In health technology assessment, decisions are based on complex cost-effectiveness
models which require numerous input parameters. When not all relevant estimates are
available the model may have to be simplified. Multi-parameter evidence synthesis combines
data from diverse sources of evidence which results in obtaining estimates required in
clinical decision-making that otherwise may not be available. We demonstrate how bivariate
meta-analysis can be used to predict an unreported estimate of a treatment effect enabling
implementation of a multi-state Markov model, which otherwise needs to be simplified. To
illustrate this, we used an example of cost-effectiveness analysis for docetaxel in
combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate
meta-analysis was used to model jointly available data on treatment effects on overall
survival and progression-free survival (PFS) to predict the unreported effect on PFS in a
study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS
enabled implementation of a three-state Markov model comprising of stable disease,
progressive disease and dead states, whilst lack of the estimate restricted the model to a
two-state model (with alive and dead states). The two-state and three-state models were
compared by calculating the incremental cost-effectiveness ratio (which was much lower in
the three-state model: £22,148 per QALY gained compared to £30,026 obtained from the
two-state model) and the expected value of perfect information (which increased with the
three-state model). The three-state model has the advantage of distinguishing surviving
patients who progressed from those who did not progress. Hence, the use of advanced
meta-analytic techniques allowed obtaining relevant parameter estimates to populate a
model describing disease pathway more appropriately, whilst helping to prevent valuable
clinical data from being discarded
