  Statistical models for proteomics data often estimate protein fold changes
between two samples, A and B, as the average peptide intensity from sample A
divided by the average peptide intensity from sample B. Such average intensity
ratios fail to take full advantage of the experimental design which eliminates
unseen confounding variables by processing peptides from both samples under
identical conditions. Typically this structure is exploited through the
estimation of a protein ratio as the median ratio of matched peptide
intensities. This simple solution fails to account for a substantial missing
data bias which has led to the development of more sophisticated average
intensity models. Here we develop the first statistical model that accounts for
nonignorable missingness while utilizing peptide level matched pairs across
samples. Our simulation analysis shows that models which fail to utilize
peptide level ratios, su.er astonishing losses to accuracy with basic ANOVA
estimates having an average MSE 371% higher than median ratio estimates. In
turn, median ratio estimates have an average MSE 35% higher than our model
estimates. An analysis of breast cancer data reinforces these relationships and
shows that our model is capable of increasing the number of proteins estimated
by 22%.
