As [Tara](http://scienceblogs.com/aetiology/2006/10/aids_and_viral_load.php), [Nick](http://aidsmyth.blogspot.com/2006/09/viral-load-paradigm-shift-not-really.html), and [Orac](http://scienceblogs.com/insolence/2006/10/more_distortion_of_peerreviewed_data_by.php) have already discussed, there’s been a burst of
activity lately from the HIV denialist crowd, surrounding [a new paper](http://jama.ama-assn.org/cgi/content/full/296/12/1498) studying the correlation between viral loads and onset and progression of symptoms in AIDS. For example, Darin Brown, allegedly a mathematician (and recently a troll in the comments here on GM/BM), has [written](http://barnesworld.blogs.com/barnes_world/2006/10/it_must_be_jell.html):
>Even if one is willing to endure the intellectual contortions necessary to
>reconcile these findings with the HIV/AIDS hypothesis, it is impossible to deny
>that they are incompatible with the justification for the treatment strategies
>advocated over the past 10 years.
>In case anyone was in a cave, for a decade, the treatment dogma has been:
>(1) CD4 counts and “viral load” are accurate predictors of progression to
>”AIDS” and death. In fact,
>(2) All three are correlated to each other. As viral loads go up, CD4 counts go
>down, and each indicates progression to “AIDS”. This is because HIV causes loss
>of CD4 cells. This is why they are called “surrogate markers”. This is why
>dozens and dozens of studies used viral load and CD4 counts as outcomes.
>Conversely, as viral load goes down, CD4 counts go up, and the patient is
>(3) If viral load goes up and CD4 counts go down sufficiently, you should go on
>ARVs immediately. Who knows how many healthy people have been put on these
>drugs on the basis of viral load and CD4 counts alone.
>The above 3 points have been drummed beyond belief over the past 10 years. For
>the AIDS establishment to deny now that this is what they have been saying all
>this time boggles the mind, but is not surprising.
When it comes to the science of it, I can’t contribute anything beyond what Tara and friends had to say. But the denialist argument around this is actually a classic example of one of my personal bugaboos concerning statistics. Details below the fold.
To briefly summarize the paper: they study the correlation between HIV viral load and onset and progression of AIDS symptoms. Like work before, the aggregate data for the population of infected people shows a *strong* correlation between viral load and symptoms. But in addition to looking at the aggregate data, they *also* looked at how well an *individual*’s viral load could be used *as a predictor* for the progression of the disease. The conclusion was that while, in the aggregate data, viral load shows a very strong correlation, in individuals, load is not a good predictor.
Naturally, the denialist crowd is all over this: after all, if the HIV viral load is *not* a good predictor of the onset of full-blown AIDS, then how can scientists credibly claim that AIDS is caused by HIV?
This is a perfect example of one of the most common errors in statistics. Statistics is looking at large collections of data in order to find patterns that appear *in the aggregate*. But statistics is about *aggregates*, not individuals. Reasoning from the aggregate back to the individual is error-prone *at best*. The predictive value of aggregate data does *not* translate back into predictive value about individuals.
It’s pretty easy to see why. Let’s take a really simple example. Suppose we’ve got a company with 100 employees: 50 of them make $20,000/year, 30 more make $30,000/year, 10 make $50,000/year, 5 make $100,000/year, and 5 make $200,000/year. So payroll for one year in this company is $3,900,000.
Suppose that for the next year those top five earners are given raises to $300,000; everyone else in the company gets a 5% raise.
The next years payroll is 4,545,000. From the *aggregate* data, we can easily see that the average raise was about 16%. Does that mean that we should be able to conclude that an average *individual* employee got anything close to a 16% raise? Obviously not – they got either 5% or 50%.
That’s *exactly* what the denialists are doing to this paper. They’re *claiming* that if you can’t use *aggregate* data as a predictor for *individual* outcome, that means that conclusions formed from the aggregate data *about the aggregate* must also be invalid.
As Orac points out, if you apply this reasoning to other medical studies, you’ll end up concluding that we know *nothing* about any diseases or their causes. You can’t even prove that *smoking* causes cancer: in the *aggregate*, smoking increases the risk of getting lung cancer very dramatically; in individuals, many (or even *most*) smokers won’t develop lung cancer. Blood serum cholesterol levels certainly shows a strong correlation with heart disease: high cholesterol definitely increases the risk of heart problems like heart attacks. But my great-grandfather had *incredibly* high cholesterol; he ate eggs for breakfast every morning, used schmaltz (chicken fat) as a condiment. But he lived to be *96* years old,and never had any heart trouble. But my father, who has moderately high cholesterol had to have a quad bypass two years ago, or he would have died.
Normal aggregate data *does not* have specific predictive value for
individuals. But that doesn’t mean that you can say that *in general* smoking increases your risk of getting cancer; or that high cholesterol *in general* increases your risk of heart disease. But the *specific* correlation to individuals isn’t necessarily correct: you can’t reason back from the aggregate
to the individual unless you take the additional step of showing *how well the data applies to individuals*. Working back from the aggregate to the individual, you’re always introducing a degree of uncertainty, and the only way to really understand that degree of uncertainty *is the measure it* by experimentation.
The study that we’re looking at was attempting to see whether or not the
aggregate data showed the same kind of *individual* correlation that it showed
in aggregate. They were doing exactly the kind of experiment that *any* good
scientist would do to figure out *how well* the aggregate data can be applied to
individuals. Disappointingly, the aggregate data does *not* form a particularly good predictor for individuals.
So.. Going back to Darin’s rant. Does that mean that the standard medical practice WRT HIV/AIDS – the practice of basing decisions about when to start anti-retroviral therapy on viral load – are wrong? No. No more than my doctor was wrong to advise *me* to change my diet to get my cholesterol down.
Viral load isn’t a great predictor of the onset of symptoms for individuals: it *can* cause us to start therapy too soon for some individuals (exposing them to the side effects of the medication); and it *can* cause us to delay starting therapy too *long* for some individuals (allowing them to develop symptoms sooner than they would have if they had taken the medication). But given our current level of knowledge, we *do* know that there is a very strong correlation between viral load and onset of symptoms *in the aggregate*.
Comparing to cholesterol/heart disease risk is quite informative. Some people with high cholesterol will *never* develop heart trouble; but we try to make them change their diet, and give them medication to try to lower it, even though those medications/diet changes *might not* be doing them any good. And some people who *don’t* have high cholesterol will die of heart attacks that could have been prevented by appropriate medication. What determines whether an individual will develop heart disease is a complex mix of many different factors, and we don’t even know what all of them are. But we use the aggregate data to determine the best estimate that we can of the optimal risk/benefit balance, and use that to determine when to start treating the problem.
Treating HIV is very much the same kind of thing. We have very good aggregate data showing that the viral load correlates with the onset of the disease. But there are many different factors involved in when an HIV infection turns into full-blown AIDS, and we don’t even know what all of them are. But we use the aggregate data to determine the best estimate of the optimal risk/benefit balance, and use it to determine when to start anti-retroviral treatment.
This *is* an extremely common statistical error. People frequently try to use statistics to reason from the aggregate to the specific. It doesn’t it work particularly well, except as a way to produce a very rough initial estimate; and it’s difficult to know *how* rough that initial estimate is without doing the experiments and the math to see.