OBJECTIVES:

The last few years have seen the emergence of promising targeted therapies in oncology leading to an increased potential number of total lines of therapy (LOT) in a sequence. Thus, it is increasingly important to develop economic models of treatment sequence, especially for the early-stage oncology indications. This research aims to summarize the existing treatment sequence models in early oncology, highlighting patterns regarding modelling approaches and key methodological challenges.

METHODS:

We conducted targeted literature searches in MEDLINE, Embase and Cochrane databases via the OVID platform, with no time limit, up to May 2022. Included studies reported treatment sequence models in early-stage oncology, explicitly modelling the efficacy of each LOT.

RESULTS:

Of 538 identified articles, eight were included and all of them conducted cost-effectiveness analysis in early-stage hematology (multiple myeloma, leukemia and lymphoma). Five studies used a cohort state-transition Markov technique, two used a patient-level simulation, and the remaining one applied a decision tree method. Half of the models defined health states by disease progression status, two by time on treatment, and two others used a combination of both. Clinical trials and real-world evidence were the primary sources of evidence in these studies. Efficacy was derived from multiple data sources in all eight models, and indirect treatment comparisons were conducted in three studies. Efficacy adjustments accounting for the sequence position of a LOT were not conducted in any of the studies, and no treatment-free intervals (TFIs) between treatment lines were considered in these models.

CONCLUSIONS:

Early-stage oncology treatment sequence models are generally modelled using a cohort state-transition Markov model with health states defined by disease progression. However, primarily owing to a paucity of clinical or real-world evidence, most models require a synthesis of data from multiple studies, and do not account for TFIs nor efficacy adjustments.