## Mr Reliable

 Kevin Durant is Mr Reliable

Being reliable is an important and sought after trait in life. Kevin Durant has proven himself to be just that to the NBA. Would you agree (pun intended)? So, we have been talking about agreement lately and we have covered precision (see Repeat After Me) and accuracy (see Men in Tights). Today let’s talk a little about reliability.

As I mentioned last time, the concepts of accuracy and precision originated in the physical sciences because direct measurements are possible. Not to be outdone, the social sciences (and later in the Medical Sciences) decided to define their own terms of agreement – validity and reliability.

So the concept of reliability was developed to reflect the amount of error, both random and systematic, in any given measurement. For example if you were to want to assess the the measurement error in repeated measurements on the same subject under identical conditions or to measure the consistency of two readings obtained by two different readers on the same subject under identical conditions.

The reliability coefficient is simply the ratio of variability between subjects to the total variability (sum of subject variability and measurement error). A coefficient of 0 indicates no reliability and 1 indicates perfect reliability with no measurement error.

Being Mr Reliable (see the trailer to this cool old movie from the sixties) is always desirable but keep in mind that when you consider reliability remember that:

1- A true score exists but is not directly measurable (philosophical…)

2- A measurement is always the sum of the true score and a random error.

3- Any two measurements for the same subject are parallel measurements in that they are assumed to have the same mean and variance.

With these assumptions in place, reliability can be also expressed as the correlation between any two measurements on the same subject – AKA the intraclass correlation coefficient or ICC (originally defined by Sir Francis Galton and later further developed by Pearson and Fisher). We will talk about the ICC in a later post.

Phew! That was a mouthful. All this talk of reliability is exhausting. Maybe Lean on me (or Bill Withers, actually) for a bit and we will talk about validity when we come back…

See you in the blogosphere,

Pascal Tyrrell

## Agreement Is Difficult: So What Are We Gonna Do? I Dunno, What You Wanna Do?

It is never easy to come to an agreement – even amongst friends! The Vultures from Disney’s The Jungle Book (oldie but a goodie) certainly know this. In medical research measuring agreement is also a challenge. In this series of posts I am going to talk about agreement and how it is measured.

Agreement measures the “closeness” between things. It is a broad term that contains both “accuracy” and “precision”. So, let’s say you are shopping for screen protectors for your wonderful new phone. You head to the internet and start going through the gazillion links advertising screen protectors of all sizes and styles. As you just spent your savings on the phone, you do not have much money left over for the screen protector. You decide on a generic brand and order a pack of 10. After an unbearable wait of a week to receive them in the mail you open the pack and find that even though you ordered the screen protector to fit your specific phone they are a little small… except for two that fit perfectly! What? That’s annoying.

So, how close are the screen protectors to being “true” to the expected product? This is agreement. Now, most of them are a little small. This represents poor “accuracy”. This is because there exists a systematic bias. If you took the mean size of these 10 protectors you would find that it deviated from the true expected value – size in this case. Furthermore, you found that two of the 10 protectors actually fit your phone screen rather well. This is great, but this inconsistency between your protectors represents poor “precision”. This time we are interested in the degree of scatter between your measurements – a measure of within sample variation due to random error (see my Dickens post for more info).

Now the concepts of accuracy and precision originated in the physical sciences where direct measurements are possible. Not to be outdone, the social sciences (and then soon to adopt medical sciences!) decided to establish similar concepts – validity and reliability. We will discuss these in a latter post but for now simply remember that the main differences are that a reference is required for validity and that both validity and reliability are most often assessed with scaled indices.

Phew! That was a little confusing. Have a listen to We Just Disagree from Dave Mason to relax.

Next post we will look a little more closely at two special kinds of precision – repeatability and reproducibility.

See you in the blogoshere,

Pascal Tyrrell

## It’s All Relevant According to Einstein… or Was It Relative?

One of Einstein‘s many theories is that a light beam always appears to have the same speed, no matter how fast you are moving relative to it. This theory is also one of the foundations of Einstein’s special theory of relativity. So why the Mini Einstein Bobble Head? Because of the Night at the Museum 2 movie, of course! Have a peek at the trailer and come back.

OK, the last letter in our F.I.N.E.R. mnemonic – a convenient way to remember what makes a good research question – is R. We covered E for Ethical last time and today we will go over R for Relevant – not relative (pay attention now!).

So, you are now a junior researcher with a newly minted pocked protector and have decided to step back a minute and assess your research question using F.I.N.E.R. Among the 5 characteristics we have discussed this last one is an important one. Let’s go back to F is for Feasible where we were thinking of a way to survey your friend’s about going camping at the end of the semester to celebrate the start of summer. The results of your survey will provide you with important information. Not only will they influence your decision to have the event or not, but they will also allow for promotion of the event as being “really popular” (important to many participants) and for better planning (important to the organizing committee). The results of the survey are “relevant”.

Make sure the results of your study will contribute to research knowledge and influence change in your field. Maybe a mentor can help shed some light on it if you are unsure. The broader the relevance of your results the better. This touches on “knowledge translation” and we will chat about that later on in our blog.

That’s it for FINER! Practice it a little this week-end and let me know how it goes.

See you in the blogosphere,

Pascal Tyrrell

## A Little Respect To Avoid The Blues… Brothers?

In her famous song Respect (have a listen while we chat), Aretha Franklin provides us with a perfect segue to the next letter in our F.I.N.E.R. mnemonic – a convenient way to remember what makes a good research question. We covered N for Novel last time and today we will go over E for Ethical.

Everyone has an idea of what they consider to be “right”. Ethics is a branch of philosophy that focuses on studying what is morally right and wrong in our society. Setting standards to live by that will be in everyone’s best interest. More importantly, ethics also involves the continuous effort of studying our own moral beliefs and our moral conduct, and striving to ensure that we live up to these standards that are reasonable and solidly-based.

Now The Blues Brothers may not have operated with completely ethical methods but you should when you perform research. Wondering what the Blues Brothers have to do with any of this? Watch the trailer and you will see Aretha singing “R-E-S-P-E-C-T”. She wants to be treated “right” just as your subjects will in your study.

When you think of your research question be sure to consider whether your study will pose unnecessary physical or emotional harm to your subjects – human or otherwise. If you are unsure then you should discuss your question with someone from your institution’s ethics review committee.

But what if you are doing research on your own for fun and are not sure? Ask Mom, she’ll know.

Next time we will chat about R for Relevant

See you in the blogosphere,

Pascal Tyrrell

## New Gold Dream: Is It that Simple?

What a great album from Simple Minds. Ahhh, the 80’s. Their title track New Gold Dream should get you in the mood for the next letter in our F.I.N.E.R. mnemonic – a convenient way to remember what makes a good research question. We covered I for Interesting last time and today we will go over N for Novel

Your pocket protector in place and armed with an interesting research question that you think is feasible, you are now stuck wondering if the research path you are about to take will satisfy the next criteria: is it novel?

The whole idea behind research is to contribute new information.

No need to
reinvent the wheel. You want to save your precious energy and time for answering a question that will move you forward in your area of science.

So, how do you know it is novel? Here are a few suggestions you can try:

1- Review the scientific literature. And then review it again. Not sure how to get started? Talk to a librarian at your institution.

2- Get out there and talk to people about your research idea. Experts in the field will be happy to chat (most of the time anyway!) and may give you insider knowledge about the area of research. How about having lunch with friends/colleagues and ask them what they think. I did exactly that yesterday (shout out to Sindhu Johnson!) and what did I find out? That my ideas were well received and that there is at least one person who thinks I am on the right track. Perfect. Who’s next?

Now though you want your question to be as original as possible don’t throw out the baby with the bath water. Often building on previous work or simply confirming it can be important. For instance assessing whether findings in one population also applies to others. This is often the case in pediatric research.

Having the new gold dream is always a great way to start. Just keep in mind what makes a good research question. Next time we take on ethics…

See you in the blogosphere,

Pascal Tyrrell

## Gwen Stefani Has No Doubt… Do You?

So, in my last post I introduced F.I.N.E.R. as a convenient way to remember what makes a good research question. We covered F for feasible and today we will go over I for Interesting

What is important when dreaming up a research question is to make sure that you are interested and engaged. This is what will provide you with the energy, drive, and determination to overcome the many hurdles and frustrations that will invariably stand in you way on your path during the research process.

Gwen may have No Doubt about what she is interested in. But do you? How will you gauge how interesting your question is? Easy – talk to people about it. One of the problems new researchers have with their research questions is that they Don’t Speak (great song!) with others during the planning process. Ask as many mentors, experts, family members, friends, colleagues as you can about your question. All that feedback will help you determine whether it is worth your precious time and effort to pursue that research.

Don’t be shy to ask people their opinion and don’t be take it personally if you get negative feedback. It is all part of the process. You can’t expect to have everyone interested but you can certainly try your best to have many.

Try early on in your research career to find a Person of Interest (well maybe not that kind of person) or someone who you value their opinion and are friendly with to act as a sounding board to your ideas before you move on outside the “inner-circle”. You can even repay the favor to them for their research endeavors. Hint: choose wisely…

Next is N…

See you in the blogosphere,

Pascal Tyrrell

## 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.
Brandon Teteruck

## 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

## 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

## 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.