State-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions.
Data from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach.
The rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis.
The fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.