Estimation of transition probabilities for state-transition models : a review of NICE appraisals

State transition models are used to inform health technology reimbursement decisions. Within state transition models, the movement of patients between the model health states over discrete time intervals is determined by transition probabilities (TPs). Estimating TPs presents numerous issues, including missing data for specific transitions, data incongruence and uncertainty around extrapolation. Inappropriately estimated TPs could result in biased models. There is limited guidance on how to address common issues associated with TP estimation. To assess current methods for estimating TPs and to identify issues that may introduce bias, we reviewed National Institute for Health and Care Excellence Technology Appraisals published from 1 January, 2019 to 27 May, 2020. Twenty-eight models (from 26 Technology Appraisals) were included in the review. Several methods for estimating TPs were identified: survival analysis (n = 11); count method (n = 9); multi-state modelling (n = 7); logistic regression (n = 2); negative binomial regression (n = 2); Poisson regression (n = 1); and calibration (n = 1). Evidence Review Groups identified several issues relating to TP estimation within these models, including important transitions being excluded (n = 5); potential selection bias when estimating TPs for post-randomisation health states (n = 2); issues concerning the use of multiple data sources (n = 4); potential biases resulting from the use of data from different populations (n = 2), and inappropriate assumptions around extrapolation (n = 3). These issues remained unresolved in almost every instance. Failing to address these issues may bias model results and lead to sub-optimal decision making. Further research is recommended to address these methodological problems.