CHE's latest research paper - Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data

Abstract

Objectives: Network meta-analysis (NMA) methods extend the standard pair-wise framework to allow simultaneous comparison of multiple interventions in a single statistical model. Despite published work on NMA mainly focussing on the synthesis of aggregate data (AD), methods have been developed that allow the use of individual patient-level data (IPD) specifically when outcomes are dichotomous or continuous. This paper focuses on the synthesis of IPD and AD time to event data, motivated by a real data example looking at the effectiveness of high compression treatments on the healing of venous leg ulcers.

Methods: This paper introduces a novel NMA modelling approach that allows IPD (time to event with censoring) and AD (event count for a given follow-up time) to be synthesised jointly by assuming an underlying, common, distribution of time to healing. Alternative model assumptions were tested within the motivating example. Model fit and adequacy measures were used to compare and select models.

Results: Due to the availability of IPD in our example we were able to use a Weibull distribution to describe time to healing; otherwise, we would have been limited to specifying a uniparametric distribution. Absolute effectiveness estimates were more sensitive than relative effectiveness estimates to a range of alternative specifications for the model.

Conclusions: The synthesis of time to event data considering IPD provides modelling flexibility, and can be particularly important when absolute effectiveness estimates, and not just relative effect estimates, are of interest. CHE Research Paper 95 (PDF , 967kb)

Other papers in this series can be found at CHE Research Papers‌