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During drug development, evidence can emerge to suggest a treatment is moreeffective in a specific patient subgroup. Whilst early trials may be conductedin biomarker-mixed populations, later trials are more likely to enrolbiomarker-positive patients alone, thus leading to trials of the same treatmentinvestigated in different populations. When conducting a meta-analysis, aconservative approach would be to combine only trials conducted in thebiomarker-positive subgroup. However, this discards potentially usefulinformation on treatment effects in the biomarker-positive subgroup concealedwithin observed treatment effects in biomarker-mixed populations. We extendstandard random-effects meta-analysis to combine treatment effects obtainedfrom trials with different populations to estimate pooled treatment effects ina biomarker subgroup of interest. The model assumes a systematic difference intreatment 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-negativepatients in biomarker-mixed studies are used to interpolate treatment effectsin the biomarker-positive subgroup from observed treatment effects in thebiomarker-mixed population. The developed methods are applied to anillustrative example in metastatic colorectal cancer and evaluated in asimulation study. In the example, the developed method resulted in improvedprecision of the pooled treatment effect estimate compared to standardrandom-effects meta-analysis of trials investigating only biomarker-positivepatients. The simulation study confirmed that when the systematic difference intreatment effects between biomarker subgroups is not very large, the developedmethod can improve precision of estimation of pooled treatment effects whilemaintaining low bias.

Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations