A tale of two endpoints

Some time ago, when Gardasil was still in clinical trials, I congratulated the Merck team for a product with 100% efficacy. After all, getting anything with 100% efficacy is a rare event, especially in drug/biologic development.

Apparently, that congratulations was a little too soon. Looks like Merck may have found a surrogate endpoint that their vaccine managed very well, but if you look at the important endpoint, the story doesn't look quite so rosy.

So, to be specific, Gardasil is marketed to protect against two strains of human pampilloma virus (HPV) that account for 70% of cervical cancer cases. (Types 16 and 18, for those keeping track.) Merck is going for 80% now by asking the FDA to add types 6 and 11 to the label.

Ed from Pharmalot notes that in clinical trials, among women that already have HPV, the vaccine reduces precancerous lesions (no time limit given) by 14%. For women that don't have HPV, the occurrence of precancerous lesions is reduced 46%. Presumably this is because the vaccine is ineffective against strains that already infect the body. Merck's spin engine is carefully worded to tout that 70%, even though that number is only of secondary importance. It's the 14% and 46% that really matter.

Addendum: I looked at the Gardasil PI, and they already mention 6 and 11. They also mention all other sorts of efficacy measures. The patient product information is less informative. My guess is Merck is overplaying the efficacy in their soundbites by shoving that 70% front and center, but its detractors are overplaying the gap between the 70% and the real story by shoving the 14% front and center.

I'm glad I'm a biostatistician, else I wouldn't be able to understand all this jockeying the numbers.

Simple, but so complex

So, in addition to statistics, I've been dabbling a little in fractal/chaos theory. Nothing serious, but enough to know that behind even the simplest functions there lies an amazing complex landscape. Who knew that z²+c could be so rich?

At any rate, I did all this stuff back in college, but in specializing I've forgotten most of it (except for the occasional admonition that it's often easy to confuse the complexity of dynamic systems for noise of a stochastic [random] system).

As I get older, it's become easier to lose the wonder. However, beneath every simple surface could be a world of complexity that will inspire a new round of curiosity.

Regulatory fallout from Tegenero's ill-fated TGN1412 trial

While biostatistics does not get used very much in early human clinical trials, any regulatory changes can have an effect on the practice. The EMEA has published new guidelines (pdf - in draft form, to be finalized after public comment and consultation with industry) about the conduct of Phase I trials for "high-risk" compounds. This comes in the wake of the infamous TGN1412 trial, in which a monoclonal antibody caused severe adverse reactions in all of the six otherwise healthy trial participants. (All six participants suffered multiple organ failure, along with gangrene. They will all probably contract and die of cancer within a few short years.)

The EMEA concluded that the trial was conducted in accordance with current regulations. These new recommendations are changes to avoid another similar disaster.

Among the recommendations:
- stronger pre-clinical data, and a stronger association between pre-clinical data and choice of dosing in humans (e.g. using minimal dose for biological activity), as opposed to the no observed adverse-event dose
- the use of independent data safety monitoring boards, along with well-defined stopping rules for subjects, cohorts, and trials
- well-defined provisions for dose-escalation
- increasing follow-up length for safety monitoring
- use of sites with appropriate medical facilities

(via Thomson Centerwatch)