COMPETING RISKS OR COMPOSITE ENDPOINT METHODS FOR MODELLING MULTIPLE ENDPOINTS FROM SURVIVAL DATA IN HEALTH ECONOMIC EVALUATION?

Purpose:

   The aim of this study is to explore the impact of different methods for modelling multiple endpoints from survival data on health economics’ modelling.

Method(s):

   Two methods for modelling multiple clinical endpoints from survival data are studied. If these events are combined, assuming that they are completely dependent, then modelling them as a composite endpoint (CEP) is an approach. Alternatively, considering the events as competing risks when modelling the survival data, accounting for the possible interdependence, does not require restrictive assumptions with respect to the interrelation between these events. There are underlying assumptions in each method about the interaction between these endpoints.

   The study’s central research component is a comparison of these approaches, together with how these would be applied in a decision model and how this can affect the final outcome. The cost effectiveness analysis, using a simple Markov model, is used as a case study. Using R software, the inversion method is performed to simulate data for two competing events from Weibull distribution and then conducting survival analysis to examine these various outcomes.

   Cumulative incidence function, derived from the related survivor functions, obtained from the different approaches of modelling the events, was the platform to estimate the transition probabilities. These were the inputs of the Markov cost-effectiveness model, from which the net benefits, generated from the natural disease history, were the main outcome to assess the divergences between the methods.

Result(s):

   When the events have a constant rate with time, exponentially distributed (a special case of Weibull), the CEP method does not affect the outcome. In the case, where events’ hazards vary with time at the same rate, the estimates obtained from the CEP method are very close to those that the competing risks method generates. However, the situation becomes more challenging if the events have different rates at which they occur. If the events’ rates have two opposite directions, the implications for the decision model that has used the survival modelling outcomes become compounded. 

Conclusion(s):

   The method used for modelling multiple endpoints from survival data can have an impact on the outcome of health economic evaluation that used the transition probabilities derived from these survival data.