Impact of Limited Sample Size and Follow-up on Partitioned Survival and Multistate Modeling-Based Health Economic Models: A Simulation Study

Background Economic models often require extrapolation of clinical time-to-event data for multiple events. Two modeling approaches in oncology that incorporate time dependency include partitioned survival models (PSM) and semi-Markov decision models estimated using multistate modeling (MSM). The objective of this simulation study was to assess the performance of PSM and MSM across datasets with varying sample size and degrees of censoring. Methods We generated disease trajectories of progression and death for multiple hypothetical populations with advanced cancers. These populations served as the sampling pool for simulated trial cohorts with multiple sample sizes and various levels of follow-up. We estimated MSM and PSM by fitting survival models to these simulated datasets with different approaches to incorporating general population mortality (GPM) and selected best-fitting models using statistical criteria. Mean survival was compared with “true†population values to assess error. Results With near complete follow-up, both PSMs and MSMs accurately estimated mean population survival, while smaller samples and shorter follow-up times were associated with a larger error across approaches and clinical scenarios, especially for more distant clinical endpoints. MSMs were slightly more often not estimable when informed by studies with small sample sizes or short follow-up, due to low numbers at risk for the downstream transition. However, when estimable, the MSM models more commonly produced a smaller error in mean survival than the PSMs did. Conclusions Caution should be taken with all modeling approaches when the underlying data are very limited, particularly PSMs, due to the large errors produced. When estimable and for selections based on statistical criteria, MSMs performed similar to or better than PSMs in estimating mean survival with limited data. Highlights Caution should be taken with all modeling approaches when underlying data are very limited. Partitioned survival models (PSMs) can lead to significant errors, particularly with limited follow-up. Incorporating general population mortality (GPM) via internal additive hazards improved estimates of mean survival, but the effects were modest. When estimable, decision models based on multistate modeling (MSM) produced similar or smaller error in mean survival compared with PSM, but small samples or limited deaths after progression produce additional challenges for fitting MSMs; more research is needed to improve estimation of MSMs and similar state transition–based modeling methods with limited data. Future studies are needed to assess the applicability of these findings to comparative analyses estimating incremental survival benefits.