During drug development, evidence can emerge to suggest a treatment is more
effective in a specific patient subgroup. Whilst early trials may be conducted
in biomarker-mixed populations, later trials are more likely to enrol
biomarker-positive patients alone, thus leading to trials of the same treatment
investigated in different populations. When conducting a meta-analysis, a
conservative approach would be to combine only trials conducted in the
biomarker-positive subgroup. However, this discards potentially useful
information on treatment effects in the biomarker-positive subgroup concealed
within observed treatment effects in biomarker-mixed populations. We extend
standard random-effects meta-analysis to combine treatment effects obtained
from trials with different populations to estimate pooled treatment effects in
a biomarker subgroup of interest. The model assumes a systematic difference in
treatment effects between biomarker-positive and biomarker-negative subgroups,
which is estimated from trials which report either or both treatment effects.
The estimated systematic difference and proportion of biomarker-negative
patients in biomarker-mixed studies are used to interpolate treatment effects
in the biomarker-positive subgroup from observed treatment effects in the
biomarker-mixed population. The developed methods are applied to an
illustrative example in metastatic colorectal cancer and evaluated in a
simulation study. In the example, the developed method resulted in improved
precision of the pooled treatment effect estimate compared to standard
random-effects meta-analysis of trials investigating only biomarker-positive
patients. The simulation study confirmed that when the systematic difference in
treatment effects between biomarker subgroups is not very large, the developed
method can improve precision of estimation of pooled treatment effects while
maintaining low bias