movie that introduced Savant Syndrome to theater audiences all over the world. The savant syndrome is a rare condition in which persons with autistic disorder or other mental disabilities have extraordinary skills that stand in stark contrast to their overall handicap. There is a very interesting documentary on Kim Peeks who was the inspiration for the movie here. Anyway, last post we talked about strength – one of Bradford Hill’s criteria for causation (see here for a refresher). Today we will talk about consistency, a good qualifier for the often obsessive and ritualistic behaviors of autistic savant persons.
An association between two entities is consistent when results are replicated in multiple studies in different settings using different methods. So if a relationship is causal, we can expect to find it consistently in different studies and among different populations. This implies that many studies need to be done before meaningful statements can be made about any causal relationship.
A great example of this is the long debated causal relationship between smoking cigarettes and lung cancer. It took hundreds, if not thousands of highly technical studies and many, many publications before a definitive conclusion could be made that cigarette smoking increases the risk of cancer and in a causal manner (see here for a statement from the CDC Surgeon General).
So be consistent in your smoking cessation and you will consistently avoid the risk of lung cancer…
Next post we will tak about Bradford’s third criterium: specificity.
Relax listening to the very eighties styled theme music to Rain Man and…
… I’ll see you in the blogosphere.
Yes, back to the eighties. They were my high school and undergrad years – so very memorable! This song – Running Up That Hill – by Kate Bush was her first great hit from that time.
So, why was she running up that hill you ask? Well, it was because she had finally come to realize the importance of establishing the minimal conditions needed to establish a causal relationship between two entities, of course! Somewhat like the story of Archimedes who leapt from his bath yelling “Eureka” in excitement having discovered a law of physics that would later become the building block to fluid mechanics (see Archimedes principle).
In 1965 (no, I was not born yet – but just!), Austin Bradford Hill a British medical statistician proposed minimal conditions needed to establish a causal relationship between two entities. These later became know as the Hill’s Criteria. Very often people get the relationship of association confused with that of causality. See my previous post Rebel Without a Cause for some insight on when an association can be considered as cause and effect.
Today we will talk about the first of the nine Hill criteria: Strength
– The strength of an association is defined as the size of a given association as measured by appropriate statistical tests. The stronger the association, the more likely it is that the relation between the two entities of interest is cause and effect. For example, the more highly correlated hypertension is with smoking, the stronger is the relation between the exposure, smoking, to the outcome, hypertension. Though we cannot be sure of the direction of the relationship (this will be achieved when we discuss Temporality) – as hypertension could hypothetically lead subjects to smoke – we can certainly decide that the strength of the association observed supports our argument of causation.
Look at that, we have completed the first criterium all ready! Next we will look at Consistency.
Have a listen to “Strength Of A Women” by Shaggy to recover from today’s fun and…
… I’ll see you in the blogosphere.
No, I did not say “bodily functions”. That is discussed in another blog. We’re talking math today.
So, my son was doing his homework the other night and yelled out from his room:”Daaaadddyyyy!!! Do you know what a parabola is?” For those of you who do not have teenage children this is code for “can you help me with my homework”. After reliving a few high school memories that came along with the word “parabola” I wondered over to his room to see what the latest homework challenge was going to be…
When helping my kids with their homework, I often think of how important and still relevant some of the basic math is we learnt in high school. I would like to talk a little about basic functions and how they are still used well after you have handed in your last math homework assignment.
Many (most?) scientific laws are expressed as relations between two or more variables – often physical quantities. Next comes the chicken or the egg conundrum. Were the results from an experiment used to formulate “empirical laws” or did we use existing knowledge and math to come up with new theories – that we will invariably later have to test. Welcome to the world of research!
If two variables are related in such a way that one of them (the dependent or response variable) is determined when the other is known (the independent or explanatory variable), then there exists what is termed a functional relationship between the variables.
y = f(x)
For example the relationship of height to weight in humans. In general, the taller we are the heavier we get. This results in what is called a straight-line relationship.
But not all relationships are linear. How about if we were to throw a ball up into the air and measure it’s trajectory? It would look a little like the picture on the left.
Although initially the value of the height of the ball increases with time, there comes a point when the ball stops rising and starts to fall back down to earth. The resulting curve is called – you guessed it – a parabola.
The math functions for the parabola and that of the straight line are actually related. Yes, I am serious! They both belong to the family of math functions called polynomials. In my next posts I will talk a little about how we describe these functions and how we can put them to work for us in the world of medical research.
For now, decompress watching this hilarious movie trailer Biloxi Blues which is all about basic training (you can now relate) and…
… I’ll see you in the blogosphere,