Background: Synthesis of data from multiple trials to obtain an overall estimate of clinical effectiveness is a well-established component of decision making. Treatment effects for time-to-event (TTE) data are often summarised using a single hazard ratio (HR) which may be derived from semi-parametric Cox proportional hazard or parametric survival models. The estimated HRs are then synthesised in pairwise or network meta-analyses (NMA). The HRs represent an 'average' of the HR over the observed study time period. If the HR varies markedly over time, a single HR may not be a useful measure of treatment effect for decision-making. Comparison of HRs may be misleading and confounded by differences in study duration.
Objectives: To compare and contrast five different approaches for individual participant data NMA of TTE data when comparison of HRs may not be appropriate.
Methods: We considered five approaches to modelling TTE data for synthesising treatment effects: piecewise exponential models; fractional polynomial models; the Royston-Parmar flexible parametric model; generalised gamma model; and explicit time-treatment effect interactions. Each approach was applied to publicly available trial data for non-small cell lung cancer reporting overall survival (1). Methods were compared in terms of clinical interpretability of assumptions, potential to apply prior clinical beliefs, potential for over-fitting, practicality of using the approach, fit to observed data and credibility of extrapolation.
Results: We fitted all models to the dataset successfully. All models fitted the data reasonably well with some variation, however there was important variation in the extrapolations of the survival curve and the interpretability of the modelling constraints.
Conclusions: Deciding on the right approach for evidence synthesis of TTE outcomes is not straightforward. It is important to consider a wide range of factors, not just model fit. A considered holistic approach to model selection, including consideration of prior belief, can improve decision making.
Patient or healthcare involvement: Evidence-based decision making can affect the interventions that are available on the NHS which can change the care that is delivered to patients resulting in cost savings for the NHS and better quality of treatment for patients.
1) Jansen BMC Med Res Meth 2011;11:61