I have come here to talk about my book

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Not yet three months ago, 27 years have passed since one of the most anthological moments in the entire history of television in our country. At the end of March 1993, the writer Francisco Umbral was invited to participate in a television program presented and hosted by Mercedes Milá who, apparently, had convinced him to attend with the bait to talk about the last book he had just published.

The problem is that poor Umbral found himself at a table with two other people who, following the thread of the program, were talking about everything except his book, with the apparent complicity of the presenter and the enthusiastic cooperation of the audience on the set.

And what had to happen happened. Time was running, the program was going to end and there was no talk about the book, so Umbral, demonstrating other lesser-known qualities than his genius as a novelist and journalist, exploded demanding that his book be talked about, that it was the reason why he had come to TV, repeatedly saying the phrase that has remained forever in the Spanish cultural heritage: “I have come here to talk about my book.”

The regular readers of the blog are accustomed to seeing that the posts usually begin with some delusion of my imagination that ends up giving way to the real topic of the day, which has nothing to do with what was spoken at the beginning of the post, so you will already be wondering what today’s post will be about.

But today you are going to get a surprise. There is no topic on evidence-based medicine. Because today, I have come here to talk about my book.

The blog “Ciencia sin seso… locura doble” was born on July 26, 2012, with the ambitious purpose of teaching topics of research methodology and evidence-based medicine in ways that seem easy and even fun. Since then, about 160 posts have been published in two languages ​​(in Spanish and in something that wants to resemble the language of the Bard from Avon) and it has grown in audience and diversity of topics, although the most important milestone from the point of view of its dissemination and prestige was its inclusion in AnestesiaR web in May 2014.

It was time, then, to bring into being to at least part of the contents so that they formed a coherent and homogeneous set. And this is how “El ovillo y la espada” (“The ball and the sword”) is born, the book I have come to talk about here today.

You can see that I continue with my hobby of giving it a title that has nothing to do with the content of the work. In reality, “The ball and the sword” is a “Manual for critical appraisal of scientific documents”, made up of a selection of blog’s posts that, grouped together, are intended to provide the reader with the necessary knowledge to face the critical appraisal of the articles that we have to resort to daily in our professional practice.

The manual is made up of a series of blocks that deal with the usual steps that make up evidence-based medicine systematics: the generation of the structured clinical question in the face of a knowledge gap, the bibliographic search, the characteristics of the most common epidemiological designs and the guidelines for critical appraising the papers based on these designs.

To finish, I wish only thank my colleagues and friends from the Evidence-Based Pediatrics Committee of the AEP-AEPap and from AnestesiaR. With the first ones I have learned everything I know about these topics (do not think it is much just because I write a book) and thanks to the second ones the blog has reached a diffusion that was beyond my possibilities, in addition to making the project that I am presenting you today. My book, in case someone hasn’t found out yet.

And with this we are leaving. I hope you are encouraged to read my creature and that it be useful to you. We get to the end of this post without explaining what the balls and swords are in the title of the manual. I will tell you that it has something to do with a certain Theseus. But that’s another story…

The shortest distance

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The least squared method

The other day I was trying to measure the distance between Madrid and New York in Google Earth and I found something unexpected: when I tried to draw a straight line between the two cities, it twisted and formed an arc, and there was no way to avoid it.

I wondered if what Euclid said about the straight line being the shortest path between two points would not be true. Of course, right away, I realized where the error was: Euclid was thinking about the distance between two points located in a plane and I was drawing the minimum distance between two points located in a sphere. Obviously, in this case the shortest distance is not marked by a straight line, but an arc, as Google showed me.

And since one thing leads to another, this led me to think about what would happen if instead of two points there were many more. This has to do, as some of you already imagine, with the regression line that is calculated to fit a point cloud. Here, as it is easy to understand, the line cannot pass through all the points without losing its straightness, so the statisticians devised a way to calculate the line that is closest to all points on average. The method they use the most is the one they call the least squares method, whose name suggests something strange and esoteric. However, the reasoning for calculating it is much simpler and, therefore, no less ingenious. Let’s see it.

The least squared method

The linear regression model makes it possible, once a linear relation has been established, to make predictions about the value of a variable Y knowing the values of a set of variables X1, X2, … Xn. We call the variable Y as dependent, although it is also known as objective, endogenous, criterion or explained variable. For their part, the X variables are the independent variables, also known as predictors, explanatory, exogenous or regressors.

When there are several independent variables, we are faced with a multiple linear regression model, while when there is only one, we will talk about simple linear regression. To make it easier, we will focus, of course, on the simple regression, although the reasoning also applies to multiple one.

As we have already said, linear regression requires that the relationship between the two variables is linear, so it can be represented by the following equation of a straight line:

Regression line

Here we find two new friends accompanying our dependent and independent variables: they are the coefficients of the regression model. β0 represents the model constant (also called the intercept) and is the point where the line intersects the ordinate axis (the Y axis, to understand each other better). It would represent the theoretical value of variable Y when variable X values zero.

For its part, β1 represents the slope (inclination) of the regression line. This coefficient tells us the increment of units of variable Y that occurs for each increment of one unit of variable X.

We meet chance again

This would be the general theoretical line of the model. The problem is that the distribution of values is never going to fit perfectly to any line, so when we are going to calculate a determined value of Y (yi) from a value of X (xi) there will be a difference between the real value of yi and the one that we obtain with the formula of the line. We have already met with random, our inseparable companion, so we will have no choice but to include it in the equation:

Regression line with random component

Although it seems a similar formula to the previous one, it has undergone a profound transformation. We now have two well-differentiated components, a deterministic and a stochastic (error) component. The deterministic component is marked by the first two elements of the equation, while the stochastic is marked by the error in the estimation. The two components are characterized by their random variable, xi and εi, respectively, while xi would be a specific and known value of the variable X.

Let’s focus a little on the value of εi. We have already said that it represents the difference between the real value of yi in our point cloud and that which would be provided by the equation of the line (the estimated value, represented as ŷi). We can represent it mathematically in the following way:

Calculation of a residual

This value is known as the residual and its value depends on chance, although if the model is not well specified, other factors may also systematically influence it, but that does not change what we are dealing with.

Let’s summarize

Let’s recap what we have so far:

  1. A point cloud on which we want to draw the line that best fits the cloud.
  2. An infinite number of possible lines, from which we want to select a specific one.
  3. A general model with two components: one deterministic and the other stochastic. This second will depend, if the model is correct, on chance.

We already have the values of the variables X and Y in our point cloud for which we want to calculate the line. What will vary in the equation of the line that we select will be the coefficients of the model, β0 and β1. And what coefficients interest us? Logically, those with which the random component of the equation (the error) is as small as possible. In other words, we want the equation with a value of the sum of residuals as low as possible.

Starting from the previous equation of each residual, we can represent the sum of residuals as follows, where n is the number of pairs of values of X and Y that we have:

Sum of residuals

But this formula does not work for us. If the difference between the estimated value and the real value is random, sometimes it will be positive and sometimes negative. Furthermore, its average will be zero or close to zero. For this reason, as on other occasions in which it is interesting to measure the magnitude of the deviation, we have to resort to a method that prevents from negatives differences canceling out with the positives ones, so we calculate these squared differences, according to the following formula:

Sum of squared residuals

We already got it!

At last! We already know where the least squares method comes from: we look for the regression line that gives us the smallest possible value of the sum of the squares of the residuals. To calculate the coefficients of the regression line we will only have to expand the previous equation a little, substituting the estimated value of Y for the terms of the regression line equation:

Sum of squared residuals

and find the values of b0 and b1 that minimize the function. From here the task is a piece of cake, we just have to set the partial derivatives of the previous equation to zero (take it easy, let’s save the hard-mathematical jargon) to get the value of b1:

Calculation of the slope

Where we have in the numerator the covariance of the two variables and, in the denominator, the variance of the independent variable. From here, the calculation of b0 is a breeze:

Calculation of the intercept

We can now build our line that, if you look closely, goes through the mean values of X and Y.

A practical exercise

And with this we end the arduous part of this post. Everything we have said is to understand what the least squares mean and where the matter comes from, but it is not necessary to do all this to calculate the linear regression line. Statistical packages do it in the blink of an eye.

Script to get the lineal regression model with RFor example, in R it is calculated using the function lm(), which stands for linear model. Let’s see an example using the “trees” database (girth, volume and height of 31 observations on trees), calculating the regression line to estimate the volume of the trees knowing their height:

modelo_reg <- lm(Height~Volume, data = trees)

summary(modelo_reg)

The lm() function returns the model to the variable that we have indicated (reg_model, in this case), which we can exploit later, for example, with the summary() function. This will provide us with a series of data, as you can see in the attached figure.

First, the quartiles and the median of the residuals. For the model to be correct, it is important that the median is close to zero and that the absolute values of the residuals are distributed evenly among the quartiles (similar between maximum and minimum and between first and third quartiles).

Next, the point estimate of the coefficients is shown below along with their standard error, which will allow us to calculate their confidence intervals. This is accompanied by the values of the t statistic with its statistical significance. We have not said it, but the coefficients follow a Student’s t distribution with n-2 degrees of freedom, which allows us to know if they are statistically significant.

Finally, the standard deviation of the residuals is provided, the square of the multiple correlation coefficient or determination coefficient (the precision with which the line represents the functional relationship between the two variables; its square root in simple regression is the Pearson’s correlation coefficient), its adjusted value (which will be more reliable when we calculate regression models with small samples) and the F contrast to validate the model (the variance ratios follow a Snedecor’s F distribution).

Thus, our regression equation would be as follows:Graphical representation of point cloud and regression line

Height = 69 + 0.23xVolume

We could already calculate how tall a tree could be given a specific volume that was not in our sample (although it should be within the range of data used to calculate the regression line, since it is risky to make predictions outside this range).

Also, with the scatterplot(Volume ~ Height, regLine = TRUE, smooth = FALSE, boxplots = FALSE, data = trees) command, we could draw the point cloud and the regression line, as you can see in the second figure.

And we could calculate many more parameters related to the regression model calculated by R, but we will leave it here for today.

We’re leaving…

Before finishing, just to tell you that the least squares method is not the only one that allows us to calculate the regression line that best fits our point cloud. There is also another method that is that of the maximum likelihood, which gives more importance to the choice of the coefficients that with more compatibility with the observed values. But that is another story…

Rioja vs Ribera

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Frequentist vs Bayesian statistics

This is one of the typical debates that one can have with a brother-in-law during a family dinner: whether the wine from Ribera is better than that from Rioja, or vice versa. In the end, as always, the brother-in-law will be (or will want to be) right, which will not prevent us from trying to contradict him. Of course, we must make good arguments to avoid falling into the same error, in my humble opinion, in which some fall when participating in another classic debate, this one from the less playful field of epidemiology: Frequentist vs. Bayesian statistics?

And these are the two approaches that we can use when dealing with a research problem.

Some previous definitions

Frequentist statistics, the best known and to which we are most accustomed, is the one that is developed according to the classic concepts of probability and hypothesis testing. Thus, it is about reaching a conclusion based on the level of statistical significance and the acceptance or rejection of a working hypothesis, always within the framework of the study being carried out. This methodology forces to stabilize the decision parameters a priori, which avoids subjectivities regarding them.

The other approach to solving problems is that of Bayesian statistics, which is increasingly fashionable and, as its name suggests, is based on the probabilistic concept of Bayes’ theorem. Its differentiating feature is that it incorporates external information into the study that is being carried out, so that the probability of a certain event can be modified by the previous information that we have on the event in question. Thus, the information obtained a priori is used to establish an a posteriori probability that allows us to make the inference and reach a conclusion about the problem we are studying.

This is another difference between the two approaches: while Frequentist statistics avoids subjectivity, Bayesian’s one introduces a subjective (but not capricious) definition of probability, based on the researcher’s conviction, to make judgments about a hypothesis.

Bayesian statistics is not really new. Thomas Bayes’ theory of probability was published in 1763, but experiences a resurgence from the last third of the last century. And as usually happens in these cases where there are two alternatives, supporters and detractors of both methods appear, which are deeply involved in the fight to demonstrate the benefits of their preferred method, sometimes looking more for the weaknesses of the opposite than for their own strengths.

And this is what we are going to talk about in this post, about some arguments that Bayesians use on some occasion that, one more time in my humble opinion, take more advantage misuses of Frequentist statistics by many authors, than of intrinsic defects of this methodology.

A bit of history

We will start with a bit of history.

The history of hypothesis testing begins back in the 20s of the last century, when the great Ronald Fisher proposed to value the working hypothesis (of absence of effect) through a specific observation and the probability of observing a value equal or greater than the observed result. This probability is the p-value, so sacred and so misinterpreted, that it does not mean more than that: the probability of finding a value equal to or more extreme than that found if the working hypothesis were true.

In summary, the p that Fisher proposed is nothing short of a measure of the discrepancy that could exist between the data found and the hypothesis of work proposed, the null hypothesis (H0).

Almost a decade later, the concept of alternative hypothesis (H1) was introduced, which did not exist in Fisher’s original approach, and the reasoning is modified based on two error rates of false positive and negative:

  1. Alpha error (type 1 error): probability of rejecting the null hypothesis when, in fact, it is true. It would be the false positive: we believe we detect an effect that, in reality, does not exist.
  2. Beta error (type 2 error): it is the probability of accepting the null hypothesis when, in fact, it is false. It is the false negative: we fail to detect an effect that actually exists.

Thus, we set a maximum value for what seems to us the worst case scenario, which is detecting a false effect, and we choose a “small” value. How small is it? Well, by convention, 0.05 (sometimes 0.01). But, I repeat, it is a value chosen by agreement (and there are those who say that it is capricious, because 5% reminds them the fingers of the hand, which are usually 5).

Thus, if p <0.05, we reject H0 in favor of H1. Otherwise, we accept H0, the hypothesis of no effect. It is important to note that we can only reject H0, never demonstrate it in a positive way. We can demonstrate the effect, but not its absence.

Everything said so far seems easy to understand: the frequentist method tries to quantify the level of uncertainty of our estimate to try to draw a conclusion from the results. The problem is that p, which is nothing more than a way to quantify this uncertainty, is sacralized and misinterpreted too often, which is used to their advantage (if I may say so) by opponents of the method to try to expose its weaknesses.

One of the major flaws attributed to the frequentist method is the dependence of the p-value on the sample size. Indeed, the value of p can be the same with a small effect size in a large sample as with a large effect size in a small sample. And this is more important than it may seem at first, since the value that will allow us to reach a conclusion will depend on a decision exogenous to the problem we are examining: the chosen sample size.

Here would be the benefit of the Bayesian method, in which larger samples would serve to provide more and more information about the study phenomenon. But I think this argument is based on a misunderstanding of what an adequate sample is. I am convinced, the more is not always the better.

We start with the debate

Another great man, David Sackett, said that “too small samples can be used to prove nothing; samples that are too large can be used to prove nothing ”. The problem is that, in my opinion, a sample is neither large nor small, but sufficient or insufficient to demonstrate the existence (or not) of an effect size that is considered clinically important.

And this is the heart of the matter. When we want to study the effect of an intervention we must, a priori, define what effect size we want to detect and calculate the necessary sample size to be able to do it, as long as the effect exists (something that we desire when we plan the experiment, but that we don’t know a priori) . When we do a clinical trial we are spending time and money, in addition to subjecting participants to potential risk, so it is important to include only those necessary to try to prove the clinically important effect. Including the necessary participants to reach the desired p <0.05, in addition to being uneconomic and unethical, demonstrates a lack of knowledge about the true meaning of p-value and sample size.

This misinterpretation of the p-value is also the reason that many authors who do not reach the desired statistical significance allow themselves to affirm that with a larger sample size they would have achieved it. And they are right, they would have reached the desired p <0.05, but they again ignore the importance of clinical significance versus statistical significance.

When the sample size to detect the clinically important effect is calculated a priori, the power of the study is also calculated, which is the probability of detecting the effect if it actually exists. If the power is greater than 80-90%, the values admitted by convention, it does not seem correct to say that you do not have enough sample. And, of course, if you have not calculated the power of the study before, you should do it before affirming that you have no results due to shortness of sample.

Another argument against the frequentist method and in favor of the Bayesian’s says that hypothesis testing is a dichotomous decision process, in which a hypothesis is rejected or accepted such as you rejects or accepts an invitation to the wedding of a distant cousin you haven’t seen for years.

Well, if they previously forgot about clinical significance, those who affirm this fact forget about our beloved confidence intervals. The results of a study should not be interpreted solely on the basis of the p-value. We must look at the confidence intervals, which inform us of the precision of the result and of the possible values that the observed effect may have and that we cannot further specify due to the effect of chance. As we saw in a previous post, the analysis of the confidence intervals can give us clinically important information, sometimes, although the p is not statistically significant.

More arguments

Finally, some detractors of the frequentist method say that the hypothesis test makes decisions without considering information external to the experiment. Again, a misinterpretation of the value of p.

As we already said in a previous post, a value of p <0.05 does not mean that H0 is false, nor that the study is more reliable, or that the result is important (even though the p has six zeros). But, most importantly for what we are discussing now, it is false that the value of p represents the probability that H0 is false (the probability that the effect is real).

Once our results allow us to affirm, with a small margin of error, that the detected effect is real and not random (in other words, when the p is statistically significant), we can calculate the probability that the effect is “real”. And for this, Oh, surprise! we will have to calibrate the value of p with the value of the basal probability of H0, which will be assigned by the researcher based on her knowledge or previous available data (which is still a Bayesian approach).

As you can see, the assessment of the credibility or likelihood of the hypothesis, one of the differentiating characteristics of the Bayesian’s approach, can also be used if we use frequentist methods.

We’re leaving…

And here we are going to leave it for today. But before finishing I would like to make a couple of considerations.

First, in Spain we have many great wines throughout our geography, not just Ribera or Rioja. For no one to get offended, I have chosen these two because they are usually the ones asked by the brothers-in-law when they come to have dinner at home.

Second, do not misunderstand me if it may have seemed to you that I am an advocate of frequentist statistics against Bayesian’s. Just as when I go to the supermarket I feel happy to be able to buy wine from various designations of origin, in research methodology I find it very good to have different ways of approaching a problem. If I want to know if my team is going to win a match, it doesn’t seem very practical to repeat the match 200 times to see what average results come out. It  would be better to try to make an inference taking into account the previous results.

And that’s all. We have not gone into depth in what we have commented at the end on the real probability of the effect, somehow mixing both approaches, frequentist’s and Bayesian’s. The easiest way, as we saw in a previous post, is to use a Held’s nomogram. But that is another story…

A weakness

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Even the greatest have weaknesses. It is a reality that affects even the great NNT, the number needed to treat, without a doubt the king of the measures of absolute impact of the research methodology in clinical trials.

Of course, that is not an irreparable disgrace. We only have to be well aware of its strengths and weaknesses in order to take advantage of the former and try to mitigate and control the latter. And it is that the NNT depends on the baseline risks of the intervention and control groups, which can be inconsistent fellow travelers and be subjected to variation due to several factors.

As we all know, NNT is an absolute measure of effect that is used to estimate the efficacy or safety of an intervention. This parameter, just like a good marriage, is useful in good times and in bad, in sickness and in health.

Thus, on the good side we talk about NNT, which is the number of patients that have to be treated for one to present a result that we consider as good. By the way, on the dark side we have the number needed to harm (NNH), which indicates how many we have to treat in order for one to present an adverse event.

NNT was originally designed to describe the effect of the intervention relative to the control group in clinical trials, but its use was later extended to interpret the results of systematic reviews and meta-analyzes. And this is where the problem may arise since, sometimes, the way to calculate it in trials is generalized for meta-analyzes, which can lead to error.

The simplest way to obtain the NNT is to calculate the inverse of the absolute risk reduction between the intervention and the control group. The problem is that this form is the one that is most likely to be biased by the presence of factors that can influence the value of the NNT. Although it is the king of absolute measures of impact, it also has its limitations, with various factors influencing its magnitude, not to mention its clinical significance.

One of these factors is the duration of the study follow-up period. This duration can influence the number of events, good or bad ones, that the study participants can present, which makes it incorrect to compare the NNTs of studies with follow-ups of different duration.

Another may be the baseline risk of presenting the event. Let’s think that the term “risk”, from a statistical point of view, does not always imply something bad. We can speak, for example, of risk of cure. If the baseline risk is higher, more events will likely occur and the NNT may be lower. The outcome variable used and the treatment alternative with which we compared the intervention should also be taken into account.

And third, to name a few more of these factors, the direction and size of the effect, the scale of measurement, and the precision of the NNT estimates, their confidence intervals, may influence its value.

And here the problem arises with systematic reviews and meta-analyzes. Even though we might want to, there will always be some heterogeneity among the primary studies in the review, so these factors we have discussed may differ among studies. At this point, it is easy to understand that the estimation of the global NNT based on the summary measures of risks between the two groups may not be the most suitable, since it is highly influenced by the variations in the baseline control event rate (CER).

NNT calculation from RR and OR

For these situations, it is much more advisable to make other more robust estimates of the NNT, the most widely used being those that use other association measures such as the risk ratio (RR) or the odds ratio (OR), which are more robust in the face of variations in CER. In the attached figure I show you the formulas for the calculation of the NNT using the different measures of association and effect.

In any case, we must not lose sight of the recommendation of not to carry out a quantitative synthesis or calculation of summary measures if there is significant heterogeneity among primary studies, since then the global estimates will be unreliable, whatever we do.

But do not think that we have solved the problem. We cannot finish this post without mentioning that these alternative methods for calculating NNT also have their weaknesses. Those have to do with obtaining an overall CER summary value, which also varies among primary studies.

The simplest way would be to divide the sum of events in the control groups of the primary studies by the total number of participants in that group. This is usually possible simply by taking the data from the meta-analysis’ forest plot. However, this method is not recommended, as it completely ignores the variability among studies and possible differences in randomization.

Another more correct way would be to calculate the mean or median of the CER of all the primary studies and, even better, to calculate some weighted measure based on the variability of each study.

And even, if baseline risk variations among studies are very important, an estimate based on the investigator’s knowledge or other studies could be used, as well as using a range of possible CER values and comparing the differences among the different NNTs that could be obtained.

You have to be very careful with the variance weighting methods of the studies, since the CER has the bad habit of not following a normal distribution, but a binomial one. The problem with the binomial distribution is that its variance depends greatly on the mean of the distribution, being maximum in mean values around 0.5.

On the contrary, the variance decreases if the mean is close to 0 or 1, so all the variance-based weighting methods will assign a greater weight to the studies the more their mean separates from 0.5 (remember that CER can range from 0 to 1, like any other probability value). For this reason, it is necessary to carry out a transformation so that the values approach a normal instead of a binomial distribution and thus be able to carry out the weighting.

And I think we will leave it here for today. We are not going to go into the methods to transform the CER, such as the double arcsine or the application of mixed generalized linear models, since that is for the most exclusive minds, among which my own’s is not included. Anyway, don’t get stuck with this. I advise you to calculate the NNT using statistical packages or calculators, such as Calcupedev. There are other uses of NNT that we could also comment on and that can be obtained with these tools, as is the case with NNT in survival studies. But that is another story…

A good agreement?

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Kappa interobserver agreement coefficient

We all know that the less we go to the doctor, the best. And this is so for two reasons. First, because if we go to many doctors we are either physically ill or very mentally sick (some unfortunates are both of them). And second, which is the fact I am always struck by, because every doctor tells you something different. And it’s not that doctors don’t know their job, it’s that getting an agreement is not as simple as it seems.

To give you an idea, the problem starts when wanting to know if two doctor who assess the same diagnostic test have a good degree of agreement. Let’s see an example.

The director’s problem

Imagine for a moment than I am the manager of a hospital and I want to hire a pathologist because the only one that works at the hospital is overworked.kappa_pathologist

I meet with my pathologist and the applicant and give them 795 biopsies to tell me if there’re malignant cells in them. As you can see in the first table, my pathologist finds malignant cells in 99 biopsies, while the applicant sees them in 135 (do not panic, in real life difference couldn’t be so wide, could be?). We wonder what degree of agreement or, rather, concordance exists between the two. The first think that comes to our mind is to calculate the number of biopsies in which they agree: they both agree with 637 normal biopsies and 76 with malignant cells, so the percentages of cases of agreement can be calculated as (637+76)/795=0.896. Hurray!, we think, the two agree almost 90% of the time. The result is not as bad as it seemed to be looking at the table.

But it turns out that when I’m about to hire the new pathologist I wonder if they could have agreed just by chance.

So, a stupid experiment springs to my mind: I take the 795 biopsies and throw a coin, labeling each biopsy as normal if I get heads, or pathological, if tails.kappa_coin

The coin says I have 400 normal biopsies and 395 with malignant cells. If I calculate the concordance between the coin and the pathologist, I see that it values (365+55)/795=0.516, 52%!. This is really amazing, just by chance there’s agreement in half of the cases (yes, yes, I know that those know-it-all of you will be thinking that it’s not a surprise, since 50% is the probability of each possible outcome when tossing a coin). So I start thinking how to save money for my hospital and I come out with another experiment that this time is not only stupid, but totally ridiculous: I offer my cousin to do the test instead of throwing a coin (by this time I’m going to left my brother-in-law alone).kappa_cousin

The problem, of course, is that my cousin is not a doctor and, although a nice guy, pathology is not his main topic. So, when he starts to see the colorful cells he thinks it’s impossible that such beauty is produced by malignant cells and gives all the biopsies as normal. When we look at the table with the results the first think that we think if to burn it but, for the sake of curiosity, we calculate the concordance between my cousin and my pathologist and see that it’s 696/795=0.875, 87!. Conclusion: it could be more convenient to me to hire my cousin instead of a new pathologist.

At this stage many of you will think that I forgot to take my medication this morning, but the truth is that all these examples serve to show you that, if we want to know what the agreement between two observers is, we must first get rid of the cumbersome and everlasting effect of chance. And for that, mathematicians have invented a statistic called kappa, the interobserver agreement coefficient.

The concept of concordance

The function of kappa is to exclude from the observed agreement that part that is due to chance, obtaining a more representative measure of the strength of agreement between observers. Its formula is a ratio in which the numerator is the difference between observed and random difference and which denominator represents the complementary of the random agreement: (Po-Pr) / (1-Pr).

We already know the value of Po with two pathologists: 0.89. To get Pr we have to calculate the theoretical expected values for each cell of the table, in a similar way that we remember we did with chi squared test: the expected value of each cell is the product of the total of its row and column divided by the total of the table. As an example, the expected value of the first cell of our table is (696×660)/795=578. With the expected values we can calculate the probability of agreement due to chance using the same method we used earlier with observed values: (578+17)/795=0.74.

kappa_solutionAnd now we can calculate kappa = (0.89-0.74)/(1-0.74) = 0.57. And what can we conclude of a value of 0.57?. We can do with it whatever we want except multiply it by a hundred, because this values doesn’t represent a true percentage. The value of kappa can range between -1 and 1. Negative values indicate that concordance is worse than that expected by chance. A value of 0 indicates that the agreement is similar than that we could get flipping a coin. Values greater than 0 indicate that concordance is slight (0.01-0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80) or almost perfect (0.81-1.00). In our case, there’s a fairly good agreement between the two pathologists. If you are curious, you can calculate the kappa for my cousin and you’ll see that it’s no better than flipping a coin.

Kappa can also be calculated if we have measurements of several observers and more than one result for each observation, but tables get so unfriendly that it is better to use a statistical program to calculate it, and by the way, come up with confidence intervals.

Anyway, do not put much trust in kappa, because it needs not to be greater difference among table’s cells. If a cell has few cases the coefficient will tend to underestimate the actual concordance even if it’s very good.

We’re leaving…

Finally, say that, although all our examples showed tests with dichotomous result, it’s also possible to calculate interobserver agreement with quantitative results (a rating scale, for instance). Of course, for that we have to use another statistical technique as Bland-Altman’s test, but that’s another story…

I am Spartacus

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I was thinking about the effect size based on mean differences and how to know when that effect is really large and, because of the association of ideas, someone great has come to mind who, sadly, has left us recently. I am referring to Kirk Douglas, that hell of an actor that I will always remember for his roles as a Viking, as Van Gogh or as Spartacus, in the famous scene of the film in which all slaves, in the style of our Spanish’s Fuenteovejuna, stand up and proclaim together to be Spartacus so that Romans cannot do anything to the true one (or to get all equally whacked, much more typical of the modus operandi of the Romans of that time).

You won’t tell me the man wasn’t great. But how great if we compare it with others? How can we measure it? It is clear that not because of the number of Oscars, since that would only serve to measure the prolonged shortsightedness of the so-called academics of the cinema, which took a long time until they awarded him the honorary prize for his entire career. It is not easy to find a parameter that defines the greatness of a character like Issur Danielovitch Demsky, which was the ragman’s son’s name before becoming a legend.

We have it easier to quantify the effect size in our studies, although the truth is that researchers are usually more interested in telling us the statistical significance than in the size of the effect. It is so unusual to calculate it that even many statistical packages forget to have routines to obtain it. In this post, we are going to focus on how to measure the effect size based on differences between means.

Imagine that we want to conduct a trial to compare the effect of a new treatment against placebo and that we are going to measure the result with a quantitative variable X. What we will do is calculate the mean effect between participants in the experimental or intervention group and compare it with the mean of the participants in the control group. Thus, the effect size of the intervention with respect to the placebo will be represented by the magnitude of the difference between the mean in the experimental group and that of the control group:d= \bar{x}_{e}-\bar{x}_{c}However, although it is the easiest to calculate, this value does not help us to get an idea of the effect size, since its magnitude will depend on several factors, such as the unit of measure of the variable. Let us think about how the differences change if one mean is twice the other as their values are 1 and 2 or 0.001 and 0.002. In order for this difference to be useful, it is necessary to standardize it, so a man named Gene Glass thought he could do it by dividing it by the standard deviation of the control group. He obtained the well-known Glass’ delta, which is calculated according to the following formula:\delta = \frac{\bar{x}_{e}-\bar{x}_{c}}{S_{s}}Now, since what we want is to estimate the value of delta in the population, we will have to calculate the standard deviation using n-1 in the denominator instead of n, since we know that this quasi-variance is a better estimator of the population value of the deviation:S_{c}=\sqrt{\frac{\sum_{i=1}^{n_{c}}(x_{ic}-\bar{x}_{c})}{n_{c}-1}}But do not let yourselves be impressed by delta, it is not more than a Z score (those obtained by subtracting to the value its mean and dividing it by the standard deviation): each unit of the delta value is equivalent to one standard deviation, so it represents the standardized difference in the effect that occurs between the two groups due to the effect of the intervention. This value allows us to estimate the percentage of superiority of the effect by calculating the area under the curve of the standard normal distribution N(0,1) for a specific delta value (equivalent to the standard deviation). For example, we can calculate the area that corresponds to a delta value = 1.3. Nothing is simpler than using a table of values of the standard normal distribution or, even better, the pnorm() function of R, which returns the value 0.90. This means that the effect in the intervention group exceeds the effect in the control group by 90%.

The problem with Glass’ delta is that the difference in means depends on the variability between the two groups, which makes it sensitive to these variance differences. If the variances of the two groups are very different, the delta value may be biased. That is why one Larry Vernon Hedges wanted to contribute with his own letter to this particular alphabet and decided to do the calculation of Glass in a similar way, but using a unified variance that does not assume their equality, according to the following formula:S_{u}=\sqrt{\frac{(n_{e}-1)S_{e}^{2}+(n_{c}-1)S_{c}^{2}}{n_{e}+n_{c}-2}}If we substitute the variance of the control group of the Glass’ delta formula with this unified variance we will obtain the so-called Hedges’ g. The advantage of using this unified standard deviation is that it takes into account the variances and sizes of the two groups, so g has less risk of bias than delta when we cannot assume equal variances between the two groups.

However, both delta and g have a positive bias, which means that they tend to overestimate the effect size. To avoid this, Hedges modified the calculation of his parameter in order to obtain an adjusted g, according to the following formula:g_{a}=g\left ( 1-\frac{3}{4df-9} \right )where df are the degrees of freedom, which are calculated as ne + nc.

This correction is more needed with small samples (few degrees of freedom). It is logical, if we look at the formula, the more degrees of freedom, the less necessary it will be to correct the bias.

So far, we have tried to solve the problem of calculating an estimator of the effect size that is not biased by the lack of equal variances. The point is that, in the rigid and controlled world of clinical trials, it is usual that we can assume the equality of variances between the groups of the two branches of the study. We might think, then, that if this is true, it would not be necessary to resort to the trick of n-1.

Well, Jacob Cohen thought the same, so he devised his own parameter, Cohen’s d. This Cohen’s d is similar to Hedges’ g, but still more sensitive to inequality of variances, so we will only use it when we can assume the equality of variances between the two groups. Its calculation is identical to that of the Hedges’ g, but using n instead of n-1 to obtain the unified variance.

As a rough-and-ready rule, we can say that the effect size is small for d = 0.2, medium for d = 0.5, large for d = 0.8 and very large for d = 1.20. In addition, we can establish a relationship between d and the Pearson’s correlation coefficient (r), which is also a widely used measure to estimate the effect size.

The correlation coefficient measures the relationship between an independent binary variable (intervention or control) and a numerical dependent variable (our X). The great advantage of this measure is that it is easier to interpret than the parameters we have seen so far, which all function as standardized Z scores. We already know that r can range from -1 to 1 and the meaning of these values.

Thus, if you want to calculate r given d, you only have to apply the following formula:r=\frac{d}{\sqrt{d^{2}+\left ( \frac{1}{pq} \right )}}where p and q are the proportions of subjects in the experimental and control groups (p = ne / n and q = nc / n). In general, the larger the effect size, the greater r and vice versa (although it must be taken into account that r is also smaller as the difference between p and q increases). However, the factor that most determines the value of r is the value of d.

And with this we will end for today. Do not believe that we have discussed all the measures of this family. There are about a hundred parameters to estimate the effect size, such as the determination coefficient, eta-square, chi-square, etc., even others that Cohen himself invented (not very happy with only d), such as f-square or Cohen’s q. But that is another story…

When nothing bad happens, is everything okay?

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I have a brother-in-law who is increasingly afraid of getting on a plane. He is able to make road trips for several days in a row so as not to take off the ground. But it turns out that the poor guy has no choice but to make a transcontinental trip and he has no choice but to take a plane to travel.

But at the same time, my brother-in-law, in addition to being fearful, is an occurrence person. He has been counting the number of flights of the different airlines and the number of accidents that each one has had in order to calculate the probability of having a mishap with each of them and fly with the safest. The matter is very simple if we remember that of probability equals to favorable cases divided by possible cases.

And it turns out that he is happy because there is a company that has made 1500 flights and has never had any accidents, then the probability of having an accident flying on their planes will be, according to my brother-in-law, 0/1500 = 0. He is now so calm that he almost has lost his fear to fly. Mathematically, it is almost certain that nothing will happen to him. What do you think about my brother-in-law?

Many of you will already be thinking that using brothers-in-law for these examples has these problems. We all know how brothers-in-law are… But don’t be unfair to them. As the famous humorist Joaquín Reyes says, “we all of us are brothers-in-law”, so just remember it. Of which there is no doubt, is that we will all agree with the statement that my brother-in-law is wrong: the fact that there has not been any mishap in the 1500 flights does not guarantee that the next one will not fall. In other words, even if the numerator of the proportion is zero, if we estimate the real risk it would be incorrect to keep zero as a result.

This situation occurs with some frequency in Biomedicine research studies. To leave airlines and aerophobics alone, think that we have a new drug with which we want to prevent this terrible disease that is fildulastrosis. We take 150 healthy people and give them the antifildulin for 1 year and, after this follow-up period, we do not detect any new cases of disease. Can we conclude then that the treatment prevents the development of the disease with absolute certainty? Obviously not. Let’s think about it a little.

Making inferences about probabilities when the numerator of the proportion is zero can be somewhat tricky, since we tend to think that the non-occurrence of events is something qualitatively different from the occurrence of one, few or many events, and this is not really so. A numerator equal to zero does not mean that the risk is zero, nor does it prevent us from making inferences about the size of the risk, since we can apply the same statistical principles as to non-zero numerators.

Returning to our example, suppose that the incidence of fildulastrosis in the general population is 3 cases per 2000 people per year (1.5 per thousand, 0.15% or 0.0015). Can we infer with our experiment if taking antifildulin increases, decreases or does not modify the risk of suffering fildulastrosis? Following the familiar adage, yes, we can.

We will continue our habit of considering the null hypothesis as of equal effect, so that the risk of disease is not modified by the new treatment. Thus, the risk of each of the 150 participants becoming ill throughout the study will be 0.0015. In other words, the risk of not getting sick will be 1-0.0015 = 0.9985. What will be the probability that none will get sick during the year of the study? Since there are 150 independent events, the probability that 150 subjects do not get sick will be 0.98985150 = 0.8. We see, therefore, that although the risk is the same as that of the general population, with this number of patients we have an 80% chance of not detecting any event (fildulastrosis) during the study, so it would be more surprising to find a patient who the fact of not having any. But the most surprising thing is that we are, thus, getting the probability that we do not have any sick in our sample: the probability that there is no sick is not 0 (0/150), as my brother-in-law thinks, but 80 %!

And the worst part is that, given this result, pessimism invades us: it is even possible that the risk of disease with the new drug is greater and we are not detecting it. Let’s assume that the risk with medication is 1% (compared to 0.15% of the general population). The risk of none being sick would be (1-0.01)150 = 0.22. Even with a 2% risk, the risk of not getting any disease is (1-0.02)150 = 0.048. Remember that 5% is the value that we usually adopt as a “safe” limit to reject the null hypothesis without making a type 1 error.

At this point, we can ask ourselves if we are very unfortunate and have not been lucky enough to detect cases of illness when the risk is high or, on the contrary, that we are not so unfortunate and, in reality, the risk must be low. To clarify ourselves, we can return to our usual 5% confidence limit and see with what risk of getting sick with the treatment we have at least a 5% chance of detecting a patient:

– Risk of 1.5/1000: (1-0.0015)150 = 0.8.

– Risk of 1/1000: (1-0.001)150 = 0.86.

– Risk of 1/200: (1-0.005)150 = 0.47.

– Risk of 1/100: (1-0.01)150 = 0.22.

– Risk of 1/50: (1-0.02)150 = 0.048.

– Risk of 1/25: (1-0.04)150 = 0.002.

As we see in the previous series, our “security” range of 5% is reached when the risk is below 1/50 (2% or 0.02). This means that, with a 5% probability of being wrong, the risk of fildulastrosis taking antifuldulin is equal to or less than 2%. In other words, the 95% confidence interval of our estimate would range from 0 to 0.02 (and not 0, if we calculate the probability in a simplistic way).

To prevent our reheated neurons from eventually melting, let’s see a simpler way to automate this process. For this we use what is known as the rule of 3. If we do the study with n patients and none present the event, we can affirm that the probability of the event is not zero, but less than or equal to 3/n. In our example, 3/150 = 0.02, the probability we calculate with the laborious method above. We will arrive at this rule after solving the equation we use with the previous method:

(1 – maximum risk) n = 0.05

First, we rewrite it:

1 – maximum risk = 0.051/n

If n is greater than 30, 0.051/n approximates (n-3)/n, which is the same as 1-(3/n). In this way, we can rewrite the equation as:

1- maximum risk = 1 – (3/n)

With which we can solve the equation and get the final rule:

Maximum risk = 3/n.

You have seen that we have considered that n is greater than 30. This is because, below 30, the rule tends to overestimate the risk slightly, which we will have to take into account if we use it with reduced samples.

And with this we will end this post with some considerations. First, and as is easy to imagine, statistical programs calculate risk’s confidence intervals without much effort even if the numerator is zero. Similarly, it can also be done manually and much more elegantly by resorting to the Poisson probability distribution, although the result is similar to that obtained with the rule of 3.

Second, what happens if the numerator is not 0 but a small number? Can a similar rule be applied? The answer, again, is yes. Although there is no general rule, extensions of the rule have been developed for a number of events up to 4. But that’s another story…

Like a hypermarket

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There is one thing that happens on a recurring basis and that is a real slap in the face for me. It turns out that I like to go shopping for food once a week, so I usually go to the hypermarket every Friday. I am a creature of habit that always eats the same things and almost the same days, so I go swift and fast through the aisles of the hyper throwing things in the shopping cart so I have the matter settled in the twinkling of an eye. The problem is that in hypermarkets they have the bad habit of periodically changing foods sites, so you go crazy until you learn its new location again. To cap it all, the first few days foods have been changed, but not yet its information panels, so I have to go around a thousand turns until I find the cans of squid in their ink that, as we all know, are one of our main staple foods.

You will wonder why I tell you all this stuff. As it turns out, the National Library of Medicine (NML) has done a similar thing: now that I had finally managed to learn how the its search engine worked, they go and change it completely.

Of course, it must be said in honor of the truth that NML’s people have not limited themselves to changing the aspects of windows and boxes, but have implemented a radical change with an interface that they define as cleaner and simpler, as well as better adapted to mobile devices, which are increasingly used to do bibliographic searches. But that doesn’t end there: there are a lot of improvements in the algorithms to find the more than 30 million citations that Pubmed includes and, in addition, the platform is hosted in the cloud, promising to be more stable and efficient.

The NLM announced the new Pubmed in October 2019 and it will be the default option at the beginning of the year 2020 so, although the legacy version will be available a few more months, we have no choice but to learn how to handle the new version. Let’s take a look.

Although all the functionalities that we know of the legacy version are also present in the new one, the aspect is radically different from the home page, which I show you in the first figure.The most important element is the new search box, where we have to enter the text to click on the “Search” button. If the NLM does not deceive us, this will be the only resource that we will have to use the vast majority of the time, although we still have a link at our disposal to enter the advanced search mode.

Below we have four sections, including the one that contains help to learn how to use the new version, and that include tools that we already knew, such as “Clinical Queries”, “Single Citation Matcher” or “MeSH Database”. At the time of writing this post, these links direct you to the old versions of the tools, but this will change when the new interface is accessed by default.

Finally, a new component called “Trending Articles” has been added at the bottom. Here are articles of interest, which do not have to be the most recent ones, but those that have aroused interest lately and have been viralized in one way or another. Next to this we have the “Latest Literature” section, where recent articles from high impact journals are shown.

Now let’s see a little how searches are done using the new Pubmed. One of the keys to this update is the simple search box, which has become much smarter by incorporating a series of new sensors that, according to the NLM, try to detect exactly what we want to look for from the text we have inserted.

For example, if we enter information about the author, the abbreviation of the journal and the year of publication, the citation sensor will detect that we have entered basic citation information and will try to find the article we are looking for. For example, if I type ” campoy jpgn 2019″, I will get the results you see in second figure, where Pubmed shows the two articles found published by this doctor in this Journal in 2019. It would be something like what before we obtained using the “Single Citation Matcher”.

We can also do the search in a more traditional way. For example, if we want to search by author, it is best to write the last name followed by the initial of the name, all in lower case, without labels or punctuation marks. For example, if we want to look for articles by Yvan Vandenplas, we will write “vandenplas y”, with which we will obtain the papers that I show you in the third figure. Of course, we can also search by subject. If I type “parkinson” in the search box, Pubmed will make a series of suggestions on similar search terms. If I press “Search”, I get the results of the fourth figure which, as you can see , includes all the results with the related terms.

Let us now turn to the results page, which is also full of surprises. You can see a detail in the fifth figure. Under the search box we have two links: “Advanced”, to access the advanced search, and “Create alert”, so that Pubmed notifies us every time a new related article is incorporated (you already know that for this to be possible we have to create an account in NCBI and enter by pressing the “Login” button at the top; this account is free and saves all our activity in Pubmed for later use).

Below these links there are three buttons which allow you to save the search ( “Save”), send it by e-mail (“Email”) and, clicking the three points button, send it to the clipboard or to our bibliography or collections, if we have an NCBI account.

On the right we have the buttons to sort the results. The “Best Match” is one of the new priorities of the NLM, which tries to show us in the first positions the most relevant articles. Anyway, we can sort them in chronological order (“Most recent”), as well as change the way of presenting them by clicking on the gearwheel on the right (in “Summary” or “Abstract” format).

We are going to focus now into the left of the results page. The first thing we see is a graph with the results indexed by year. This graph can be enlarged, which allows us to see the evolution of the number of papers on the subject indexed over time. In addition, we can modify the time interval and restrict the search to what is published in a given period. In the sixth figure I show you how to limit the search to the results of the last 10 years.Under each result we have two new links: “Cite” and “Share”. The first allows us to write the work citation in several different formats. The second, share it on social networks.

Finally, to the left of the results screen we have the list of filters that we can apply. These can be added or removed in a similar way to how it was done with the legacy version of Pubmed and its operation is very intuitive, so we will not spend more time on them.

If we click on one of the items in the list of results we will access the screen with the text of the paper (seventh figure). This screen is similar to that of the legacy version of Pubmed, although new buttons such as “Cite” and those for accessing social networks are included, as well as additional information on related articles and articles in which the one we have selected is cited. Also, as a novelty, we have some navigation arrows on the left and right ends of the screen to change to the text of the previous and subsequent articles, respectively.

To finish this post, let’s take a look at the new advanced search, which can be accessed by clicking on the “Advanced” link, which will take us to the screen you see in the eighth figure.

Its operation is very similar to the legacy version. We can add terms with Boolean operators, combine searches, etc. I encourage you to play with the advanced search, the possibilities are endless. The newest part of this tool is the section with the history and the search details (“History and Search Details”) at the bottom. This allows you to keep previous searches and return to them, taking into account that all this is lost when you leave Pubmed, unless you have an NCBI account.

I call your attention to the “Search Details” tab, which you can open as shown in the ninth figure. The search becomes more transparent, since it shows how Pubmed interpreted it based on an automatic system of choice of terms (“Automatic Term Mapping”). Although we do not know very well how to narrow the search to specific terms of Parkinson’s disease, Pubmed does know what we are looking for and includes all the terms in the search, in addition to the initial text that we introduced, of course.

And here we end for today. You have seen that these people of the NLM have outdone themselves, putting at our disposal a new tool easier to use, but at the same time, much more powerful and intelligent. Google must be shaking with fear, but don’t worry, it is sure it will invent something to try to prevail.

You can go forgetting about the legacy version, do not wait for it to disappear to start enjoying the new one. We will have to talk about these issues again when new versions of the rest of the tools are established, such as Clinical Queries, but that is another story …

Columns, sectors, and an illustrious Italian

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When you read the title of this post, you can ask yourself with what stupid occurrence am I going to crush the suffered concurrence today, but do not fear, all we are going to do is to put in prospective value that famous aphorism that says that a picture is worth a thousand words. Have I clarified something? I suppose not.

As we all know, descriptive statistics is that branch of statistics that we usually use to obtain a first approximation to the results of our study, once we have finished it.

The first thing we do is to describe the data, for which we make frequency tables and use various measures of tendency and dispersion. The problem with these parameters is that, although they truly represent the essence of the data, it is sometimes difficult to provide a synthetic and comprehensive view with them. It is in these cases that we can resort to another resource, which is none other than the graphic representation of the study results. You know, a picture is worth a thousand words, or so they say.

There are many types of graphs to help us better understand the data, but today we are only going to talk about those that have to do with qualitative or categorical variables.

Remember that qualitative variables represent attributes or categories of the variable. When the variable does not include any sense of order, it is said to be a nominal categorical variable, while if a certain order can be established between the categories, we would say that it is an ordinal categorical variable. For example, the variable “smoker” would be nominal if it has two possibilities: “yes” or “no”. However, if we define it as “occasional”, “little smoker”, “moderate” or “heavy smoker”, there is already a certain hierarchy and we speak of ordinal qualitative variable.

The first type of chart that we are going to consider when representing a qualitative variable is the pie chart. This consists of a circle whose area represents the total data. Thus, an area that will be directly proportional to its frequency is assigned to each category. In this way, the most frequent categories will have larger areas, so that we can get an idea of how the frequencies are distributed in the categories at a glance.

There are three ways to calculate the area of each sector. The simplest is to multiply the relative frequency of each category by 360 °, obtaining the degrees of that sector.

The second is to use the absolute frequency of the category, according to the following rule of three:

Absolute frequency / Total data frequency = Degrees of the sector / 360 °

Finally, the third way is to use the proportions or percentages of the categories:

% of the category / 100% = Degrees of the sector / 360 °

The formulas are very simple, but, in any case, there will be no need to resort to them because the program with which we make the graph will do it for us. The instruction in R is pie(), as you can see in the first figure, in which I show you a distribution of children with exanthematic diseases and how the pie chart would be represented.The pie chart is designed to represent nominal categorical variables, although it is not uncommon to see pies representing variables of other types. However, and in my humble opinion, this is not entirely correct.

For example, if we make a pie chart for an ordinal qualitative variable, we will be losing information about the hierarchy of the variables, so it would be more correct to use a chart that allows to sort the categories from less to more. And this chart is none other than the bar chart, which we’ll talk about next.

The pie chart will be especially useful when there are few categories of the variable. If there are many, the interpretation is no longer so intuitive, although we can always complete the graph with a frequency table that helps us to better interpret the data. Another tip is to be very careful with 3D effects when drawing cakes. If we go from elaborate, the graphic will lose clarity and will be more difficult to read.

The second graph that we are going to see is, as we have already mentioned, the bar chart, the optimum to represent ordinal qualitative variables. On the horizontal axis, the different categories are represented, and on it some columns or bars are raised whose height is proportional to the frequency of each category. We could also use this type of graph to represent discrete quantitative variables, but what is not very correct to do is use it for the qualitative nominal variables.

The bar chart is able to express the magnitude of the differences between the categories of the variable, but it is precisely its weak point, since it is easily manipulated if we modify the axes’ scales. That is why we must be careful when analyzing this type of graphics to avoid being deceived by the message that the author of the study may want to convey.

This chart is also easy to do with most statistical programs and spreadsheets. The function in R is barplot(), as you can see in the second figure, which represents a sample of asthmatic children classified by severity.

With what has been seen so far, some will think that the title of this post is a bit misleading. Actually, the thing is not about columns and sectors, but about bars and pies. Also, who is the illustrious Italian? Well, here I do not fool anyone, because the character was both Italian and illustrious, and I am referring to Vilfredo Federico Pareto.

Pareto was an Italian who was born in the mid-19th century in Paris. This small contradiction is due to the fact that his father was then exiled in France for being one of the followers of Giuseppe Mazzini, who was then committed to Italian unification. Anyway, Pareto lived in Italy from he was 10 years old on, becoming an engineer with extensive mathematical and humanistic knowledge and who contributed decisively to the development of microeconomics. He spoke and wrote fluently in French, English, Italian, Latin and Greek, and became famous for a multitude of contributions such as the Pareto’s distribution, Pareto’s efficiency, Pareto’s index and Pareto’s principle. To represent the latter, he invented the Pareto’s diagram, which is what brings him here today among us.

Pareto chart (also known in economics as a closed curve or A-B-C distribution) organizes the data in descending order from left to right, represented by bars, thus assigning an order of priorities. In addition, the diagram incorporates a curved line that represents the cumulative frequency of the categories of the variable. This initially allowed the Pareto’s principle to be explained, which goes on to say that there are many minor problems compared to a few that are important, which was very useful for decision-making.

As it is easy to understand, this prioritization makes the Pareto diagram especially useful for representing ordinal qualitative variables, surpassing the bar chart by giving information on the percentage accumulated by adding the categories of the distribution of the variable. The change in slope of this curve also informs us of the change in the concentration of data, which depends on the variability in which the subjects of the sample are divided between the different categories.

Unfortunately, R does not have a simple function to represent Pareto diagrams, but we can easily obtain it with the script that I attached in the third figure, obtaining the graph of the fourth.

And here we are going to leave it for today. Before saying goodbye, I want to warn you that you should not confuse the bars of the bar chart with those of the histogram since, although they can be similar from the graphic point of view, both represent very different things. In a bar chart only the values of the variables we have observed when doing the study are represented. However, the histogram goes much further since, in reality, it contains the frequency distribution of the variable, so it represents all possible values that exist within the intervals, although we have not observed any directly. It allows us to calculate the probability that any distribution value will be represented, which is of great importance if we want to make inference and estimate population values based on the results of our sample. But that is another story…

Like a forgotten clock

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I don’t like the end of summer. The days with bad weather begin, I wake up completely in the dark and in the evening it gets dark early and early. And, as if this were not bad enough, the cumbersome moment of change between summer and winter time is approaching.

In addition to the inconvenience of the change and the tedium of being two or three days remembering what time it is and what it could be if it had not been any change, we must proceed to adjust a lot of clocks manually. And, no matter how much you try to change them all, you always leave some with the old hour. It does not happen to you with the kitchen clock, at which you always look to know how fast you have to have breakfast, or with the one in the car, which stares at you every morning. But surely there are some that you do not change. Even, it has ever happened to me, that I realize it when the next time to change I see that I don’t need to do it because I left it unchanged in the previous time.

These forgotten clocks remind me a little of categorical or qualitative variables.

You will think that, once again, I forgot to take my pill this morning, but no. Everything has its reasoning. When we finish a study and we already have the results, the first thing we do is a description of them and then go on to do all kinds of contrasts, if applicable.

Well, qualitative variables are always belittled when we apply our knowledge of descriptive statistics. We usually limit ourselves to classifying them and making frequency tables with which to calculate some indices as their relative or accumulated frequency, to give some representative measure such as mode and little else. We use to work a little more with its graphic representation with bar or sector diagrams, pictograms and other similar inventions. And finally, we apply a little more effort when we relate two qualitative variables through a contingency table.

However, we forget their variability, something we would never do with a quantitative variable. The quantitative variables are like that kitchen wall clock that looks us straight in the eye every morning and does not allow us to leave it out of time. Therefore, we use these concepts we understand very well as the mean and variance or standard deviation. But that we do not know how to objectively measure the variability of qualitative or categorical variables, whether nominal or ordinal, does not mean that it does not exist a way to do it. For this purpose, several diversity indexes have been developed, which some authors distinguish as dispersion, variability and disparity indexes. Let’s see some of them, whose formulas you can see in the attached box, so you can enjoy the beauty of mathematical language.

The two best known indexes used to measure the variability or diversity are the Blau’s index (or of Hirschman- Herfindal’s) and the entropy index (or Teachman’s). Both have a very similar meaning and, in fact, are linearly correlated.

Blau’s index quantifies the probability that two individuals chosen at random from a population are in different categories of a variable (provided that the population size is infinite or the sampling is performed with replacement). Its minimum value, zero, would indicate that all members are in the same category, so there would be no variety. The higher its value, the more dispersed among the different categories of the variable will be the components of the group. This maximum value is reached when the components are distributed equally among all categories (their relative frequencies are equal). Its maximum value would be (k-1) / k, which is a function of k (the number of categories of the qualitative variable) and not of the population size. This value tends to 1 as the number of categories increases (to put it more correctly, when k tends to infinity).

Let’s look at some examples to clarify it a bit. If you look at the Blau’s index formula, the value of the sum of the squares of the relative frequencies in a totally homogeneous population will be 1, so the index will be 0. There will only be one category with frequency 1 (100%) and the rest with zero frequency.

As we have said, although the subjects are distributed similarly in all categories, the index increases as the number of categories increases. For example, if there are four categories with a frequency of 0.25, the index will be 0.75 (1 – (4 x 0.252)). If there are five categories with a frequency of 0.2, the index will be 0.8 (1 – (5 x 0.22). And so on.

As a practical example, imagine a disease in which there is diversity from the genetic point of view. In a city A, 85% of patients has genotype 1 and 15% genotype 2. The Blau’s index values 1 – (0.85+ 0.152) = 0.255. In view of this result, we can say that, although it is not homogeneous, the degree of heterogeneity is not very high.

Now imagine a city B with 60% of genotype 1, 25% of genotype 2 and 15% of genotype 3. The Blau’s index will be 1 – (0.6x 0.252 x 0.152) = 0.555. Clearly, the degree of heterogeneity is greater among the patients of city B than among those of A. The smartest of you will tell me that that was already clear without calculating the index, but you have to take into account that I chose a very simple example for not giving my all calculating. In real-life, more complex studies, it is not usually so obvious and, in any case, it is always more objective to quantify the measure than to remain with our subjective impression.

This index could also be used to compare the diversity of two different variables (as long as it makes sense to do so) but, the fact that its maximum value depends on the number of categories of the variable, and not on the size of the sample or population, questions its usefulness to compare the diversity of variables with different number of categories. To avoid this problem, the Blau’s index can be normalized by dividing it by its maximum, thus obtaining the qualitative variation index. Its meaning is, of course, the same as that of the Blau’s index and its value ranges between 0 and 1. Thus, we can use either one if we compare the diversity of two variables with the same number of categories, but it will be more correct to use the qualitative variation index if the variables have a different number of categories.

The other index, somewhat less famous, is the Teachman’s index or entropy index , whose formula is also attached. Very briefly we will say that its minimum value, which is zero, indicates that there are no differences between the components in the variable of interest (the population is homogeneous). Its maximum value can be estimated as the negative value of the neperian logarithm of the inverse of the number of categories (- ln ( 1 / k)) and is reached when all categories have the same relative frequency (entropy reaches its maximum value). As you can see, very similar to Blau’s, which is much easier to calculate than Teachman’s.

To end this entry, the third index that I want to talk about today tells us, more than about the variability of the population, about the dispersion that its components have regarding the most frequent value. This can be measured by the variation ratio, which indicates the degree to which the observed values ​​do not coincide with that of mode, which is the most frequent category. As with the previous ones, I also show the formula in the attached box.

In order not to clash with the previous ones, its minimum value is also zero and is obtained when all cases coincide with the mode. The lower the value, the less the dispersion. The lower the absolute frequency of the mode, the closer it will be to 1, the value that indicates maximum dispersion. I think this index is very simple, so we are not going to devote more attention to it.

And we have reached the end of this post. I hope that from now on we will pay more attention to the descriptive analysis of the results of the qualitative variables. Of course, it would be necessary to complete it with an adequate graphic description using the well-known bar or sector diagrams (the pies) and others less known as the Pareto’s diagrams. But that is another story…