Just a relative risk is not enough to fully understand the implications of your findings. Sure, if you are an expert in a field, the context of that field will help you to assess the RR. But if ou are not, the context of the numerator and denominator is often lost. There are several ways to work towards that. If you have a question that revolves around group discrimination (i.e. questions of diagnosis or prediction) the RR needs to be understood in relation to other predictors or diagnostic variables. That combination is best assessed through the added discriminatory value such as the AUC improvement or even more fancy methods like reclassification tables and net benefit indices. But if you are interested in are interested in a single factor (e.g. in questions of causality or treatment) a number needed to treat (NNT) or the Population Attributable Fraction can be used.
The PAF has been subject of my publications before, for example in these papers where we use the PAF to provide the context for the different OR of markers of hypercoagulability in the RATIO study / in a systematic review. This paper is a more general text, as it is meant to provide in insight for non epidemiologist what epidemiology can bring to the field of law. Here, the PAF is an interesting measure, as it has relation to the etiological fraction – a number that can be very interesting in tort law. Some of my slides from a law symposium that I attended addresses these questions and that particular Dutch case of tort law.
But the PAF is and remains an epidemiological measure and tells us what fraction of the cases in the population can be attributed to the exposure of interest. You can combine the PAF to a single number (given some assumptions which basically boil down to the idea that the combined factors work on an exact multiplicative scale, both statistically as well as biologically). A 2016 Lancet paper, which made huge impact and increased interest in the concept of the PAF, was the INTERSTROKE paper. It showed that up to 90% of all stroke cases can be attributed to only 10 factors, and all of them modifiable.
We had the question whether this was the same for young stroke patients. After all, the longstanding idea is that young stroke is a different disease from old stroke, where traditional CVD risk factors play a less prominent role. The idea is that more exotic causal mechanisms (e.g. hypercoagulability) play a more prominent role in this age group. Boy, where we wrong. In a dataset which combines data from the SIFAP and GEDA studies, we noticed that the bulk of the cases can be attributed to modifiable risk factors (80% to 4 risk factors). There are some elements with the paper (age effect even within the young study population, subtype effects, definition effects) that i wont go into here. For that you need the read the paper -published in stroke- here, or via my mendeley account. The main work of the work was done by AA and UG. Great job!
There are strange ambulances driving around in Berlin. They are the so-called STEMO cars, or Stroke EinsatzMobile, basically driving stroke units. They have the possibility to make a CT scan to rule out bleeds and subsequently start thrombolysis before getting to the hospital. A previous study showed that this descreases time to treatment by ~25 minutes. The question now is whether the patients are indeed better of in terms of functional outcome. For that we are currently running the B_PROUD study of which we recently published the design here.
I had the honor to be invited to the PHYSBE research group in Gothenburg, Sweden. I got to talk about the paradox of the BMI paradox. In the announcement abstract I wrote:
“The paradox of the BMI paradox” Many fields have their own so-called “paradox”, where a risk factor in certain instances suddenly seems to be protective. A good example is the BMI paradox, where high BMI in some studies seems to be protective of mortality. I will argue that these paradoxes can be explained by a form of selection bias. But I will also discuss that these paradoxes have provided researchers with much more than just an erroneous conclusion on the causal link between BMI and mortality.
I first address the problem of BMI as an exposure. Easy stuff. But then we come to index even bias, or collider stratification bias. and how selections do matter in a recurrence research paradox -like PFO & stroke- or a health status research like BMI- and can introduce confounding into the equation.
I see that the confounding might not be enough to explain all that is observed in observational research, so I continued looking for other reasons there are these strong feelings on these paradoxes. Do they exist, or don’t they?I found that the two sides tend to “talk in two worlds”. One side talks about causal research and asks what we can learn from the biological systems that might play a role, whereas others think with their clinical POV and start to talk about RCTs and the need for weight control programs in patients. But there is huge difference in study design, RQ and interpretation of results between the studies that they cite and interpret. Perhaps part of the paradox can be explained by this misunderstanding.
But the cool thing about the paradox is that through complicated topics, new hypothesis , interesting findings and strong feelings about the existence of paradoxes, I think that the we can all agree: the field of obesity research has won in the end. and with winning i mean that the methods are now better described, better discussed and better applied. New hypothesis are being generated and confirmed or refuted. All in all, the field makes progress not despite, but because the paradox. A paradox that doesn’t even exist. How is that for a paradox?
All in all an interesting day, and i think i made some friends in Gothenburg. Perhaps we can do some cool science together!
Good question, and even though thromboprofylaxis is already given according to guidelines in some countries, I can see the added value of a good discriminating prediction rule. Especially finding those patients with low DVT risk might be useful. But using d-dimer is a whole other question. To answer this, a thorough prediction model needs to be set up both with and without the information of d-dimer and only a direct comparison of these two models will provide the information we need.
In our view, that is not what the paper by Balogun et al did. And after critical appraisal of the tables and text, we found some inconsistencies that prohibits the reader from understanding what exactly was done and which results were obtained. In the end, we decided to write a letter to the editor, especially to prevent that other readers to mistakenly take over the conclusion of the authors. This conclusion, being that “D-dimer concentration with in 48 h of acute stroke is independently associated with development of DVT.This observation would require confirmation in a large study.” Our opinion is that the data from this study needs to be analysed properly to justify such an conclusion. One of the key elements in our letter is that the authors never compare the AUC of the model with and without d-dimer. This is needed as that would provide the bulk of the answer whether or not d-dimer should be measured. The only clue we have are the ORs of d-dimer, which range between 3-4, which is not really impressive when it comes to diagnosis and prediction. For more information on this, please check this paper on the misuse of the OR as a measure of interest for diagnosis/prediction by Pepe et al.
A final thing I want to mention is that our letter was the result of a mini-internship of one of the students at the Master programme of the CSB and was drafted in collaboration with our Virchow scholar HGdH from the Netherlands. Great team work!
My first conference experience (ISTH 2008, Boston) got me hooked on science. All these people doing the same thing, speaking the same language, and looking to show and share their knowledge. This is true when you are involved in the organisation. Organising the international soccer match at the Olympic stadium in Amsterdam linked to the ISTH 2013 to celebrate the 25th anniversary of the NVTH was fun. But lets not forget the exciting challenge of organising the WEON 2014.
And now, the birth of a new conference, the European Congress of Thrombosis and Hemostasis, which will be held in The Hague in Netherlands (28-30 sept 2016). I am very excited for several reasons: First of all, this conference will fill in the gap of the bi-annual ISTH conferences. Second, I have the honor to help out as the chair of the junior advisory board. Third, the Hague! My old home town!
So, we have 10 months to organise some interesting meetings and activities, primary focussed on the young researchers. Time to get started!
Lower changing incidences of disease over time do not necessarily mean that the number of patients in care also goes down, as the prevalence of the disease is a function of incidence and mortality. “Death Cures”. Combine this notion with the fact that both the incidence and mortality rates of the different stroke subtypes change different over time, and you will see that the group of patients that suffer from stroke will be quite different from the current one.
I made this picture to accompany a small text on declining stroke incidences which I have written for the newsletter of the Kompetenznetz Schlaganfall. which can be found in this pdf.