  Individualizing treatment assignment can improve outcomes for diseases with
patient-to-patient variability in comparative treatment effects. When a
clinical trial demonstrates that some patients improve on treatment while
others do not, it is tempting to assume that treatment effect heterogeneity
exists. However, if variability in response is mainly driven by factors other
than treatment, investigating the extent to which covariate data can predict
differential treatment response is a potential waste of resources. Motivated by
recent meta-analyses assessing the potential of individualizing treatment for
major depressive disorder using only summary statistics, we provide a method
that uses summary statistics widely available in published clinical trial
results to bound the benefit of optimally assigning treatment to each patient.
We also offer alternate bounds for settings in which trial results are
stratified by another covariate. We demonstrate our approach using summary
statistics from a depression treatment trial. Our methods are implemented in
the rct2otrbounds R package, which is available at
https://github.com/ngalanter/rct2otrbounds .
