I've Been Framed! My #twitterstatslesson

The Gist:  The way in which one frames a drug or intervention may severely alter the way in which one perceives the most objective information.  Patients and physicians are similarly affected by framing bias (a cognitive bias, perhaps amenable to metacognition).  The use of absolute risks and terms rather than relative terms helps mitigate this bias.  Number needed to treat (NNT) and number needed to harm (NNH) are probably better ways of evaluating interventions and testing, even though these have limitations as well.  Bookmark theNNT.com.

I have no statistics background except a biostatistics class that's part of my MPH curriculum, and attempts to keep up with SMART EM and Dr. David Newman on EM:RAP's mini JC section.  I realized we're often not formally taught to interpret and integrate bits of statistics, although the USMLE Step 2 is now beginning to attempt testing this aspect.  Thus, this stuff is important both to trainees and clinicians.  As I've begun to learn to sort through evidence and literature, I've found that FOAM delivers.  One evening, I found following conversation regarding absolute versus relative risk among some world-renowned physicians dominated my Twitter feed (only parts are listed below):

@precordialthump @karimbrohi @broomedocstrials judged by ARR, trials applied by RRR
— Scott Weingart (@emcrit) September 11, 2012

Discrepancy worse for lower baseline risk: 1% -> 0.9%. NNT = 1000.Only RR is unchanged across populations. #twitterstatslesson :)
— Karim Brohi (@karimbrohi) September 11, 2012

X in your population lowers to 9%. ARR=1%, NNT=100. But baseline risk in my population 20%. X lowers to 18%. ARR=2%, NNT=50. @broomeddocs
— Karim Brohi (@karimbrohi) September 11, 2012

NNT a flawed measure of utility:@broomedocs Say intervention X reduces risk of death by 10%. Your population baseline risk of death = 10%.
— Karim Brohi (@karimbrohi) September 11, 2012

RT @precordialthump: @karimbrohi If baseline event rate different, RCT lacks external validity & doesn't apply to your population.[No...
— Karim Brohi (@karimbrohi) September 11, 2012

@precordialthump I take your point though. The problem is that RRR is amenable to spin by those on the dark side...
— Chris Nickson (@precordialthump) September 11, 2012

So, I began to think about my own shortcomings in understanding these statistical dilemmas and figures.  I found an older podcast by the St. Emlyn's group on iTunes under "Statistics 2" to be helpful in sorting out absolute and relative risks, as mentioned in the Twitter conversation.  Then, amazingly, I stumbled upon a paper on framing the following day via Emergency Medicine Abstracts.  Apparently, I had good reason to worry...

We frame things constantly, providing a context for information and a means of understanding the information.  Like most things, this has both positive and negative implications.  For example, when one is trying to admit a patient to a particular service, one might phrase things rather differently than when one tells a patient why you plan on discharging them.  Oftentimes we use paternalism or the legitimate best interest of the patient to justify these 'frames.'

However, pharmaceutical companies do this as well through well-crafted consumer (and provider) advertising.  For example, this advertisement states that Pradaxa (dabigatran) reduced stroke risk 35% more than warfarin.  Thus, individuals may believe that the drug will reduce their stroke risk by a similar amount.  However, the 35% reduction in stroke risk only existed for a subset of patients.  Patients and populations are heterogeneous so these numbers don't necessarily apply any given patient.  As a pretty well educated bunch, health care providers should be really good at detecting framing differences and less susceptible to this bias, right?  Wrong!

The Paper:  Perneger T,  Agoritsas T.  Doctors and Patients’ Susceptibility to Framing Bias: A Randomized Trial.  J Gen Intern Med 26(12):1411–7

• Sent questionnaires to physicians and patients recently discharged from the hospital asking the respondent to judge the efficacy of a new drug based on the information provided
• Information on the new and old drug was identical but presented in only one of the following ways:  
• Absolute mortality:  with the new drug, 4% died by the end of the study versus 6% for those who received the old drug
• Absolute survival: with the new drug, 96% lived versus 94% who lived with the old drug.   Least favorable reviews of the drug by both groups (Physicians 51.8%, patients 51.7%, p=0.98 between groups)
• Relative mortality reduction:  of those who received the old drugthose that got the new drug had their mortality reduced by 1/3.  Both patients and physicians presented with relative mortality reduction perceived the drug most favorably (Physicians 93.8%, patients 89.3% p=0.11 between groups).  The odds ratio (OR) of a respondent perceiving the drug more favorably with the information presented in this format compared with other formats was 4.40 (95% CI 3.05 – 6.34, p<0.001)
• All three presentations of risk.  with the new drug, 96% lived, 4% died versus 94% lived, 6% died  with the old drug, so those that got the new drug had their mortality reduced by 1/3
• Physicians were also tested on the NNT (50) and the relative survival extension.
• Doctors responded to the presentations with no statistical difference from the patients. OR of Doctor (versus patient) = 1.06 (95% CI 0.87 – 1.29, p = 0.55)
• The notion that information presented in relative terms rather than absolute terms is more favorably received is not new (1).

There is so much to learn from the ever-relevant FOAM world.

References:
1.  Malenka DJ, Baron JA, Johansen S, Wahrenberger JW, Ross JM. The framing effect of relative and absolute risk. Journal of general internal medicine. 1993;8(10):543-8.

#FOAMed - Twitter...More Than Bieber Fever

The Gist:  Social media like Twitter can have great utility for medical students, residents, and professionals and, in my opinion, encourages active learning. One has the option to listen or join in to conversations happening around the globe.  Check out this article and scroll to the bottom of this entry for practical/technical tips.

The Case (of the Twitter Convert):  I revel in existing as an efficiency machine.  This serves as my way of cultivating a professional/medical/student life while allowing time to maintain my personal life and interests.  I listen to podcasts and lectures at the gym and during commutes.  Similarly, I follow blogs by checking Feedly as I sip my morning coffee or on my iPhone Feedly app whenever I'm waiting on something. And then?  Then, I discovered Twitter.

I didn't intend to join Twitter.  In fact, I scoffed when a friend predicted that I would soon have a Twitter account and manage to "ruin" this popular culture haven of pop stars and foodie pictures with medical tweets.  He was right; however, I discovered I was in good company in the Emergency Medicine world.

It's a stream of continuous goodness!  Upon joining, it seemed as though I was sitting in the audience at the Society for Academic Emergency Medicine (SAEM) conference whilst seeing 35 patients a day in a rural Alabama family medicine clinic.  Attendees linked presentations and pictures while disseminating core messages from lectures.  Additionally, I gained an inside view of medical education.  The experience felt like cheating as I learned more about the residency and medical student education process.  Since that time, I've networked with medical providers and educators around the world.  I've virtually attended more conferences than I can count, participating in some such as the Social Media and Critical Care conference.  In fact, Neill et al, analyzed the impact of Twitter on the International Conference on Emergency Medicine 2012.  Questions are answered quickly and debates frequently occur.

Recommendations:

• Twitter Accounts:
• Some recommend two twitter accounts, one professional and one personal.  Others opt to attempt to strike a balance between professional and personal in one account.
• A twitter handle with one's real name is recommended over a pseudonym.  This also allows one to "claim" their online presence in a positive, professional manner.
• Don't Violate HIPPA...or come close - when in doubt, don't post something.
• Don't complain/be negative
• Comply with school/site social media policies
• Abstain from posting while on shift
• Everything is public, be on good behavior
• Tweet useful pearls!
• Interact

The Technicalities: 

• Start with @FOAMstarter.
• Follow favorite bloggers, podcasters, and medical journals
• Examples of medical journals: @EBmedicine, @AnnalsofEM, @BMJ_latest
• Examples of podcasters:  @emcrit, @embasic, @emergencypdx, @rfdsdoc, @ultrasoundpod
• Examples of bloggers: @sandnsurf, @precordialthump, @kane_guthrie, @eleytherius, @SonoSpot, @M_Lin, @andyneill 
• Miscellaneous: Tox-  @PharmERToxGuy, @poisonreview, @emlitofnote, @MDAware
• Check for conferences or other interests using the # (hashtag system), @ALIEMconf tweets major EM conferences
• Chime in/start conversations.  This engenders active learning and critical thinking about issues.
• Note bene: As always, stay professional.  Twitter may not be the best place for debates as 140 characters may lead to misinterpretation or too much effort for too little conversation of substance.
• Like anything you read - double check it for accuracy/reliability.

What about all this Hashtag, MT, RT, HT stuff?

• Hashtag = #.  This is a way of linking tweets together.
• #FOAMed = Used for sharing free Open Access Medical education (why this hashtag? Check this out)  
• RT = retweet. This shares content created by another user.
• MT = modified tweet. Similar to a retweet, this shares someone else's content with one's own spin on it.  One can shorten another user's tweet in order to do this in 140 characters.
• DM = direct message, which is private between two users.
• HT = hat tip/heard through. This is a way of attributing a link or reference to another user.