Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

Abstract

This is a repository copy of Partitioned survival and state transition models for healthcare decision making in oncology : where are we now?.

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma

“…Several published studies have compared survival estimates obtained by STMs and PSMs suggesting that these 2 approaches can give considerably different survival predictions. [12][13][14][37][38][39] Although STMs were considered to outperform PSMs, 7 they often had implementation challenges. In addition, the risk of progression was sometimes derived directly from time to progression data, 14,40 even though in the presence of competing events the hazard of progression itself does not determine the risk of progression.…”

“…STMs describe clinical pathways in terms of mutually exclusive health states that patients move through during the course of their disease. [5][6][7] In STMs, the proportion of patients occupying a health state at a certain time point is determined by transition probabilities explicitly considering the relationship between clinical events; therefore, model predictions are closely linked to biological and clinical processes and are based on a more direct use of information on prognostic intermediate endpoints. Although, for the trial follow-up period, STMs and PSMs are expected to give similar survival estimates because relationships between outcomes are represented within the data, STMs may provide more plausible extrapolations and improved transparency and allow more meaningful sensitivity analyses to be conducted.…”

“…Although, for the trial follow-up period, STMs and PSMs are expected to give similar survival estimates because relationships between outcomes are represented within the data, STMs may provide more plausible extrapolations and improved transparency and allow more meaningful sensitivity analyses to be conducted. 1,7 Partitioned survival analysis has faced considerable scrutiny over recent years as a result of its lack of theoretical underpinnings in modeling the disease process and consequent issues with extrapolating survival data. [8][9][10][11] To mitigate the limitations associated with PSMs and to assess uncertainty in OS extrapolations in decision models, the National Institute for Health and Care Excellence (NICE) recommends the use of STMs alongside PSMs.…”

“…[1][2][3] A recent review of modeling methods and NICE technology appraisals suggested that there has been a growing number of empirical studies applying STM approaches and that applications have become more sophisticated. 7,[12][13][14][15] The review also highlighted that earlier applications of STMs gave limited consideration to modeling postprogression survival data; nevertheless, if the timing of progression is related to prognosis, for example, early progressors have shorter survival than late progressors, then a naïve analysis of postprogression survival data may bias survival extrapolations. As a consequence, including a covariate in the postprogression survival model that describes the prognosis at the time of progression may improve the accuracy of STM predictions.…”

“…As a consequence, including a covariate in the postprogression survival model that describes the prognosis at the time of progression may improve the accuracy of STM predictions. 7 Given the importance of extrapolation methods for clinical and policy decision making, the objective of our study was to present an application of an STM using data from a clinical trial in multiple myeloma focusing on methods to evaluate the postprogression transition as a function of time to progression and its impact on postprogression survival and OS. To illustrate the impact of differences in structural assumptions, our study also compared survival outcomes obtained by the STM and the more conventional PSM approach.…”