Statistics from Altmetric.com
Archimedes seeks to assist practising clinicians by providing “evidence based” answers to common questions that are not at the forefront of research but are at the core of practice (format adapted from BestBETs published in the EmergencyMedicine Journal). A full description of the format is available online at http://adc.bmj.com/ifora/archimedes.dtl.
Readers wishing to submit their own questions—with best evidence answers—are encouraged to review those already proposed at www.bestbets.org. If your question still hasn’t been answered, feel free to submit your summary according to the instructions for authors at http://adc.bmj.com/ifora/archimedes.dtl.
Risk versus prognostic factors
The separation of “risk” factors and “prognostic” factors at first seems the sort of obsessive fine detail that gives epidemiologists and statisticians a bad name. Sadly, the difference is actually worth understanding for any clinician who is going to try to cut through an observational study and understand what it might be truthfully telling us. (This isn’t the true of the difference between a Peto odds ratio meta-analysis and a DerSimion and Laird random effects meta-analysis – that is a pointlessly academic difference.) Fortunately, the difference between risk and prognostic factors is straight forward.
“Risk” factors are those which are associated with causing a condition (like smoking for lung cancer, being premature for chronic lung disease, or soft light and wine for falling in love). “Prognostic” factors are those which, in people who have the condition, influence the outcome (like the resectability of a tumour for lung cancer, duration of intubation for chronic lung disease, or an unhealthy joint interest in home furnishings for staying in love).
Risk factors are determined by looking at things that influence new cases (“incident” cases), whereas prognostic factors can only be determined by following up people who already have the disease. The two things are frequently similar (eg, 24/40 are often intubated for longer and have more chronic lung disease) but may be strikingly different (eg, those who fall in love by candlelight are not much more likely to stay together than those whose relationship began with florescent overheads). When you’re reading, it’s worth keeping this in mind to untangle those factors which might make a difference in stopping something happening, and those which you may use to modify the intensity of your treatments.
Dr Steven Oliver, HYMS, for the love-related inspiration for this article.
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