Analysis and Modeling to evaluate influenza vaccine responses

Current Influenza vaccines

SC = Seroconversion, TI = Titer Increase (D28/D0), n = individuals, j = Strains.

We sampled from the panel of heterologous strains from UGAFluVac to mimic different labs

The table shows the coefficient of variation for each outcome.

Our new method is worse (more variable)!

Create a universe of 50 possible heterologous strains with varying antigenic distances.
Create 10 lab panels by randomly sampling 9 strains and adding the homologous strain (distance of 0).
For each lab, generate 100 random individuals by simulating flu vaccine response titers from a model that shows linearly reduced response with increasing antigenic distance.

Now our new method is better. Hm…

With censored data, the current method looks artificially good.

Our proposed new method seems to be generally more robust.
If a good amount of censored data are present, the current method falsely under-estimates the uncertainty.
Our method also doesn’t properly handle the censored data (yet).
We need to update our method to properly deal with the censored values. Then we can do another comparison of our method and the current approach.