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Objective: We aimed to develop a meta-analytic model for evaluation ofpredictive biomarkers and targeted therapies, utilising data from digitalsources when individual participant data (IPD) from randomised controlledtrials (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 toevaluate predictive biomarkers. IPD were sourced from electronic healthrecords, using target trial emulation approach, or digitised Kaplan-Meiercurves. The model is illustrated using two examples; breast cancer with ahormone receptor biomarker, and metastatic colorectal cancer with the KirstenRat Sarcoma (KRAS) biomarker.  Results: The model developed allowed for estimation of treatment effects intwo subgroups of patients defined by their biomarker status. Effectiveness oftaxane did not differ in hormone receptor positive and negative breast cancerpatients. Epidermal growth factor receptor (EGFR) inhibitors were moreeffective than chemotherapy in KRAS wild type colorectal cancer patients butnot in patients with KRAS mutant status. Use of IPD reduced uncertainty of thesub-group specific treatment effect estimates by up to 49%.  Conclusion: Utilisation of IPD allowed for more detailed evaluation ofpredictive biomarkers and cancer therapies and improved precision of theestimates compared to use of AD alone.

Individual participant data from digital sources informed and improved  precision in the evaluation of predictive biomarkers in Bayesian network  meta-analysis