Note: This post is purely on statistics, and is long as I try to discuss somewhat technical issues.
(Via Social Sciences Statistics blog.)
This article in Wired (Aug 24,2009) is a must-read. It presents current research on the "placebo effect", that is, the observation that some patients show improvement if they believe they are being treated (say, with pills) even though they have received "straw men" (say, sugar pills) that have no therapeutic value.
The article is a great piece, and a terrible piece. It fascinated and frustrated me in equal measure. Steve Silberman did a good job bringing up an important topic in a very accessible way. However, I find the core arguments confused.
Let's first review the setting: in order to prove that a drug can treat a disease, pharmas are required by law to conduct "double-blind placebo-controlled randomized clinical trials". Steve did a great job defining these: "Volunteers would be assigned randomly to receive either medicine or a sugar pill, and neither doctor nor patient would know the difference until the trial was over." Those receiving real medicine is known as the treatment group, and those receiving sugar pills is the placebo control group. Comparing the two groups at the end of the trial allows us to establish the effect of the drug (net of the effect of believing that one is being treated).
(I have run a lot of randomized controlled tests in a business setting and so have experience interpreting such data. I have not, however, worked in the pharma setting so if you see something awry, please comment.)
Two key themes run through the article:
1) An increasing number of promising drugs are failing to prove their effectiveness. Pharmas suspect that this is because too many patients in the placebo control group are improving without getting the "real thing". They have secretly combined forces to investigate this phenomenon. The purpose of such research is "to determine which variables are responsible for the apparent rise in the placebo effect."
2) The placebo effect meant that patients could get better without getting expensive medicine. Therefore, studying this may help improve health care while lowering cost.
Theme #1 is misguided and silly, and of little value to patients. Theme #2 is worthwhile, even overdue, and of great value to patients. What frustrated me was that by putting these two together, not sufficiently delineating them, Steve allowed Theme #1 to borrow legitimacy from Theme #2.
To understand the folly of Theme #1, consider the following stylized example:
Effect on treatment group = Effect of the drug + effect of belief in being treated
Effect on placebo group = Effect of belief in being treated
Thus, the difference between the two groups = effect of the drug, since the effect of belief in being treated affects both groups of patients.
Say, if the treatment group came in at 15, and the placebo at 13, we say the effect of the drug = 15 - 13 = 2.
A drug fails because the effect of the drug is not high enough above the placebo effect. If you are the pharmas cited in this article, you describe this result as the placebo effect is "too high". Every time we see "placebo effect is too high", substitute "the effect of the drug is too low".
Consider a test of whether a fertilizer makes your plant grow taller. If the fertilized plant is the same height as the unfertilized plant, you would say the fertilizer didn't work. Who would conclude that the unfertilized plant is "unexpectedly tall"? That is what the pharmas are saying, and that is what they are supposedly studying as Theme #1. They want to know why the plant that grew on unfertilized soil was "so tall", as opposed to why the fertilizer was impotent. (One should of course check that the soil was indeed unfertilized as advertised.)
Take the above example where the effect on the placebo group was 13. Say, it "unexpectedly" increased by 10 units. Since the effect of the treatment group = effect of drug + effect of believing that one is treated, the effect of the treatment group also would go up by 10. Because both the treatment group and the control group believe they are being treated, any increase in the placebo effect would affect both groups equally, and leave the difference the same. This is why in randomized controlled tests, we focus on the difference in the metrics and don't worry about the individual levels. This is elementary stuff.
One of their signature findings is that some cultures may produce people who tend to show high placebo effects. The unspoken conclusion that we are supposed to draw is that if these trials were conducted closer to home, the drug would have been passed rather than failed. I have already explained why this is wrong as described... the higher placebo effect lifts the metrics on both the treatment and the control groups, leaving the difference the same.
There is one way in which cultural difference can affect trial results. This is if the effect of the drug is not common to all cultures; in other words, the drug is effective for Americans (say) but not so for Koreans (say). Technically, we say there is a significant interaction effect between the treatment and the cultural upbringing. Then, it would be wrong to run the trial in Korea and then generalize the finding to the U.S. Note that I am talking about the effect of the drug, not the effect of believing one is being treated (which is always netted out). To investigate this, one just needs to repeat the same trial in America; one does not need to examine why the placebo effect is "too high".
I have sympathy for a different explanation, advanced for psychiatric drugs. "Many experts are starting to wonder if what drug companies now call depression is even the same disease that the HAM-D [traditional criterion] was designed to diagnose". The idea is that as more and more people are being diagnosed as needing treatment, the average effect of the drug relative to placebo group gets smaller and smaller. This is absolutely possible: the marginal people who are getting diagnosed are those with lighter problems, and thus those who derive less value from the drug, in other words, could more easily get better via placebo. This is also elementary: in the business world, it is well known that if you throw discounts at loyal customers who don't need the extra incentive, all you are doing is increasing your cost without changing your sales.
No matter how the pharmas try, the placebo effect affects both groups and will always cancel out. Steve even recognizes this: "Beecher [who discovered the placebo effect] demonstrated that trial volunteers who got real medication were *also subject to placebo effects*." It is too bad he didn't emphasize this point.
On the other hand, Theme #2 is great science. We need to understand if we can harness the placebo effect. This has the potential of improving health care while at the same time reducing its cost. Of course, this is not so useful for pharmas, who need to sell more drugs.
I think it is not an accident that Theme #2 research, as cited by Steve, are done in academia while Theme #1 research is done by an impressive roster of pharmas, with the help of NIH.
The article also tells us some quite startling facts:
- if they tell us, they have to kill us: "in typically secretive industry fashion, the existence of the project [Theme #1] itself is being kept under wraps." Why?
- "NIH staffers are willing to talk about it [Theme #1] only anonymously, concerned about offending the companies paying for it."
- Eli Lilly has a database of published and unpublished trials, "including those that the company had kept secret because of high placebo response". Substitute: low effect of the drug. This is the publication bias problem.
- Italian doctor Benedetti studies "the potential of using Pavlovian conditioning to give athletes a competitive edge undetectable by anti-doping authorities". This means "a player would receive doses of a performance-enhancing drug for weeks and then a jolt of placebo just before competition." I hope he is on the side of the catchers not the cheaters.
- Learnt the term "nocebo" effect, which is when patients develop negative side effects because they were anticipating them
Again, highly recommended reading even though I don't agree with some of the material. Should have focused on Theme #2 and talk to people outside pharma about Theme #1.