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!
A small group of epi-nerds (JLR, TK and myself) decided to start a colloquium on epidemiological methods. This colloquium series kicks off with a webcast of an event organised by the Society for Epidemiological Research (SER), but in general we will organize meetings focussed on advanced topics in epidemiological methods. Anyone interested is welcome. Regularly meetings will start in February 2017. All meetings will be held in English.
More information on the first event can be found below or via this link:
“Perspective of relative versus absolute effect measures” via SERdigital
Date: Wednesday, November 16th 2016 Time: 6:00pm – 9:00pm
Location: Seminar Room of the Neurology Clinic, first floor (Alte Nervenklinik)
Bonhoefferweg 3, Charite Universitätsmedizin Berlin- Campus Mitte, 10117 Berlin
Join us for a live, interactive viewing party of a debate between two leading epidemiologists, Dr. Charlie Poole and Dr. Donna Spiegelman, about the merits of relative versus absolute effect measures. Which measure of effect should epidemiologists prioritize? This digital event organized by the Society for Epidemiologic Research will also include three live oral presentations selected from submitted abstracts. There will be open discussion with other viewers from across the globe and opportunities to submit questions to the speakers. And since no movie night is complete without popcorn, we will provide that, too! For more information, see: https://epiresearch.org/ser50/serdigital
If you plan to attend, please register (space limited): https://goo.gl/forms/3Q0OsOxufk4rL9Pu1
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
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!
Slides can be found here.