  Objective: We aimed to develop a meta-analytic model for evaluation of
predictive biomarkers and targeted therapies, utilising data from digital
sources when individual participant data (IPD) from randomised controlled
trials (RCTs) are unavailable.
  Methods: A Bayesian network meta-regression model, combining aggregate data
(AD) from RCTs and IPD, was developed for modelling time-to-event data to
evaluate predictive biomarkers. IPD were sourced from electronic health
records, using target trial emulation approach, or digitised Kaplan-Meier
curves. The model is illustrated using two examples; breast cancer with a
hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten
Rat Sarcoma (KRAS) biomarker.
  Results: The model developed allowed for estimation of treatment effects in
two subgroups of patients defined by their biomarker status. Effectiveness of
taxane did not differ in hormone receptor positive and negative breast cancer
patients. Epidermal growth factor receptor (EGFR) inhibitors were more
effective than chemotherapy in KRAS wild type colorectal cancer patients but
not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the
sub-group specific treatment effect estimates by up to 49%.
  Conclusion: Utilisation of IPD allowed for more detailed evaluation of
predictive biomarkers and cancer therapies and improved precision of the
estimates compared to use of AD alone.
