Little ado about too much

Critical appraisal of meta-analysis

Yes, I know that the saying goes just the opposite. But that is precisely the problem we have with so much new information technology. Today anyone can write and make public what goes through his head, reaching a lot of people, although what he says is bullshit (and no, I do not take this personally, not even my brother-in-law reads what I post!). The trouble is that much of what is written is not worth a bit, not to refer to any type of excreta. There is a lot of smoke and little fire, when we all would like the opposite to happen.

The same happens in medicine when we need information to make some of our clinical decisions. Anywhere the source we go, the volume of information will not only overwhelm us, but above all the majority of it will not serve us at all. Also, even if we find a well-done article it may not be enough to answer our question completely. That’s why we love so much the revisions of literature that some generous souls publish in medical journals. They save us the task of reviewing a lot of articles and summarizing the conclusions. Great, isn’t it? Well, sometimes it is, sometimes it is not. As when we read any type of medical literature’s study, we should always make a critical appraisal and not rely solely on the good know-how of its authors.

Revisions, of which we already know there are two types, also have their limitations, which we must know how to value. The simplest form of revision, our favorite when we are younger and ignorant, is what is known as a narrative review or author’s review. This type of review is usually done by an expert in the topic, who reviews the literature and analyzes what she finds as she believes that it is worth (for that she is an expert) and summarizes the qualitative synthesis with her expert’s conclusions. These types of reviews are good for getting a general idea about a topic, but they do not usually serve to answer specific questions. In addition, since it is not specified how the information search is done, we cannot reproduce it or verify that it includes everything important that has been written on the subject. With these revisions we can do little critical appraising, since there is no precise systematization of how these summaries have to be prepared, so we will have to trust unreliable aspects such as the prestige of the author or the impact of the journal where it is published.

As our knowledge of the general aspects of science increases, our interest is shifting towards other types of revisions that provide us with more specific information about aspects that escape our increasingly wide knowledge. This other type of review is the so-called systematic review (SR), which focuses on a specific question, follows a clearly specified methodology of searching and selection of information and performs a rigorous and critical analysis of the results found. Moreover, when the primary studies are sufficiently homogeneous, the SR goes beyond the qualitative synthesis, also performing a quantitative synthesis analysis, which has the nice name of meta-analysis. With these reviews we can do a critical appraising following an ordered and pre-established methodology, in a similar way as we do with other types of studies.

The prototype of SR is the one made by the Cochrane’s Collaboration, which has developed a specific methodology that you can consult in the manuals available on its website. But, if you want my advice, do not trust even the Cochrane’s and make a careful critical appraising even if the review has been done by them, not taking it for granted simply because of its origin. As one of my teachers in these disciplines says (I’m sure he’s smiling if he’s reading these lines), there is life after Cochrane’s. And, besides, there is lot of it, and good, I would add.

Critical appraisal of meta-analyes

Although SRs and meta-analyzes impose a bit of respect at the beginning, do not worry, they can be critically evaluated in a simple way considering the main aspects of their methodology. And to do it, nothing better than to systematically review our three pillars: validity, relevance and applicability.

Regarding VALIDITY, we will try to determine whether or not the revision gives us some unbiased results and respond correctly to the question posed. As always, we will look for some primary validity criteria. If these are not fulfilled we will think if it is already time to walk the dog: we probably make better use of the time.

Has the aim of the review been clearly stated? All SRs should try to answer a specific question that is relevant from the clinical point of view, and that usually arises following the PICO scheme of a structured clinical question. It is preferable that the review try to answer only one question, since if it tries to respond to several ones there is a risk of not responding adequately to any of them. This question will also determine the type of studies that the review should include, so we must assess whether the appropriate type has been included. Although the most common is to find SRs of clinical trials, they can include other types of observational studies, diagnostic tests, etc. The authors of the review must specify the criteria for inclusion and exclusion of the studies, in addition to considering their aspects regarding the scope of realization, study groups, results, etc. Differences among the studies included in terms of (P) patients, (I) intervention or (O) outcomes make two SRs that ask the same question to reach to different conclusions.

If the answer to the two previous questions is affirmative, we will consider the secondary criteria and leave the dog’s walk for later. Have important studies that have to do with the subject been included? We must verify that a global and unbiased search of the literature has been carried out. It is frequent to do the electronic search including the most important databases (generally PubMed, Embase and the Cochrane’s Library), but this must be completed with a search strategy in other media to look for other works (references of the articles found, contact with well-known researchers, pharmaceutical industry, national and international registries, etc.), including the so-called gray literature (thesis, reports, etc.), since there may be important unpublished works. And that no one be surprised about the latter: it has been proven that the studies that obtain negative conclusions have more risk of not being published, so they do not appear in the SR. We must verify that the authors have ruled out the possibility of this publication bias. In general, this entire selection process is usually captured in a flow diagram that shows the evolution of all the studies assessed in the SR.

It is very important that enough has been done to assess the quality of the studies, looking for the existence of possible biases. For this, the authors can use an ad hoc designed tool or, more usually, resort to one that is already recognized and validated, such as the bias detection tool of the Cochrane’s Collaboration, in the case of reviews of clinical trials. This tool assesses five criteria of the primary studies to determine their risk of bias: adequate randomization sequence (prevents selection bias), adequate masking (prevents biases of realization and detection, both information biases), concealment of allocation (prevents selection bias), losses to follow-up (prevents attrition bias) and selective data information (prevents information bias). The studies are classified as high, low or indeterminate risk of bias according to the most important aspects of the design’s methodology (clinical trials in this case).

In addition, this must be done independently by two authors and, ideally, without knowing the authors of the study or the journals where the primary studies of the review were published. Finally, it should be recorded the degree of agreement between the two reviewers and what they did if they did not agree (the most common is to resort to a third party, which will probably be the boss of both).

To conclude with the internal or methodological validity, in case the results of the studies have been combined to draw common conclusions with a meta-analysis, we must ask ourselves if it was reasonable to combine the results of the primary studies. It is fundamental, in order to draw conclusions from combined data, that the studies are homogeneous and that the differences among them are due solely to chance. Although some variability of the studies increases the external validity of the conclusions, we cannot unify the data for the analysis if there are a lot of variability. There are numerous methods to assess the homogeneity about which we are not going to refer now, but we are going to insist on the need for the authors of the review to have studied it adequately.

In summary, the fundamental aspects that we will have to analyze to assess the validity of a SR will be: 1) that the aims of the review are well defined in terms of population, intervention and measurement of the result; 2) that the bibliographic search has been exhaustive; 3) that the criteria for inclusion and exclusion of primary studies in the review have been adequate; and 4) that the internal or methodological validity of the included studies has also been verified. In addition, if the SR includes a meta-analysis, we will review the methodological aspects that we saw in a previous post: the suitability of combining the studies to make a quantitative synthesis, the adequate evaluation of the heterogeneity of the primary studies and the use of a suitable mathematical model to combine the results of the primary studies (you know, that of the fixed effect and random effects models).

Regarding the RELEVANCE of the results we must consider what is the overall result of the review and if the interpretation has been made in a judicious manner. The SR should provide a global estimate of the effect of the intervention based on a weighted average of the included quality items. Most often, relative measures such as risk ratio or odds ratio are expressed, although ideally, they should be complemented with absolute measures such as absolute risk reduction or the number needed to treat (NNT). In addition, we must assess the accuracy of the results, for which we will use our beloved confidence intervals, which will give us an idea of ​​the accuracy of the estimation of the true magnitude of the effect in the population. As you can see, the way of assessing the importance of the results is practically the same as assessing the importance of the results of the primary studies. In this case we give examples of clinical trials, which is the type of study that we will see more frequently, but remember that there may be other types of studies that can better express the relevance of their results with other parameters. Of course, confidence intervals will always help us to assess the accuracy of the results.

The results of the meta-analyzes are usually represented in a standardized way, usually using the so-called forest plot. A graph is drawn with a vertical line of zero effect (in the one for relative risk and odds ratio and zero for means differences) and each study is represented as a mark (its result) in the middle of a segment (its confidence interval). Studies with results with statistical significance are those that do not cross the vertical line. Generally, the most powerful studies have narrower intervals and contribute more to the overall result, which is expressed as a diamond whose lateral ends represent its confidence interval. Only diamonds that do not cross the vertical line will have statistical significance. Also, the narrower the interval, the more accurate result. And, finally, the further away from the zero-effect line, the clearer the difference between the treatments or the comparative exposures will be.

If you want a more detailed explanation about the elements that make up a forest plot, you can go to the previous post where we explained it or to the online manuals of the Cochrane’s Collaboration.

We will conclude the critical appraising of the SR assessing the APPLICABILITY of the results to our environment. We will have to ask ourselves if we can apply the results to our patients and how they will influence the care we give them. We will have to see if the primary studies of the review describe the participants and if they resemble our patients. In addition, although we have already said that it is preferable that the SR is oriented to a specific question, it will be necessary to see if all the relevant results have been considered for the decision making in the problem under study, since sometimes it will be convenient to consider some other additional secondary variable. And, as always, we must assess the benefit-cost-risk ratio. The fact that the conclusion of the SR seems valid does not mean that we have to apply it in a compulsory way.

If you want to correctly evaluate a SR without forgetting any important aspect, I recommend you to use a checklist such as PRISMA’s or some of the tools available on the Internet, such as the grills that can be downloaded from the CASP page, which are the ones we have used for everything we have said so far.

PRISMA statement

The PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyzes) consists of 27 items, classified in 7 sections that refer to the sections of title, summary, introduction, methods, results, discussion and financing:

  1. Title: it must be identified as SR, meta-analysis or both. If it is specified, in addition, that it deals with clinical trials, priority will be given to other types of reviews.
  2. Summary: it should be a structured summary that should include background, objectives, data sources, inclusion criteria, limitations, conclusions and implications. The registration number of the revision must also be included.
  3. Introduction: includes two items, the justification of the study (what is known, controversies, etc) and the objectives (what question tries to answer in PICO terms of the structured clinical question).
  4. Methods. It is the section with the largest number of items (12):

– Protocol and registration: indicate the registration number and its availability.

– Eligibility criteria: justification of the characteristics of the studies and the search criteria used.

– Sources of information: describe the sources used and the last search date.

– Search: complete electronic search strategy, so that it can be reproduced.

– Selection of studies: specify the selection process and inclusion’s and exclusion’s criteria.

– Data extraction process: describe the methods used to extract the data from the primary studies.

– Data list: define the variables used.

– Risk of bias in primary studies: describe the method used and how it has been used in the synthesis of results.

– Summary measures: specify the main summary measures used.

– Results synthesis: describe the methods used to combine the results.

– Risk of bias between studies: describe biases that may affect cumulative evidence, such as publication bias.

– Additional analyzes: if additional methods are made (sensitivity, metaregression, etc) specify which were pre-specified.

  1. Results. Includes 7 items:

– Selection of studies: it is expressed through a flow chart that assesses the number of records in each stage (identification, screening, eligibility and inclusion).

– Characteristics of the studies: present the characteristics of the studies from which data were extracted and their bibliographic references.

– Risk of bias in the studies: communicate the risks in each study and any evaluation that is made about the bias in the results.

– Results of the individual studies: study data for each study or intervention group and estimation of the effect with their confidence interval. The ideal is to accompany it with a forest plot.

– Synthesis of the results: present the results of all the meta-analysis performed with the confidence intervals and the consistency measures.

– Risk of bias between the subjects: present any evaluation that is made of the risk of bias between the studies.

– Additional analyzes: if they have been carried out, provide the results of the same.

  1. Discussion. Includes 3 items:

– Summary of the evidence: summarize the main findings with the strength of the evidence of each main result and the relevance from the clinical point of view or of the main interest groups (care providers, users, health decision-makers, etc.).

– Limitations: discuss the limitations of the results, the studies and the review.

– Conclusions: general interpretation of the results in context with other evidences and their implications for future research.

  1. Financing: describe the sources of funding and the role they played in the realization of the SR.

As a third option to these two tools, you can also use the aforementioned Cochrane’s Handbook for Systematic Reviews of Interventions, available on its website and whose purpose is to help authors of Cochrane’s reviews to work explicitly and systematically.

We’re leaving…

As you can see, we have not talked practically anything about meta-analysis, with all its statistical techniques to assess homogeneity and its fixed and random effects models. And is that the meta-analysis is a beast that must be eaten separately, so we have already devoted two post only about it that you can check when you want. But that is another story…

You have to know what you are looking for

Critical appraisal of diagnostic studies

Every day we find articles that show new diagnostic tests that appear to have been designed to solve all our problems. But we should not be tempted to pay credit to everything we read before reconsidering what we have, in fact, read. At the end of the day, if we paid attention to everything we read we would be swollen from drinking Coca-Cola.

We know that a diagnostic test is not going to say whether or not a person is sick. Its result will only allow us to increase or decrease the probability that the individual is sick or not so we can confirm or rule out the diagnosis, but always with some degree of uncertainty.

Anyone has a certain risk of suffering from any disease, which is nothing more than the prevalence of the disease in the general population. Below a certain level of probability, it seems so unlikely that the patient is sick that we leave him alone and do not do any diagnostic tests (although some find it hard to restrain the urge to always ask for something). This is the diagnostic or test threshold.

But if, in addition to belonging to the population, one has the misfortune of having symptoms, that probability will increase until this threshold is exceeded, in which the probability of presenting the disease justifies performing diagnostic tests. Once we have the result of the test that we have chosen, the probability (post-test probability) will have changed. It may have changed to less and it has been placed below the test threshold, so we discard the diagnosis and leave the patient alone again. It may also exceed another threshold, the therapeutic, from which the probability of the disease reaches the sufficient level so as not to need further tests and to be able to initiate the treatment.

Usefulness of diagnostic test

The usefulness of the diagnostic test will be in its ability to reduce the probability below the threshold of testing (and discard the diagnosis) or, on the contrary, to increase it to the threshold at which it is justified to start treatment. Of course, sometimes the test leaves us halfway and we have to do additional tests before confirming the diagnosis with enough security to start the treatment.

Diagnostic tests studies should provide information about the ability of a test to produce the same results when performed under similar conditions (reliability) and about the accuracy with which the measurements reflect that measure (validity). But they also give us data about their discriminatory power (sensitivity and specificity), their clinical performance (positive predictive value and negative predictive value), its ability to modify the probability of illness and change our position between the two thresholds (likelihood ratios), and about other aspects that allow us to assess whether it’s worth to test our patients with the diagnostic test. And to check if a study gives us the right information we need to make a critical appraisal and read the paper based on our three pillars: validity, relevance and applicability.

Let’s go with the critical appraisal of diagnostic studies

Let’s start with VALIDITY. First, we’ll make ourselves some basic eliminating questions about primary criteria about the study. If the answer to these questions is no, the best you can do probably is to use the article to wrap your mid-morning snack.

Was the diagnostic test blindly and independently compared with an appropriate gold standard or reference test?. We must review that results of reference test were not interpreted differently depending on the results of the study test, thus committing an incorporation bias, which could invalidate the results. Another problem that can arise is that the reference test results are frequently inconclusive. If we made the mistake of excluding that doubtful cases we’d commit and indeterminate exclusion bias that, in addition to overestimate the sensitivity and specificity of the test, will compromise the external validity of the study, whose conclusions would only be applicable to patients with indeterminate result.

Do patients encompass a similar spectrum to which we will find in our practice?. The inclusion criteria of the study should be clear, and the study must include healthy and diseased with varying severity or progression stages of disease. As we know, the prevalence influences the clinical performance of the test so if it’s validated, for example, in a tertiary center (the probability of being sick is statistically greater) its diagnostic capabilities will be overestimated when we use the test at a Primary Care center or with the general population (where the proportion of diseased will be lower).

At this point, if we think it’s worth reading further, we’ll focus on secondary criteria, which are those that add value to the study design. Another question to ask is: had the study test’s results any influence in the decision to do the reference test?. We have to check that there hasn’t been a sequence bias or a diagnostic verification bias, whereby excluding those with negative test. Although this is common in current practice (we start with simple tests and perform the more invasive ones only in positive patients), doing so in a diagnostic test study affect the validity of the results. Both tests should be done independently and blindly, so that the subjectivity of the observer does not influence the results (review bias). Finally, is the method described with enough detail to allow its reproduction?. It should be clear what is considered normal and abnormal and what criteria we have used to define normal and how we have interpreted the results of the test.

Having analyzed the internal validity of the study we’ll appraise the RELEVANCE of the presented data. The purpose of a diagnostic study is to determine the ability of a test to correctly classify individuals according to the presence or absence of disease. Actually, and to be more precise, we want to know how the likelihood of being ill increases after the test’s result (post-test probability). It’s therefore essential that the study gives information about the direction and magnitude of this change (pretest / posttest), that we know depends on the characteristics of the test and, to a large extent, on the prevalence or pretest probability.

Do the work present likelihood ratios or is it possible to calculate them from the data?. This information is critical because if not, we couldn’t estimate the clinical impact of the study test. We have to be especially careful with tests with quantitative results in which the researcher has established a cutoff of normality. When using ROC curves, it is usual to move the cutoff to favor sensitivity or specificity of the test, but we must always appraise how this measure affects the external validity of the study, since it may limit its applicability to a particular group of patients.

How reliable are the results?. We will have to determine whether the results are reproducible and how they can be affected by variations among different observers or when retested in succession. But we have not only to assess the reliability, but also how accurate the results are. The study was done on a sample of patients, but it should provide an estimate of their values in the population, so results should be expressed with their corresponding confident intervals.

The third pillar in critical appraising is that of APLICABILITY or external validity, which will help us to determine whether the results are useful to our patients. In this regard, we ask three questions. Is the test available and is it possible to perform it in our patients?. If the test is not available all we’ll have achieved with the study is to increase our vast knowledge. But if we can apply the test we must ask whether our patients fulfill the inclusion and exclusion criteria of the study and, if not, consider how these differences may affect the applicability of the test.

The second question is if we know the pretest probability of our patients. If our prevalence is very different from that of the study the actual usefulness of the test can be modified. One solution may be to do a sensitivity analysis evaluating how the study results would be modified after changing values of pre and posttest probability to a different ones that are clinically reasonable.

Finally, we should ask ourselves the most important question: can posttest probability change our therapeutic attitude, so being helpful to the patient?. For example, if the pretest probability is very low, probably the posttest probability will be also very low and won’t reach the therapeutic threshold, so it would be not worth spending money and effort with the test. Conversely, is pretest probability is very high it may be worth starting treatment without any more evidence, unless the treatment is very expensive or dangerous. As always, the virtue will be in the middle and it will be in these intermediate areas where more benefits can be obtained from the studied diagnostic test. In any case, we must never forget who our boss is (I mean the patient, not our boss at the office): you must not to be content only with studying the effectiveness or cost-effectiveness, but also consider the risks, discomfort, and patients preferences and the consequences that can lead to the performing of the diagnostic test.

If you allow me an advice, when critically appraising an article about diagnostic tests I recommend you to use the CASP’s templates, which can be downloaded from the website. They will help you make the critical appraising in a systematic and easy way.

A clarification to go running out: we must not confuse the studies of diagnostic tests with diagnostic prediction rules. Although the assessment is similar, the prediction rules have specific characteristics and methodological requirements that must be assessed in an appropriate way and that we will see in another post.

We’re leaving…

Finally, just say that everything we have said so far applies to the specific papers about diagnostic tests. However, the assessment of diagnostic tests may be part of observational studies such as cohort or case-control studies, which can have some peculiarity in the sequence of implementation and validation criteria of the study and reference test. But that’s another story…

The three pillars of wisdom

Critical appraisal overview

Surely all of us, with a greater frequency tan we would like, have found a small hole in our knowledge that made us doubt about the diagnostic or treatment steps to take with any of our patients. Following the usual practice, and trying to save time and effort, we have certainly asked to our closest colleagues, hoping that they solve the problem, avoiding us the need to deal with the dread PubMed (who said Google!?). As a last resort, we have consulted a medical book in a desperate attempt to get answers, but nor even the fattest books can free us from having to search on a database occasionally.

Steps in Evidence-Based Medicine

And in order to do it well, we should follow the five steps of Evidence-Based Medicine: formulating our question in a structured way (first step), doing our bibliographic search (second step) and critically appraise the articles we find and that we consider relevant to the theme (third step), ending with the last two steps that will be to combine what we have found with our experience and the preferences of the patient (fourth step) and to evaluate how it influences our performance (fifth step).

So we roll up our sleeves, make our structured clinical question, and enter PubMed, Embase or TRIP looking for answers. Covered in a cold sweat, we come up with the initial number of 15234 results and get the desired article that we hope to enlighten our ignorance with. But, even though our search has been impeccable, are we really sure we have found what we need?. Here it starts the arduous task of critically appraise the article to assess its actual utility to solve our problem.

This step, the third of the five we have seen and perhaps the most feared of all, is indispensable within the methodological flow of Evidence-Based Medicine. And this is so because all that glitters is not gold: even articles published in prestigious journals by well-known authors may have poor quality, contain methodological errors, have nothing to do with our problem or have errors in the way of analyzing or presenting the results, often in a suspiciously interested way. And this is not true because I say so, because there are even people who think that the best place to send 90% out of what is published is the trash can, regardless of whether the journal has impact fact or if the authors are more famous than Julio Iglesias (or his son Enrique, for that matter). Our poor excuse to justify our lack of knowledge about how to produce and publish scientific papers is that we are clinicians rather than researchers, and of course the same is often the case with journals reviewers, who overlook all the mistakes that clinicians make.

Thus, it is easy to understand that critical appraising is a fundamental step in order to take full advantage of the scientific literature, especially in an era in which information abounds but we have little time available to evaluate it.

Critical appraisal overview

The first thing we must do is always to assess whether the article answers to our question. This is usually the case if we have developed the clinical question correctly and we have done a good search of the available evidence but, anyway, we should always check that the study population, the intervention, etc., match with what we are seeking.

Before entering into the systematic of critically appraising, we will take a look over the document and its summary to try to see if the article in question can meet our expectations. The first step we must always take is to evaluate whether the paper answers our question. This is often the case if we have correctly elaborated the structured clinical question and we have made a good search for the available evidence, but it is always appropriate to check that the type of population, study, intervention, etc. are in line with what we are looking for.

Once we are convinced that the article is what we need, we will perform a critical appraising. Although the details depend on the type of study design, we are always based on three basic pillars: validity, relevance and applicability.


Appraising validity consist on checking the scientific rigor of the paper to find out how much close to the true it is. There are a number of common criteria to all studies, such as the correct design, an adequate population, the existence of homogeneous intervention and control groups at the beginning of the study, a proper monitoring, etc. Someone thought this term should be best called internal validity, so we can find it with this name.


The second pillar is clinical importance, which measures the magnitude of the effect found. Imagine that a new hypotensive is better than the usual one with a p-value with many zeroes, but that it decrease blood pressure an average of 5 mmHg. No matter how many zeroes the p-value have (which is statistically significant, we cannot denied it), we have to admit that the clinical effect is rather ridiculous.


The last pillar is the clinical applicability, which consist in assessing whether the context, patients and intervention of the study are sufficiently similar to our environment as to generalize the results. The applicability is also known as external validity.

Not all scientific papers can be described favorably in these three aspects. It may happen that a valid study (internal validity) finds a significant effect that cannot be applied to our patients. And we must not forget that we are using just a working tool. Even the most suitable study must be appraise in terms of benefits, harms and costs, and patient preferences, the latter aspect one that we forget more often than it would be desirable.

We’re leaving…

For those with a fish memory, there are some templates by group CASP that are recommended to use as a guide to make critical reading without forgetting any important aspect. Logically, the specifics measures of association and impact and the requirements to meet internal validity criteria depend specifically on the type of the study design that we are dealing with. But that’s another story…

To what do you attribute it?

It seems like only yesterday. I began my adventures at the hospital and had my first contacts with The Patient. And, by the way, I didn’t know much about diseases but I knew without thinking about it what were the three questions with which any good clinical history began: what is bothering you?, how long has it been going on?, and to what do you attribute it?.

The fact is that the need to know the why of things is inherent to human nature and, of course, is of great importance in medicine. Everyone is mad for establishing cause and effect relations; sometimes one does it rather loosely and comes to the conclusion that the culprit of his summer’s cold is the supermarket’s guy, who has set the air conditioned at maximal power. This is the reason why studies on etiology must be conducted and assessed with scientific rigour. For this reason and because when we talk about etiology we also refer to harm, including that derived from our own actions (what educated people call iatrogenic).

This is why studies on etiology/harm have similar designs. The clinical trial is the ideal choice and we can use it, for example, to know if a treatment is the cause of the patient’s recovery. But when we study risk factors or harmful exposures, the ethical principle of nonmaleficence prevent us to randomized exposures, so we have to resort to observational studies such us cohort studies or case-control studies, although the level of evidence provided by them will be smaller than that of the experimental studies.

To critically appraise a paper on etiology / harm, we’ll resort to our well-known pillars: validity, relevance and applicability.

First, we’ll focus on the VALIDITY or scientific rigour of the work, which should answer to the question whether the factor or intervention studied was the cause of the adverse effect or disease observed.

As always, we’ll asses a series of primary validity criteria. If these are not fulfilled, we’ll left the paper and devote ourselves to something else more profitable. The first is to determine whether groups compared were similar regarding to other important factors different from the exposure studied. Randomization in clinical trials provides that the groups are homogeneous, but we cannot count on it in the case of observational studies. The homogeneity of the two cohorts is essential and the study is not valid without it. One can always argue that has stratified the differences between the two groups or that has made a multivariate analysis to control for the effect of known confounders but, what about the unknown?. The same applies to case-control studies, much more sensitive to bias and confusion.

Have exposure and effect been assessed in the same way in all groups?. In clinical trials and cohort studies we have to check that the effect has had the same likelihood of appearance and of be detected in the two groups. Moreover, in case-control studies is very important to properly asses previous exposure, so we must investigate whether there is potential bias in data collection, such us recall bias (patients often remember symptoms better than healthy). Finally, we must consider if follow-up has been long enough and complete. Losses during the study, common in observational designs, can bias the results.

If we have answered yes to all the three questions, we’ll turn to consider secondary validity criteria. Study’s results have to be evaluated to determine whether the association between exposure and effect satisfies a reasonably evidence of causality.Hill_en One useful tool are the Hill’s criteria, which was a gentleman who suggested using a series of items to try to distinguish the causal or non-causal nature of an association. These criteria are: a) strength of association, represented by the risk ratio between exposure and effect, that we’ll consider shortly; b) consistency, which is reproducibility in populations or in different situations; c) specificity, which means that a cause produces a unique effect and no a multiple one; d) temporality: it’s essential that cause precedes the effect; e) biological gradient: the more intense the cause, the more intense the effect; f) plausibility: the relationship has to be logical according to our biological knowledge; g) coherence, the relationship should not be in conflict with other knowledge about disease or effect; h) experimental evidence, often difficult to obtain in humans for ethical reasons; and finally, i) analogy to other known situations. Although these are a quite-vintage criteria and some of them may be irrelevant (experimental evidence or analogy), they may serve as a guidance. The criterion of temporality would be a necessary one and would be well complemented with biological gradient, plausibility and coherence.

Another important aspect is to consider whether, apart from the intervention under study, both groups were treated similarly. In this type of study in which the double-blind is absent is where there is more risk of bias due to co-interventions, especially if these are treatments with a much greater effect than the exposure under study.

Regarding the RELEVANCE of the results, we must consider the magnitude and precision of the association between exposure and effect.

What was the strength of the association?. The most common measure of association is the risk ratio (RR), which can be used in trials and cohort studies. However, in case-control studies we don’t know the incidence of the effect (the effect has occurred when the study is conducted), so we used the odds ratio (OR). As we know, the interpretation of the two parameters is similar. Even the values of the two are similar when the frequency of the effect is very low. However, the greater the magnitude or frequency of the effect, the more different RR and OR are, with the peculiarity that the OR tends to overestimate the strength of the association when it is greater than 1 and underestimate it when it is less than 1. Anyway, these vagaries of OR will exceptionally modify the qualitative interpretation of the results.

It has to be kept in mind that a test is statistically significant for any value of OR or RR whose confidence interval does not include one, but observational studies have to be a little more demanding. Thus, in a cohort study we’ll like to see values greater than or equal to three for RR and equal than or greater than four in case-control studies.

Another useful parameter (in trials and cohort studies) is the difference in risks or incidence difference, which is a fancy way of calling our known absolute risk reduction (ARR), which allows us to calculate the NNT (or NNH, number needed to harm) parameter that best quantifies us the clinical significance of the association. Also, similar to the relative risk reduction (RRR), we have the attributable fraction in the exposed, which is the percentage of risk observed in the exposed that is due to exposure.

And, what is the accuracy of the results?. As we know, we’ll use our beloved confidence intervals, which serve to determine the accuracy of the parameter estimate in the population. It is always useful to have all these parameters, which must be included in the study or its calculation should be possible from the data provided by the authors.

Finally, we’ll asses the APPLICABILITY of the results to our clinical practice.

Are the results applicable to our patients?. Search to see if there are differences that advise against extrapolating results of the work to our environment. Also, consider what is the magnitude of the risk in our patients based on the results of the study and their characteristics. And finally, having all this information in mind, we must think about our working conditions, the choices we have and the patient’s preferences to decide whether to avoid or not the studied exposure. For example, if the magnitude of the risk is high and we have an effective alternative, the decision will be clear, but things are not always so simple.

As always, I advise you to use the resources available on the Internet, such as CASP’s, both the design-specific templates and the calculator to assess the relevance of the results.

Before concluding, let me clarify one thing. Although we’ve said we use RR in cohort studies and clinical trials and we use OR in case-control studies, actually we can use OR in any type of study (not so for RR, for which we must know the incidence of the effect). The problem is that ORs are somewhat less accurate, so we prefer to use RR and NNT whenever possible. However, OR is increasingly popular for another reason, its use in logistic regression models, which allow us to obtain estimates adjusted for confounding variables. But that’s another story…