Decision Analytic Modelling Methods For The Assessing The Effectiveness Of Treatment Sequences For Clinical And Economic Decision Making: A Methodological Review

Abstract

Appropriate recognition of treatment sequencing is crucial to many policy decisions and related economic evaluations. The decision problem can become complex when accounting for extensive treatment sequences and pertinent factors relating to the disease. A methodological review was conducted to identify the breadth of decision analytic modelling approaches used for evaluating treatment sequences. A comprehensive literature review of MEDLINE, Embase and Cochrane library. Studies were categorised according to modelling approach and decision problem. Treatment sequencing assumptions were analysed. 70 studies and 48 discrete models were included. A wide range of modelling techniques were identified: cohort-based models (deterministic and stochastic decision trees, Markov, semi-Markov, partitioned survival); individual sampling models (state transition, and discrete event simulation (DES)); and open population-based models (DES and Markov cohort). No study systematically tested different modelling approaches for treatment sequences. Cohort models can be simple and easy to implement. Examples of cohort models adapted successfully to accommodate additional complexity were identified, but these were no longer simple. Individual sampling models are more sophisticated, better able to accommodate greater decision problem complexity, and provide more flexibility. DES appeared the optimum approach in terms of intuitively modelling sequencing algorithms, computational efficacy, and ease of updating, but requires more extensive modelling skills, specialist software, and is data and time intensive. In the absence of sequencing trials, modellers often applied simplifying assumptions to treatment effects obtained from trials of single treatments. These assumptions were frequently not validated, nor their impact assessed: an important limitation of these models. Modelling treatment sequences may require a complex model, which can be time consuming to develop and implement. Using simplistic assumptions, regarding sequencing effects, results in significant uncertainty around the effectiveness and cost-effectiveness estimates, the extent of which is generally unknown. This needs to be recognised in decision making, and further evaluated.