My Dominant Hemisphere

Courtesy cayusa@flickr. (creative commons by-nc license)

Just a quick thought. It just occurred to me that some of the questions on the USMLE involving pedigree analysis in genetics, are actually typical decision tree questions. The probability that a certain individual, A, has a given disease (eg: Huntington’s disease) purely by random chance is simply the disease’s prevalence in the general population. But what if you considered the following questions:

• How much genetic code do A and B share if they are third cousins?
• If you suddenly knew that B has Huntington’s disease, what is the new probability for A?
• What is the disease probability for A’s children, given how much genetic code they share with B?

When I’d initially written about decision trees, it did not at all occur to me at the time how this stuff was so familiar to me already!

Apply a little Bayesian strategy to these questions and your mind is suddenly filled with all kinds of probability questions ripe for decision tree analysis:

• If the genetic test I utilize to detect Huntington’s disease has a false-positive rate x and a false-negative rate y, now what is the probability for A?
• If the pre-test likelihood is m and the post-test likelihood is n, now what is the probability for A?

I find it truly amazing how so many geneticists and genetic counselors accomplish such complex calculations using decision trees without even realizing it! Don’t you ?

Copyright © Firas MR. All rights reserved.

Courtesy laurenatclemson @ Flickr (attribution license)

Just thinking aloud a question that’s been ringing in my head recently. Gravity is the weakest force that we know of. In flapping its tiny wings, a fly easily overcomes the gravitational pull of this gigantic earth that we inhabit. A massive airplane can carry hundreds of people on board as it cruises the skies.

But what is it that makes gravity so weak? I think the secret lies in the gravitational constant. What if there’s a force out there whose constant(s) make it so weak that we just haven’t experienced its direct effects yet? A force weaker than gravity?

Could the Higgs field be a candidate for what I’m thinking about?

Copyright © Firas MR. All rights reserved.

Bronze balance pans and lead weights from the Vapheio tholos tomb, circa 15th century BC. National Museum, Athens. Shot courtesy dandiffendale@Flickr. by-nc-sa license.

Objectives and talking points:

• To recap basic concepts of hypothesis testing in scientific experiments. Readers should read-up on hypothesis testing in reference works.
• To contrast drug vs. placebo and drug vs. standard drug study designs.
• To contrast non-equivalence and equivalence studies.
• To understand implications of these study designs, in terms of interpreting study results.

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Howdy readers! Today I’m going to share with you some very interesting concepts from a fabulous book that I finished recently – “Designing Clinical Research – An Epidemiologic Approach” by Stephen Hulley et al. The book speaks fairly early on, on what are called “equivalence studies”. Equivalence studies are truly fascinating. Let’s see how.

When a new drug is tested for efficacy, there are multiple ways for us to do so.

A Non-equivalence Study Of Drug vs. Placebo

A drug can be compared to something that doesn’t have any treatment effect whatsoever – a ‘placebo’. Examples of placebos include sugar tablets, distilled water, inert substances, etc. Because pharmaceutical companies try hard to make drugs that have a treatment effect and that are thus different from placebos, the objective of such a comparison is to answer the following question:

Is the new drug any different from the placebo?

Note the emphasis on ‘any different’. As is usually the case, a study of this kind is designed to test for differences between drug and placebo effects in both directions¹. That is:

Is the new drug better than the placebo?

OR

Is the new drug worse than the placebo?

The boolean operator ‘OR’, is key here.

Since we can not conduct such an experiment on all people in the target ‘population’ (eg. all people with diabetes from the whole country), we conduct it on a random and representative ’sample’ of this population (eg. randomly selected diabetes patients from the whole country). Because of this, we can not directly extrapolate our findings to the target population without doing some fancy roundabout thinking and a lot of voodoo first – a.k.a. ‘hypothesis testing’. Hypothesis testing is crucial to take in to account random chance (error) effects that might have crept in to the experiment.

In this experiment:

• The null hypothesis is that the drug and the placebo DO NOT differ in the real world².
• The alternative hypothesis is that the drug and the placebo DO differ in the real world.

So off we go, with our experiment with an understanding that our results might be influenced by random chance (error) effects. Say that, before we start, we take the following error rates to be acceptable:

1. Even if the null hypothesis is true in the real world, we would find that the drug and the placebo DO NOT differ only 95% of the time, purely by random chance. [Although this rate doesn't have a name, it is equal to (1 - Type 1 error)].
2. Even if the null hypothesis is true in the real world, we would find that the drug and the placebo DO differ 5% of the time, purely by random chance. [This rate is also called our Type 1 error, or critical level of significance, or critical α level, or critical 'p' value].
3. Even if the alternative hypothesis is true in the real world, we would find that the drug and the placebo DO differ only 80% of the time, purely by random chance. [This rate is also called the 'Power' of the experiment. It is equal to (1 - Type 2 error)].
4. Even if the alternative hypothesis is true in the real world, we would find that the drug and the placebo DO NOT differ 20% of the time, purely by random chance. [This rate is also called our Type 2 error].

The strategy of the experiment is this:

If we are able to accept these error rates and show in our experiment that the null hypothesis is false (that is ‘reject‘ it), the only other hypothesis left on the table is the alternative hypothesis. This has then, GOT to be true and we thus ‘accept’ the alternative hypothesis.

Q: With what degree of uncertainty?

A: With the uncertainty that we might arrive at such a conclusion 5% of the time, even if the null hypothesis is true in the real world.

Q: In English please!

A: With the uncertainty that we might arrive at a conclusion that the drug DOES differ from the placebo 5% of the time, even if the drug DOES NOT differ from the placebo in the real world.

Our next question would be:

Q: How do we reject the null hypothesis?

A: We proceed by initially assuming that the null hypothesis is true in the real world (i.e. Drug effect DOES NOT differ from Placebo effect in the real world). We then use a ‘test of statistical significance‘ to calculate the probability of observing a difference in treatment effect in the real world, as large or larger than that actually observed in the experiment.  If this probability is <5%, we reject the null hypothesis. We do this with the belief that such a conclusion is within our pre-selected margin of error. Our pre-selected margin of error, as mentioned previously, is that we would be wrong about rejecting the null hypothesis 5% of the time (our Type 1 error rate)³.

If we fail to show that this calculated probability is <5%, we ‘fail to reject‘ the null hypothesis and conclude that a difference in effect has not been proven⁴.

A lot of scientific literature out there is riddled with drug vs. placebo studies. This kind of thing is good if we do not already have an effective drug for our needs. Usually though, we already have a standard drug that we know works well. It is of more interest to see how a new drug compares to our standard drug.

A Non-equivalence Study Of Drug vs. Standard Drug

These studies are conceptually the same as drug vs. placebo studies and the same reasoning for inference is applied. These studies ask the following question:

Is the new drug any different than the standard drug?

Note the emphasis on ‘any different’. As is often the case, a study of this kind is designed to test the difference between the two drugs in both directions¹. That is:

Is the new drug better than the standard drug?

OR

Is the new drug worse than the standard drug??

Again, the boolean operator ‘OR’, is key here.

In this kind of experiment:

• The null hypothesis is that the new drug and the standard drug DO NOT differ in the real world².
• The alternative hypothesis is that the new drug and the standard drug DO differ in the real world.

Exactly like we discussed before, we initially assume that the null hypothesis is true in the real world (i.e. the new drug’s effect DOES NOT differ from the standard drug’s effect in the real world). We then use a ‘test of statistical significance‘ to calculate the probability of observing a difference in treatment effect in the real world, as large or larger than that actually observed in the experiment.  If this probability is <5%, we reject the null hypothesis – with the belief that such a conclusion is within our pre-selected margin of error. Just to repeat ourselves here, our pre-selected margin of error, is that we would be wrong about rejecting the null hypothesis 5% of the time (our Type 1 error rate)³.

If we fail to show that this calculated probability is <5%, we ‘fail to reject’ the null hypothesis and conclude that a difference in effect has not been proven⁴.

An Equivalence Study Of Drug vs. Standard Drug

Sometimes all you want is a drug that is as good as the standard drug. This can be for various reasons – the standard drug is just too expensive, just too difficult to manufacture, just too difficult to administer, … and so on. Whereas the new drug might not have these undesirable qualities yet retain the same treatment effect.

In an equivalence study, the incentive is to prove that the two drugs are the same. Like we did before, let’s explicitly formulate our two hypotheses:

• The null hypothesis is that the new drug and the standard drug DO NOT differ in the real world².
• The alternative hypothesis is that the new drug and the standard drug DO differ in the real world.

We are mainly interested in proving the null hypothesis. Since this can’t be done⁴, we’ll be content with ‘failing to reject’ the null hypothesis. Our strategy is to design a study powerful enough to detect a difference close to 0 and then ‘fail to reject’ the null hypothesis. In doing so, although we can’t ‘prove’ for sure that the null hypothesis is true, we can nevertheless be more comfortable saying that it in fact is true.

In order to detect a difference close to 0, we have to increase the Power of the study from the usual 80% to something like 95% or higher. We wan’t to maximize power to detect the smallest difference possible. Usually though, it’s enough if we are able to detect the the largest difference that doesn’t have clinical meaning (eg: a difference of 4mm on a BP measurement). This way we can compromise a little on Power and choose a less extreme figure, say 88% or something.

And then just as in our previous examples, we proceed with the assumption that the null hypothesis is true in the real world. We then use a ‘test of statistical significance‘ to calculate the probability of observing a difference in treatment effect in the real world, as large or larger than that actually observed in the experiment.  If this probability is <5%, we reject the null hypothesis – with the belief that such a conclusion is within our pre-selected margin of error. And to repeat ourselves yet again (boy, do we like doing this ), our pre-selected margin of error is that we would be wrong about rejecting the null hypothesis 5% of the time (our Type 1 error rate)³.

If we fail to show that this calculated probability is <5%, we ‘fail to reject‘ the null hypothesis and conclude that although a difference in effect has not been proven, we can be reasonably comfortable saying that there is in fact no difference in effect.

So Where Are The Gotchas?

If your study isn’t designed or conducted properly (eg: without enough power, inadequate  sample size, improper randomization, loss of subjects to followup, inaccurate measurements, etc.)  you might end up ‘failing to reject’ the null hypothesis whereas if you had taken the necessary precautions, this might not have happened and you would have come to the opposite conclusion. Purely because of random chance (error) effects. Such improper study designs usually dampen any obvious differences in treatment effect in the experiment.

In a non-equivalence study, researchers, whose incentive it is to reject the null hypothesis, are thus forced to make sure that their designs are rigorous.

In an equivalence study, this isn’t the case. Since researchers are motivated to ‘fail to reject’ the null hypothesis from the get go, it becomes an easy trap to conduct a study with all kinds of design flaws and very conveniently come to the conclusion that one has ‘failed to reject’ the null hypothesis!

Hence, it is extremely important, more so in equivalence studies than in non-equivalence studies, to have a critical and alert mind during all phases of the experiment. Interpreting an equivalence study published in a journal is hard, because one needs to know the very guts of everything the research team did!

Even though we have discussed these concepts with drugs as an example, you could apply the same reasoning to many other forms of treatment interventions.

Hope you’ve found this post interesting . Do send in your suggestions, corrections and comments!

Adios for now!

Copyright © Firas MR. All rights reserved.

Readability grades for this post:

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1. An alternative hypothesis for such a study is called a ‘two-tailed alternative hypothesis‘. A study that tests for differences in only one direction has an alternative hypothesis that is called a ‘one-tailed alternative hypothesis‘.
2. This situation is a good example of a ‘null’ hypothesis also being a ‘nil’ hypothesis. A null hypothesis is usually a nil hypothesis, but it’s important to realize that this isn’t always the case.
4. Note that we never use the term, ‘accept the null hypothesis’.

The Monty Hall Paradox

One of the 3 doors hides a car. The other two hide a goat each. In search of a new car, the player picks a door, say 1. The game host then opens one of the other doors, say 3, to reveal a goat and offers to let the player pick door 2 instead of door 1. Is there an advantage if the the player decides to switch? (Courtesy: Wikipedia)

Hola amigos! Yes, I’m back! It’s been eons and I’m sure many of you may have been wondering why I was MIA. Let’s just say it was academia as usual.

This post is unique as it’s probably the first where I’ve actually learned something from contributors and feedback. A very critical audience and pure awesome discussion. The main thrust was going to be an analysis of the question, “If you had to pick an answer in an MCQ randomly, does changing your answer alter the probabilities to success?” and it was my hope to use decision trees to attack the question. I first learned about decision trees and decision analysis in Dr. Harvey Motulsky’s great book, “Intuitive Biostatistics“. I do highly recommend his book. As I pondered over the question, I drew a decision tree that I extrapolated from his book. Thanks to initial feedback from BrownSandokan (my venerable computer scientist friend from yore ) and Dr. Motulsky himself, who was so kind as to write back to just a random reader, it turned out that my diagram was wrong and so was the original analysis. The problem with the original tree (that I’m going to maintain for other readers to see and reflect on here) was that the tree in the book is specifically for a math (or rather logic) problem called the Monty Hall Paradox. You can read more about it here. As you can see, the Monty Hall Paradox is a special kind of unequal conditional probability problem, in which knowing something for sure, influences the probabilities of your guesstimates. It’s a very interesting problem, and has bewildered thousands of people, me included. When it was originally circulated in a popular magazine,  “nearly 1000 PhDs” (cf. Wikipedia) wrote back to say that the solution put forth was wrong, prompting numerous psychoanalytical studies to understand human behavior. A decision tree for such a problem is conceptually different from a decision tree for our question and so my original analysis was incorrect.

So what the heck are decision trees anyway? They are basically conceptual tools that help you make the right decisions given a couple of known probabilities. You draw a line to represent a decision, and explicitly label it with a corresponding probability. To find the final probability for a number of decisions (or lines) in sequence, you multiply or add their individual probabilities. It takes skill and a critical mind to build a correct tree, as I learned. But once you have a tree in front of you, its easier to see the whole picture.

Let’s just ignore decision trees completely for the moment and think in the usual sense. How good an idea is it to change an answer on an MCQ exam such as the USMLE? The Kaplan lecture notes will tell you that your chances of being correct are better off if you don’t. Let’s analyze this. If every question has 1 correct option and 4 incorrect options (the total number of options being 5), then any single try on a random choice gives you a probability of 20% for the correct choice and 80% for the incorrect choice. The odds are higher that on any given attempt, you’ll get the answer wrong. If your choice was correct the first time, it still doesn’t change these basic odds. You are still likely to pick the incorrect choice 80% of the time. Borrowing from the concept of “regression towards the mean” (repeated measurements of something, yield values closer to said thing’s mean), we can apply the same reasoning to this problem. Since the outcomes in question are categorical (binomial to be exact), the measure of central tendency used is the Mode (defined as the most commonly or frequently occurring thing in a series). In a categorical series – cat, dog, dog, dog, cat – the mode is ‘dog’. Since the Mode in this case happens to be the category “incorrect”, if you pick a random answer and repeat this multiple times, you are more likely to pick an incorrect answer! See, it all make sense ! It’s not voodoo after all !

Coming back to decision analysis, just as there’s a way to prove the solution to the Monty Hall Paradox using decision trees, there’s also a way to prove our point on the MCQ problem using decision trees. While I study to polish my understanding of decision trees, building them for either of these problems will be a work in progress. And when I’ve figured it all out, I’ll put them up here. A decision tree for the Monty Hall Paradox can be accessed here.

To end this post, I’m going to complicate our main question a little bit and leave it out in the void. What if on your initial attempt you have no idea which of the answers is correct or incorrect but on your second attempt, your mind suddenly focuses on a structure flaw in one or more of the options? Assuming that an option with a structure flaw can’t be correct, wouldn’t this be akin to Monty showing the goat? One possible structure flaw, could be an option that doesn’t make grammatical sense when combined with the stem of the question. Does that mean you should switch? Leave your comments below!

Hope you’ve found this post interesting. Adios for now!

Copyright © Firas MR. All rights reserved.

Readability grades for this post:

Flesch reading ease score:  72.4
Automated readability index: 7.8
Flesch-Kincaid grade level: 7.3
Coleman-Liau index: 8.5
Gunning fog index: 11.4
SMOG index: 10.7

Readings:

Intuitive Biostatistics, by Harvey Motulsky

The Monty Hall Problem: The Remarkable Story Of Math’s Most Contentious Brain Teaser, by Jason Rosenhouse

usmle, decisionanalysis, decisiontree, examination, examinationtechnique, “examination technique”, “decision analysis”, “decision tree’, montyhallproblem, “monty hall problem”

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There’s a book on fluid and electrolyte management that I’ve been reading recently. Called, “Practical Guideline on Fluid Therapy” and authored, as probably evident by the English used in the title, by a very Indian Sanjay Pandya, the book contains many interesting nuggets for day to day practice. Although like most Indian books there is a distinct absence of the emphasis on applying one’s brain, it is nevertheless worth the time to peruse. Today I will be discussing two equations from the book and a question that came up in my mind about the usage of a specific fluid.

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Calculating ECF volume deficit (in dehydration, etc.)

1. If the patient’s previous body weight is known, all you gotta do to obtain ECF deficit is find out the difference between his present and past weight.
2. Another technique uses changes in the Hematocrit to discern ECF volume deficit. This method is applicable only if there is no hemorrhage, hemolysis or other situations involving loss of blood cells, the idea being that any change in blood volume is caused by plasma volume change. So if there’s dehydration and loss of ECF volume, plasma volume shrinks and causes the hematocrit to rise.

ECF Volume Deficit in liters = 0.2 * lean body weight * [(Current hematocrit/Desired hematocrit) - 1]

Can someone figure out the proof for the above equation and post it here? Like most other stuff, I absolutely hate roting math formulas and prefer remembering their derivations. This equation is taking me some time to prove.

To help get started, here are a couple of possible pointers I’m currently exploring:

Total body water (TBW) when expressed as a percentage of Total body weight (TBwt), varies by gender and age. In young adult men for example

TBW = 60% TBwt

TBW in liters

TBwt in kg

Interestingly enough, TBW when expressed as a percentage of lean body weight (LBwt) is a constant and isn’t conditioned upon gender or age.

TBW = 70% LBwt

LBwt = (100/70) * TBW

= (100/70) * [(x/100) * TBwt]

= (x/70) * TBwt

x is the percentage of TBwt that is TBW

Plasma volume is related to blood volume as follows

Plasma volume = Blood volume * [(100 - Hematocrit)/Hematocrit]

Plasma volume is also 1/4 of ECF volume. ECF is 1/3 of TBW. So plasma volume is 1/12 of TBW.

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Calculating Electrolyte Infusion Rates

Change in plasma electrolyte concentration in mEq/L when 1 liter of  infusate is given

= [Infusate electrolyte concentration in mEq/L - Actual electrolyte concentration in mEq/L] / (TBW + 1)

This one’s easy to derive. Taking Na+ as our electrolyte example,

Initial Na+ content = x * TBW

Initial Na+ concentration = (x * TBW)/TBW

Final Na+ content after infusing 1L infusate = (x * TBW) + {y * 1}

Final Na+ concentration = [(x * TBW) + {y}]/(TBW + 1)

Change in Na+ concentration due to infusion = [(x * TBW) + {y}/(TBW + 1)] – [(x * TBW)/TBW]

= (y – x)/(TBW+1)

x = mEq/L of Na+ initially in the body

y = mEq/L of Na+ in the infusate

And voila! There you have it!

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And now for that promised question:

Given the fact that DNS (Dextrose Normal Saline) only stays in the ECF, would it be right to assume that it’s contraindicated in cerebral edema?

The interesting thing is that on exploring the scientific literature, I found that recent research shows that it isn’t just the shifting of fluid into the brain parenchyma that should be avoided when infusing fluid; hyperglycemia is a real danger as well. How hyperglycemia contributes to cerebral edema and especially in situations of cerebral ischemia is a topic of ongoing research and multiple plausible hypotheses are being investigated.

As per Pandya’s book, by the way, it is best to restrict glucose infusion to ≤ 0.5 grams/kg/hour when infusing any glucose containing fluid to avoid complications of hyperglycemia.

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Readability grades for this post:

Kincaid: 11.4
ARI: 12.4
Coleman-Liau: 11.2
Flesch Index: 57.0/100
Fog Index: 14.6
Lix: 46.9 = school year 8
SMOG-Grading: 12.4

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Copyright © Firas MR. All rights reserved.

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Being face to face with writer’s block, I suppose there isn’t anything particularly exciting I feel like writing about for today. I will therefore talk about a couple of things that I’ve been learning from biostatistics and that I feel many of my fellow medics would benefit from.

We all make comparisons between numbers. If ‘A’ weighs 100 kg and ‘B’ weighs 50 kg, we often say A is twice as heavy as B (wt. of A / wt. of B). We can also say A is 50 kg heavier than B (weight of A – weight of B). Is the same true for temperature in Fahrenheit? Is 100F twice as hot as 50F? Well interestingly, no! A temperature of 100F is 50F hotter than a temperature of 100F but not twice as hot. Therein lies a fundamental difference between two different kinds of ‘Dimensional‘ (otherwise called ‘Continuous‘) data:

1. Interval data: a dimensional data set that has values with an equal difference between them. So if numbers denoting Fahrenheit in F are listed as 1, 2, 3, 4, … we clearly know that as we progress from 1 to 2 and then to 3, every subsequent number in that set is separated from its predecessor by an equal interval.
2. Ratio data: a dimensional data set having properties of an Interval data set and, in addition has an absolute zero. Kelvin vs. Fahrenheit is a classic example. Kelvin has an absolute zero while Fahrenheit does not. Weight in kg, too belongs to the class of Ratio data.

The implications of the above dictate how we can manipulate and handle our data. In making comparisons between interval data such as Fahrenheit, we don’t have a universal reference against which two compare two different values – in our example 100F and 50F. The 0F standard is purely arbitrary. If in a fit of mad-hatter rage, we suddenly said that from now on 0F is no longer 0F but 10F, our original values for 100F and 50F now become 110F and 60F. The difference (110-60) remains the same as before (100-50) but the ratio (110/60) changes from the original (100/50). All of this occurs because there isn’t anything stopping you from making a change to your arbitrary 0F standard.

Ratio data sets on the other hand have an absolute standard – the absolute zero. By definition, you can’t change it! This standard is not subject to arbitrary whims and fancies. Taking our Kelvin example, 100K is 50K hotter than a temperature of 50K (100-50). Not only that, it is absolutely fine for you to say 100K is twice as hot as 50K (100/50). Similarly for weight in kilograms, 0kg is absolute. And thus 100kg is 50kg heavier than a weight measurement of 50kg (100-50) and it is also twice as heavy as 50kg (100/50).

The crude analogy is that of a sailor out in the sea. In order to navigate, he could use objects in the ocean such as rocks that could very well change their positions due to climatic conditions (~interval data). Or he could use the Pole Star to help him navigate (~ratio data).

Lessons Learned

You can compare interval data by calculating their difference. No matter what you set as your arbitrary standard, the difference will not change. For ratio data, in addition to calculating differences you also have the luxury of calculating ratios.

A Comedy of Errors

Most people don’t realize this but the IQ score is an example of interval data. A guy scoring 200 on the test did not do twice as good as another who scored 100. He did 100 points better. Standards for a given IQ testing method are set arbitrarily. Not only that, different testing methods could have different arbitrary standards. The WAIS has a different standard than the Stanford-Binet. Remember that.

[In real life, the IQ score isn't truly interval in nature. How is one to assume that there's an equal interval of 'intelligence' between subsequent scores of 100, 101, 102, ... ? It's analogous to cancer staging actually. Stage IV disease is no doubt worse than Stage III disease which in turn is worse than Stage II disease, ... You don't necessarily progress by equal intervals of 'disease-ness' with each subsequent stage from I to IV. Similar to numbers for cancer staging, numbers for IQ scores are actually 'Ordinal' data in disguise.]

Notes:

All data can be divided into the following types (from least informative to most informative):

1. Categorical – Nominal : Distinct categories of data, that you assign names to and that you can’t rank. Eg. Smoker and Non-smoker; Asian, African, American, Australian, etc.
2. Categorical – Ordinal : Distinct categories of data that you can not only assign names to but can also assign ranks. Intervals between ranks aren’t equal. Eg. Gold medal, Silver medal, Bronze medal; Class rank, Cancer Staging, etc. are also examples of ordinal data. The only difference is that they are disguised as numbers.
3. Dimensional – Interval : Numerical data with ranks. Ranks have equal intervals between them. There is no absolute zero.
4. Dimensional – Ratio : Interval data with an absolute zero.

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References

1. Biostatistics – The Bare Essentials (by Geoffrey R. Norman (Author), David L. Streiner)
   
2. Principles of Medical Statistics (by Alvan R. Feinstein)

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Readability grades for this post:-

Kincaid: 6.3
ARI: 5.3
Coleman-Liau: 10.3
Flesch Index: 70.6/100
Fog Index: 9.8
Lix: 33.9 = below school year 5
SMOG-Grading: 9.7
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Copyright © 2006 – 2008 Firas MR. All rights reserved.

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UPDATE, 2nd April 2008: This post is now open-access.

Now that the 2007–08 US Residency Match is over, let’s review some interesting statistics. Preliminary/Transitional programs will not be reviewed in this post. All programs covered are Categorical.

2008 NRMP Match data imply data gathered from the 2007–08 Match session for residency programs beginning in July 2008. FYI, programs and seats are not equivalent. Any given program will typically have >1 seat.

For your reading pleasure, this article has been framed in a Q&A format . Let’s begin right away:

• What specialty do I stand a reasonable chance at?
  • Based on the number of unfilled programs (number in brackets indicates number of unfilled programs as opposed to the total number of unfilled individual seats),
    • Family Medicine (105)
    • Pediatrics (40)
    • Internal Medicine (31)
    • Pathology (27)
    • Psychiatry (27)
    • Emergency Medicine (11)
  • The above specialties also have more seats than the number of US seniors applying for them.
• How is this relevant?
  • Well, typically if there’s a (>=) 1:1 US senior:Seat ratio, that means in order for you to secure a position you will need to displace a US senior. That can happen if the program views you as a superior enough candidate to justify hiring you over a US senior. And this is not all hunky dory for the average IMG Joe.
• What about General Surgery?
  • For a total of 1069 seats, the number of US seniors competing was 1161. That gives us a US senior:Seat ratio = 1.1 . Therefore, seats in Surgery are far fewer than the number of US senior applicants competing for them, let alone non-US-senior applicants, and finding a position is going to be difficult as per the aforementioned Firas’s Law of ‘Displacement’ . Only 2 seats went unfilled for 2008.
• How many applicants competed for Internal Medicine in comparison?
  • The US senior:Seat ratio was 0.6 . Not only that, a significant number of programs went entirely unfilled by any group.
• Has there been a renewed interest in any of the above ‘high yield’ specialties?
  • Emergency Medicine saw an increase in the number of positions being offered, by 10% between 2004 and 2008. The number of US seniors filling EM positions has also increased by the same number during this period.
• Has interest in US residency training or competition increased or decreased?
  • Increased. Overall, there has been a 13.4% increase in the number of Active Applicants (meaning applicants who did not withdraw their applications for some reason or the other) between 2004 and 2008. The increase in Active IMG Applicants (both US citizen and non-US citizen) has been even greater than this number during the same period. This could possibly be due to shrinking opportunities for quality training in other parts of the world.
• Is Internal Medicine really that disliked by US seniors?
  • Match data indicate that IM is still where more US-seniors end up than in any other specialty.
• Is an average, run-of-the-mill non-US citizen IMG more likely to succeed than not?
  • No. Although this will depend on the specific specialty in question. In general, by random chance alone, a non-US citizen IMG is more likely to be unsuccessful. Not only that; success rates have dropped from previous years. The match success rate for Non-US citizen IMGs and US-citizen IMGs for 2008 were 42.4% and 51.9% respectively.

That end’s my wrap-up for the NRMP 2008 data. For more in-depth coverage, the NRMP stats are available on NRMP’s website. Another great resource is Charting Outcomes in the Match: Characteristics of Applicants who Matched to Their Preferred Specialty in the 2007 NRMP Main Residency Match published by the AAMC available for free on its website.

Please feel free to leave behind your comments! They aren’t gonna cost ya anything !

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Copyright © 2006 – 2008 Firas MR. All rights reserved.

(A bouncing ball captured with a stroboscopic flash at 25 images per second: Source)

Hola Amigos!

It indeed has been a very long time since my last entry. This time around, let’s focus on something different. I’d like to talk about medical myths and how they might wreak havoc in your lives. No, I don’t mean the food fads, the herbal remedies your grandma made popular and all that. This article’s meant for the doc in training.

Consider this question:-

What antibody response is typical of Extrinsic Allergic Alveolitis?

In case you hadn’t read about this disorder’s immunology specifically, your survival instincts would have immediately led you to say IgE. Given the utterly memory-oriented way our brain functions in modern medical study, it immediately homes onto specific keywords in the question. Allergy and IgE go hand in hand don’t they? Well, not in this case they don’t. The correct answer here happens to be IgG.¹ Examinations such as the USMLE will test you with these odd-balls and boy do they constitute a bunch! Regardless of the ethical issues concerning what should or shouldn’t be tested in medical examinations, knowing the right responses to such questions and indexing them to one’s already burdened memory can indeed make or break one’s career.

There’s another oddity that you’d be interested to know about Extrinsic Allergic Alveolitis. This is one lung disorder that has a lower incidence rate among smokers!²

That’s it for now. Hope you’ve found this post interesting. Your comments are welcome as always. Until my next post, Ciao!

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References:-

1. Current Pulmonary, Chapter 32. Hypersensitivity Pneumonitis, Clinical Findings; by Cecile Rose MD MPH
2. Current Pulmonary, Chapter 32. Hypersensitivity Pneumonitis, Pathogenesis; by Cecile Rose MD MPH

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“Millions saw the apple fall, but Newton was the one who ASKED WHY.”
~ Bernard Mannes Baruch
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Copyright © 2006 – 2008 Firas MR. All rights reserved.

Hi! Here’s a quick question. This exercise requires you to say the first thing that comes to your mind. Answer in a jiffy if you want to be honest with yourself! :

Q. What’s the value for normal blood pressure in an adult?

What? 120/80 mm Hg? Congratulations, you’re so wrong! Wait a minute, hold your horses! Read the rest of this entry »

Source

Explain the following dichotomy &/or paradox :-

Q. Why is it that while blood donations between family members (in other words, those individuals who share HLA-haplotypes) are discouraged, bone-marrow/organ transplantations are encouraged between them?

Background keywords :-

• blood (a type of connective tissue) – blood relative – HLA haplotype sharing – donor lymphocyte attack on host cells – transfusion associated graft versus host disease (TAGVHD) – blood donation from family donors contraindicated unless products irradiated
• organ donation – allogeneic bone marrow transplantation – closely matched HLA donor – family donor – host versus graft reaction or graft rejection – acute & chronic graft versus host disease (GVHD) – leukemias – graft versus leukemia effect – donor lymphocyte infusion – reduced intensity bone marrow transplantation

Apparently, GVHD is not so much of a problem in bone-marrow/organ transplantation when compared to blood transfusion. In fact, the graft versus leukemia effect (a type of GVHD), coupled with donor lymphocyte infusion, is used to our advantage in ‘reduced intensity bone marrow transplantation’ for the treatment of leukemias. Why the difference?

Background books:-

• Davidson’s Principles & Practice of Medicine 20 ed
  • Part 2 PRACTICE OF MEDICINE > 24 Blood disorders > BLOOD PRODUCTS AND TRANSFUSION > ADVERSE EFFECTS OF TRANSFUSION
  • Part 2 PRACTICE OF MEDICINE > 24 Blood disorders > HAEMATOLOGICAL MALIGNANCIES > LEUKAEMIAS
• Harrison’s Principles of Internal Medicine 16 ed

——
“Millions saw the apple fall, but Newton was the one who ASKED WHY.”
~ Bernard Mannes Baruch
-

Copyright © Firas MR. All rights reserved.