Tyler Cowen has argued that we can release vaccines now without compromising phase III trials through randomization. We could thus benefit from the expected value of innoculating more people earlier and of getting an answer sooner. He has proposed two mechanisms.
Randomly distribute treatments and placebos to at risk groups like bus drivers. This seems like a great idea, since bus drivers are in unusual danger and need.
Use a "tie-breaker" design, which is a hybrid of regression discontinuity and randomized control trial. Basically you want to treat some subset of the population, but want an impact assessment. So you randomize only near the cutoff for service. So we could vaccinate some of the most at-risk persons and randomize at the liminal cases, achieving an optimal tradeoff between present benefits and information. The abstract of Owen and Varians article is below.
For some reason, the US is currently implementing neither ideas. Our approach is to stockpile lots of vaccines and wait for a greenlight from a single conventional information-only trial. Cowen is mostly being ignored.
We can lobby the government and Pfizer to change this. The US gov has plenty of avenues for lobbying to force discussion on these ideas. Here are a few, off the top of my head.
- Tweet at public health experts in the style of 1 day sooner
- Call our senators, complain about FDA regulations
- Call our representatives, complain
- Go to our representatives office and demand to speak to the staff. Show him the paper. Demand a meeting.
- Call local television stations. Read the paper in detail and prepare a speech. Build publicity
- Tweet at Donald Trump directly
- Call the FDA
- Call think tanks affiliated with party leadership
I am uncertain which body needs to approve such a policy change. It could be mandatable from the white house, require legislation, be mandatable from the FDA, or be entirely under the pharma companies control. The easiest body to pressure is the FDA because they answer to our legislators.
Appendix A: Motivated by customer loyalty plans and scholarship programs, we study tie-breaker designs which are hybrids of randomized controlled trials (RCTs) and regression discontinuity designs (RDDs). We quantify the statistical efficiency of a tie-breaker design in which a proportion Δ of observed subjects are in the RCT. In a two line regression, statistical efficiency increases monotonically with Δ, so efficiency is maximized by an RCT. We point to additional advantages of tie-breakers versus RDD: for a nonparametric regression the boundary bias is much less severe and for quadratic regression, the variance is greatly reduced. For a two line model we can quantify the short term value of the treatment allocation and this comparison favors smaller Δ with the RDD being best. We solve for the optimal tradeoff between these exploration and exploitation goals. The usual tie-breaker design applies an RCT on the middle Δ subjects as ranked by the assignment variable. We quantify the efficiency of other designs such as experimenting only in the second decile from the top. We also show that in some general parametric models a Monte Carlo evaluation can be replaced by matrix algebra.