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What Makes a F.I.N.E.R. Research Question? F is for…

…Fugitive? Well that is certainly how you can feel when out with your friends and knowing you should be home studying for your upcoming stats 101 exam. Not sure how that feels? Watch the trailer from the fantastic movie The Fugitive.


Now that you are well versed in dreaming up research questions and driving everyone around you nuts, I thought I would chat a little about how you can assess whether you have a good one “on the line” or a stinker.


A great mnemonic suggested by Hulley et al is F.I.N.E.R. and suggests that research questions need to be feasible, interesting, novel, ethical, and relevant. Today we will talk about the first one.




F is for FEASIBLE. When you are creating a research question you must always ask yourself: “Can I answer this question?”. There are many reasons that may stop you from completing your quest to answer a given question. Maybe not as dramatic as in the Quest for Camelot, but it is important to take note of your limitations:


1- Do you or other members of your team have the know-how or technical expertise to plan, execute, and analyze the study required to answer your question? Maybe you need to brush up on your skills before embarking on your adventure… or phone a friend!


2- Can you afford the time to complete your quest? Do you have the money to pay for it? Gold wins wars not soldiers (Game of Thrones season 1)! Always plan ahead so that your study does not get compromised or cancelled because you don’t have the money or the time to continue (summer student projects often fall pray to this).


3- Do you have access to enough subjects to appropriately power your study? Sample size calculation is a fun and challenging topic and we will address this in later posts. If you want to know how many of your classmates – say 100 – want to go camping over the weekend to celebrate the end of the school year, how many should you ask before you are confident it will be worth your effort to organize the trip? All of them? Some of them? But how many…


4- Are you asking too much at once? Is the scope of your research question too broad? Focus on the most important goals. 




Don’t become a “Jack of all trades and master of none“! Aim for a better answer to the main question that you are interested in.













Next post we will move on to I…

See you in the blogosphere,


Pascal Tyrrell



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The Importance of Research

There’s more to the field of medical imaging than a bunch of stuffy radiologists huddled around a couple of monitors. As I mentioned before in my previous post about the history of the imaging technique, the field has undergone a rapid technological advancement in the past century or so, improving the clinical model of visualization. But let’s take a step back from all the scientific stuff for a brief second and look at these developments in a
slightly different light.

During the early stages of medical imaging, X-rays were able to provide people with an initial view of the internal structure of the human body. As limited as that first view may have been, it still played a pivotal role in both challenging and changing people’s perceptions on the human body – to the point where these details would eventually become common knowledge. Without all the major advancements in medical imaging, we could well expect to still be living in the dark.
To really hammer this point home, further advancements in the field would only continue to build on our understanding. What was once the accepted view of the human body has now been given a complete overhaul, thanks to the availability of imaging devices able to produce higher-resolution cross-sectional pictures.

The SparkNotes illustrated version of this post
So what’s the common thread in all of this? Research, of course. While the idea of research leading to new and exciting developments is a pretty basic concept in and of itself, it’s still an important one to keep in mind. Although the field of medicine is comprised of many different sectors, even at the base level there are plenty of opportunities to contribute meaningful ideas and suggestions. Just because you’re an undergraduate student, that doesn’t stop you from devising an independent thesis in an area you’re passionate about. Granted, I don’t want to be too idealistic here, given the logistics of funding, but an interesting and relevant pitch to your primary investigator
could go a long way. Who knows, you may find yourself presenting your findings at a research symposium, complete with nifty results and statistics to showcase your efforts.

The bottom line is, a little can go a long way, and if you already have a keen interest in science to start contributing as soon as possible. The entire medical field is driven by people with a knack for research and discovery – and while there’s never a shortage of great minds, there’s always room for more.
Thanks for reading,
Brandon Teteruck
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The Order in K-OS and Who’s Dog Is It?

Did you know that Einstein is also known to have contributed significantly to statistical physics? In 1905, he proposed an explanation for the phenomenon called Brownian motion – named after the botanist Robert Brown who first described the process. Essentially, particles suspended in a fluid (liquid or gas) exhibit a random motion (path) resulting from their collision with the quick atoms or molecules in the gas or liquid. This is the K-OS or more appropriately “chaos” of the process. Have a listen to The Dog Is Mine from K-OS to get you ready for some Einstein talk.


The problem with understanding Brownian motion is that the molecules are too light to move the floating particle and molecular collisions occur way more frequently than the observed jiggles.

 
Einstein’s genius was to realize that though collisions occur frequently, they are so light there is no visible effect but… occasionally, by pure luck, a bunch of hits from one particular direction leads to a noticeable jiggle. Cool. So when he studied this phenomena he found that despite the chaos there was a predictable relationship between the molecules (speed, size, and number) and the frequency and magnitude of jiggling. This is the order of the process. Maybe not like in the Godzilla – Nature Has An Order movie, but more in the the arrangement of things in relation to each other according to a particular pattern type order.


What is the take home message? That much of the order we perceive in the world around us is dependent on an invisible underlying disorder. Words of caution: though random variation can lead to orderly patterns, these patterns are not always meaningful. (See previous posts: Rebel Without a Cause and What Does the Fox Say for some hints on how not to be fooled) 

So what is the link between Einstein and The Dog is Mine K-OS song? The dog named Einstein from the Back to the Future movie, of course!



See you in the blogosphere,




Pascal Tyrrell

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The Key to Research: (Key)Words

Do you ever hear a good song on the radio, catch some of the lyrics, and try to type in those lyrics into Google or Youtube to find that particular song you rocked out to on the way home? When that happens and you Google it, do you ever count  how many options you need to pass until you hit the right song? 


Yes, you are not the only one, many people use Google to further explore some of the things they have come across throughout daily encounters. For each instance google is used, whether it be for a song or for neuroscience research and analysis, one thing remains in common: keywords. 


Keywords are essential when searching for various types of information, and the options appearing on any search engine are dependent on the keywords given. How does one establish appropriate keywords for a search engine entry? 
For instance, if one wants to find out more about medical imaging, perhaps using those exact words would give one a head start in finding information. If one wants to find out about the modalities of medical imaging, typing in ‘modalities of medical imaging’ may also be helpful as well. The tricky part becomes when searching for specific uses and studies of the use of those modalities, in medical papers. In any medical search engine, like PubMed, keywords can make or break a search, and are very specific, as the many sections of medical imaging involve many specific factors and details that differ from each study. So next time you decide to search something, whether it be as general as ‘medical imaging’ or specific as ‘cost effective analysis of CT scans,’ just remember that those keywords may give you what you need, or lead you to a place you don’t want. 
Keep (re)Searching!




Faith Balshin 
Follow us on Twitter! @MiVIP_UofT  
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What Does The Fox Say?

I have often talked about “inferential statistics” in this blog. Don’t remember? Have a quick peek here If Only I Had a Brain and here It’s Cold Out Today – Please Remember to Dress Your Naked P-Value.


Back in the saddle? OK. Lately, I have had the pleasure of addressing young minds (shout out to CAGIS who were AWESOME on Saturday at our Sunnybrook Health Science Center presentation) and I thought I might talk a little about what “inferential” means to statistics.


So What Does The Fox Say? And does Ylvis have the answer? Listen to the song while you read through the rest of the post. We live in a crazy complex world that is largely random and uncertain. This is a good thing as it would be mighty boring to know how everything will turn out in the future. Imagine sitting in the middle of the forest and counting and recording the sounds of ALL animals that pass you – by species! Wow, that’s a lot of data. Now as new research scientists (don’t forget to wear your Pocket Protector before heading out into the woods!) we like ways to describe and make sense of what we observe – we simply want to understand the world better or maybe we are working on a answer to our newly minted Research Question. 


Either way you are certainly thinking where does the randomness and uncertainty come into all this? Well, it exists in two places:


1- Most importantly, in the process of what you are interested in studying.


2- But also in how we collect our data (collection and sampling methods).


So you now have an incredible amount of data in your spreadsheet or on little pieces of paper in a shoe box. What now? You have gone from the world around you to data in your hand. You need to somehow capture the essence of all of your data and turn it into something more concise and understandable. You do this by finding “statistical estimators” which means performing appropriate statistical analyses. The results from these analyses will allow you to estimate, predict, or give your “best educated guess” at the answer to your research question.


So by going from the world to your data, and then from your data back to the world is what we call statistical inference.


For example after collecting many days worth of data in the woods, you find that all “furry” creatures make a a kind of barking sound whereas all “feathered” creatures chirp. Excited, you tell your friends that the next time that they are in the woods and they see a furry creature they can expect to hear them bark. However, we do not know that for sure and this is where the uncertainty creeps in.


Ylvis seems to think the fox says:”Ring-ding-ding-ding”. Maybe his data collection and sampling technique was different to yours. This contributes to error and we will talk about this in a later post.




Hopefully you do not feel like you are in the movie “Inception” and… we’ll see you back in the blogoshere soon.




Pascal Tyrrell









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A Crash Course in Medical Imaging

Oddly enough, there’s been a surprising lack of content about medical imaging on a blog with medical imaging in its title. So in order to fill that void, I’ll be providing a brief history on the development of the clinical technique used to visualize the human body.

The advent of medical imaging dates all the way back to 1895, following the discovery of X-rays by the German physicist, Wilhelm Conrad Roentgen. The first X-ray picture was then produced, detailing the skeletal composition of his wife’s left hand. However, the actual quality of this imaging process was still very primitive, only allowing for the visualization of bones or foreign objects.

    Much to Dr. Roentgen’s pleasure, Mrs. Roentgen
    had not discarded her wedding ring
    It was not until the 1920’s that radiologists would develop a more effective method of visualization. This process, known as fluoroscopy, involved either an oral or vascular injection of a radio-opaque contrast agent, which would travel through the patient’s gastrointestinal track. Radiologists could then take films tracking the agent, allowing them to view blood vessels and digestive tracks alike.

      By the 1950’s, imaging procedures progressed towards nuclear medicine, involving radioactive compounds. These compounds were administered to patients because they could be absorbed by cellular clusters being invaded by tumours. As compounds decayed and emitted gamma rays, the recorded radiation could then be detected by gamma cameras, signalling the location of any cancerous developments. 
          The 1970’s were a period of rapid advancement for the field, as a number of modern imaging techniques were developed for clinical practice such as: 

            • Ultrasound – Uses sound waves that are able to penetrate cellular tissue. Once they reflect off the body’s internal organs, the vibrations generate an electrical pulse which can then be reconstructed into an image. 
            • PET-CT Scan – Positron emission tomography (PET) uses compounds that emit positrons when they decay rather than gamma rays. It is now combined with a computed tomography (CT) device to generate a high-resolution image displaying sectioned layers of the scanned area. 
            • MRI – A Magnetic Resonance Imaging scanner runs a strong magnetic field through the body, aligning hydrogen protons. As the protons return to their original position in the atom, they generate radio waves, which are then picked up by the scanner and used to create an image based on signal strength. 

            Fast-forward to present day and over 70 million CT scans, 30 million MRI scans and 2 billion X-rays have been performed worldwide! The field of medical imaging is still growing by the day, with ongoing research leading to new developments.

              Thanks for reading,

                Brandon Teteruck 
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                Researcher’s Dream: Katy Perry Edition

                What happens when you put a famous pop superstar with various Billboard number one hit singles as an endorser for a medical field involving teeth, mouth and gum surgeries? 
                A Katy Perry-odontist!
                And no, I am not insinuating the likelihood of Ms. Perry giving up her “Hard Candy” tour and making her way down to Harvard Med for a doctor of dental surgery specializing in periodontistry, but in reality, when researching, there are a lot of weird combinations of research that actually lead to a plausible conclusion!
                Take cost effectiveness of MRIs, CT scans and ultrasounds. There are many variables pertaining to which machine is more cost effective, but in order to find that out, the research being done with regards to the cost involves stepping OUTSIDE THE BOX and figuring out unique key factors that all contribute to the cost, timing, and effectiveness. One must observe not only the actual cost of the machine, but also the condition the patient is in and the situation of the effected area. In order to look at that, maybe some family history must be dug up. And there you go! A whole research perspective on family history of certain patients, just to figure out cost effectiveness of a certain machine. Weird combination of research if you think about it, but in the end, very effective in reaching somewhat of a conclusion to the research question, just like the medical imaging equipment should be doing in the first place! Do not underestimate the lengths in research it takes to solve the question at hand, and always think outside the box, because you never know what you will find, and someday, maybe Katy Perry will open up her own clinic, and sooth patients with her very own soundtrack!
                Keep Researching and Singing, 
                Faith Balshin 
                Don’t forget to check out MiVIP’s twitter account, @MiVIP_UofT! 
                Comment on what you think are weird research combinations if you dare! 
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                Baby Steps and What About Bob?

                I had the pleasure of addressing the students from the SciTech program at Tomken Middle School last week. Bright, enthusiastic, and interested in science… all 165 of them! I was there to talk about our sister program – Medical imaging Buddies. Remember the MiB movies? Very funny. Have a quick peek for fun. I’ll wait here.


                So the question is always: “what do I do to get started?”. Believe it or not this applies to whether you are a 10 year old SciTech student or a radiologist on faculty with our department. I have been doing this for a while and I would like to share some encouraging suggestions that you may find helpful:

                • Read this blog! OK, so I am shamelessly promoting my own program. But it is a perfect place to start. Easy reading, no commitment, anonymous, informative, and best of all FREE! Look for more resources like this one.
                • As you are thinking about what has been said in the various posts think of what a next step could be that would move you closer to your goal of becoming a research scientist and at the same time not trigger a fight or flight response. Take Baby Steps just like in the movie What About Bob? What a hilarious movie but the small steps to slowly move you forward is no joke.
                • Start telling people about who you are becoming. Share with them some of your achievable and positive goals. This way they will be able to encourage you when you need a little push AND be proud of you when you succeed.
                • Don’t be afraid of failure. It is simply an unwanted outcome. So what. Learn from it and move on.
                • Finally, don’t be a silo (unless you are Bruce Cockburn and singing If I had a Rocket Launcher). Be a team player. Remember to always bring something of value to your team. At first, this may just simply be positive energy and enthusiasm – good enough for my team!
                 
                Questions? Post a comment or email me!
                 
                 
                See you in the blogosphere,
                 
                 
                Pascal Tyrrell
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                Rebel Without a Cause… Or Maybe?

                OK, enough with stats. Let’s talk a little about causality. You have been patiently wearing your pocket protector for a couple of months, asking the right questions all the time, and diligently reading this blog to glean as much information as possible to become a research scientist. 


                So what now? Do you feel a little like a Rebel Without a Cause? You are asking questions that are interested in describing an association of interest. How about the association between watching horror movies and myocardial infarction (MI). One possibility is that watching horror movies is the cause-effect of an MI. You’re thinking: sure but there must be other explanations. You are right! Actually there can be another 4 rival explanations:



                1- By chance alone you observed an association in your data. This is a spurious association.

                2- Due to bias (systematic error) you observed an association in your data. This is another spurious association.

                3- Effect – cause: having an MI is the reason (cause) for watching horror movies – reverse to what you were thinking.


                4- Confounding: watching horror movies is associated with a third factor that is the cause of MI. Say eating all those unhealthy snacks during the movie.


                And of course don’t forget your initial “gut feeling” cause-effect: watching horror movies is a cause of MI.


                Phew! That is a lot to think of. So what is important to remember? When designing your study to answer your question, you must always consider how to avoid spurious associations and concentrate on ruling out real associations that do not represent cause-effect. Especially those due to confounding.


                Take a break and watch the Chicken Game from Rebel Without a Cause and then listen to Rebel Music to calm down after the game of chicken. So is playing chicken with cars hurtling towards a cliff associated with death? Possibly. But in watching the clip you see that maybe there is a confounding factor… See it?




                Until next time in the blogosphere,




                Pascal Tyrrell

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                If Only I Had A Brain…

                So how was March break? My family and I went to Stowe, Vermont for a little skiing. Awesome. However, the 8 hour drive with 3 kids, our luggage, skis, snowboards, and snacks to get there… maybe not so much. We felt a little like Dorothy in the The Wizard of Oz.

                Last time we were talking about p-values and inferential statistics (see Naked p-value if you don’t remember) and I mentioned that I would talk a little more about hypothesis testing. Now Ronald Fisher believed that if you obtained a large p-value when performing a statistical test then you would reject the null hypothesis. So the null hypothesis is always assumed to be true until shown to be false with a statistical test. This helps you determine the probability of seeing an effect as big or bigger than that in your study by chance alone if the null hypothesis were true. This is called significance testing.

                 
                Now two other great statisticians – Jerzy Neyman and Egon Pearson – were concerned about the possibility of rejecting a hypothesis that was obviously true. What if the statistical test at hand was NOT being applied correctly? So basically, it would be unreasonable to test whether your data is a certain way (significance testing) unless you assumed that there was other possibilities for your data. This became what is known as the alternative hypothesis. Interestingly, the probability of detecting that alternative hypothesis, if it is true, is called the power of the test. We will talk more about power later in the blog.
                 
                So the power of a statistical test is a measure of how good the test is. The more powerful of two tests is the better one to use. 
                 

                Here is an interesting thought: in many (most?) situations the statistical test you perform for the your study is to test the null hypothesis that no difference in effect exists between groups. In our previous example we were interested in whether gals or guys are associated with whether they like the Naked Gun movies or not in the population of blog readers. If no difference truly existed between gals and guys then why perform the study? The null hypothesis that both gals and guys equally like the Naked gun movies is a “straw man” meant to be knocked down by the results of your study. Therefore, you should always maximize the power of your study in order to knock down the straw man and show a difference exists between gals and guys.

                Ok. Now that we have worked up a sweat knocking down Scarecrow from the Wizard of Oz, cool down listening to Long December (yes, I am happy spring is around the corner) from the Counting Crows and…

                I’ll see you next time in the blogosphere.

                Pascal Tyrrell