Monday, June 4, 2012

Why "efficacy" does not equal "effectiveness"

As you may have noticed, one of the biggest medical conferences is in progress right now in Chicago -- the American Society of Clinical Oncology. It is the premier meeting of cancer doctors, where new data are the order of the day. This stream of data is reflected in a constant barrage of media coverage. One of the stories I saw today serves as a great illustration of the title of this post: why efficacy does not equal effectiveness. Although I do discuss this in my book, I thought it might be nice to apply these ideas to a current story.

The MedPage story opens with this:
Older patients with advanced cancers treated in the community had survival that fell short of that for patients who received the same regimens in clinical trials, analysis of a large government database showed.
Most of the differences were small in absolute terms, except for patients with stage IV colorectal cancer treated with the FOLFIRI chemotherapy regimen. Patients in the community had a median survival of 16.1 months, which was 30% lower than that of patients treated with the same regimen in a cooperative-group randomized clinical trial.
Moreover, survival among FOLFIRI-treated older patients was 4 months shorter than that of older patients treated with FOLFOX chemotherapy, Elizabeth B. Lamont, MD, reported here at the American Society of Clinical Oncology meeting. 
This is a common conundrum, and a poster child for the distinction between efficacy and effectiveness. So, what are these two "e"s? Efficacy is essentially a statistical distinction between the outcomes of interest among patients who undergo a particular treatment compared to those who do not, or those who get treated with a placebo. Efficacy is measured under the controlled circumstances of a clinical trial, where only very specific patients are enrolled, very specific treatments are administered, and very specific outcomes are monitored. These trials usually randomize patients to either the treatment or the placebo, thus ensuring that treated and untreated patients are the same in all ways save for the treatment under examination. Efficacy is frequently represented by what we call surrogate outcomes, such as laboratory measurements or radiographic tests. In cancer studies, for example, a frequent surrogate outcome that is used is so-called progression-free survival. This refers to the duration of time from treatment that a person is alive AND the tumor has not shown any evidence of growth on a scan. Another frequently used surrogate measure is blood pressure as a marker for heart disease. These are surrogates because, although correlated with such important outcomes as death and heart attacks, respectively, they themselves do not tell us with any precision how our interventions impact these ultimate measures. I will not belabor at this time why we rely on efficacy and surrogate outcomes, since I go into detail about that in the book.

Effectiveness, on the other hand, is something altogether different. Effectiveness tells us exactly what happens in the real messy world to outcomes that matter, such as death and quality of life, in conjunction with the treatment in question. We have known for a long time that the outcomes we see in naturalistic studies are often much less spectacular than those reported in RCTs of efficacy. Why is this? And more importantly, which do we believe? The second question is easier to answer than the first: we believe what happens in the real world, because it is precisely what happens in the real world rather than in the laboratory of clinical research that matters. As to why this difference exists, there are many reasons for this, most of which I have discussed elsewhere on this blog and in Between the Lines. Some of the reasons may have to do with patient selection, which in real life tends to be less restrictive than in RCTs. For example, individuals who are more ill may get the intervention that was intended to be given to those with lesser illness severity. In this population the intervention may not prove to be as effective as in those who are not as ill. This is called "confounding by indication," and we talked about it most recently here. Other reasons may be that other conditions patients have in real life, which tend to be excluded from RCT populations, attenuate the impact of the intervention. When looking at mortality in cancer, patients with end-stage heart disease may be excluded from the RCT, but treated in the wilds of clinical practice. And this treatment may give us a Pyrrhic victory, where cancer is indeed held at bay, but the patient dies of his heart disease. And here is yet another reason for the efficacy-effectiveness disconnect: attribution. In clinical trials there is a meticulous process that has to be followed in order to attribute the cause of death to a particular disease. In real life -- not so: death certificates are a notoriously dicey source of information on the causes of death.

So here are some of the challenges with applying RCT data to the real world, illustrated so palpably in the story we started out with. Please, do not misunderstand my message: I am not saying that RCTs are useless. What I am saying (I must sound like a broken record by now) is that we need different types of studies to see the whole picture. RCTs by their nature are exclusive undertakings whose findings are only narrowly translatable to the real world. Naturalistic observational data are key components of building the entire jigsaw puzzle of how our interventions really work.                

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