  Subgroup analyses are common in epidemiologic and clinical research.
Unfortunately, restriction to subgroup members to test for heterogeneity can
yield imprecise effect estimates. If the true effect differs between members
and non-members due to different distributions of other measured effect measure
modifiers (EMMs), leveraging data from non-members can improve the precision of
subgroup effect estimates. We obtained data from the PRIME RCT of panitumumab
in patients with metastatic colon and rectal cancer from Project Datasphere(TM)
to demonstrate this method. We weighted non-Hispanic White patients to resemble
Hispanic patients in measured potential EMMs (e.g., age, KRAS distribution,
sex), combined Hispanic and weighted non-Hispanic White patients in one data
set, and estimated 1-year differences in progression-free survival (PFS). We
obtained percentile-based 95% confidence limits for this 1-year difference in
PFS from 2,000 bootstraps. To show when the method is less helpful, we also
reweighted male patients to resemble female patients and mutant-type KRAS (no
treatment benefit) patients to resemble wild-type KRAS (treatment benefit)
patients. The PRIME RCT included 795 non-Hispanic White and 42 Hispanic
patients with complete data on EMMs. While the Hispanic-only analysis estimated
a one-year PFS change of -17% (95% C.I. -45%, 8.8%) with panitumumab, the
combined weighted estimate was more precise (-8.7%, 95% CI -22%, 5.3%) while
differing from the full population estimate (1.0%, 95% CI: -5.9%, 7.5%). When
targeting wild-type KRAS patients the combined weighted estimate incorrectly
suggested no benefit (one-year PFS change: 0.9%, 95% CI: -6.0%, 7.2%). Methods
to extend inferences from study populations to specific targets can improve the
precision of estimates of subgroup effect estimates when their assumptions are
met. Violations of those assumptions can lead to bias, however.
