A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

Cost-effectiveness of endovascular treatment after 6-24 h in ischaemic stroke patients with collateral flow on CT-angiography: A model-based economic evaluation of the MR CLEAN-LATE trial

BACKGROUND

The MR CLEAN-LATE trial has shown that patient selection for endovascular treatment (EVT) in the late window (6-24 h after onset or last-seen-well) based on the presence of collateral flow on CT-angiography is safe and effective. We aimed to assess the cost-effectiveness of late-window collateral-based EVT-selection compared to best medical management (BMM) over a lifetime horizon (until 95 years of age).

MATERIALS AND METHODS

A model-based economic evaluation was performed from a societal perspective in The Netherlands. A decision tree was combined with a state-transition (Markov) model. Health states were defined by the modified Rankin Scale (mRS). Initial probabilities at 3-months post-stroke were based on MR CLEAN-LATE data. Transition probabilities were derived from previous literature. Information on short- and long-term resource use and utilities was obtained from a study using MR CLEAN-LATE and cross-sectional data. All costs are expressed in 2022 euros. Costs and quality-adjusted life years (QALYs) were discounted at a rate of 4% and 1.5%, respectively. The effect of parameter uncertainty was assessed using probabilistic sensitivity analysis (PSA).

RESULTS

On average, the EVT strategy cost €159,592 (95% CI: €140,830-€180,154) and generated 3.46 QALYs (95% CI: 3.04-3.90) per patient, whereas the costs and QALYs associated with BMM were €149,935 (95% CI: €130,841-€171,776) and 2.88 (95% CI: 2.48-3.29), respectively. The incremental cost-effectiveness ratio per QALY and the incremental net monetary benefit were €16,442 and €19,710, respectively. At a cost-effectiveness threshold of €50,000/QALY, EVT was cost-effective in 87% of replications.

DISCUSSION AND CONCLUSION

Collateral-based selection for late-window EVT is likely cost-effective from a societal perspective in The Netherlands.

A Simple Cost-Effectiveness Model of Screening: An Open-Source Teaching and Research Tool Coded in R

Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.

An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.

Minimising population health loss in times of scarce surgical capacity: a modelling study for surgical procedures performed in nonacademic hospitals

BACKGROUND

The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures.

METHODS

In this study, we used the previously published three-state cohort state-transition model, to evaluate the health effects of surgery postponement for 28 surgical procedures commonly performed in nonacademic hospitals. Scientific literature and national registries yielded nearly all input parameters, except for the quality of life (QoL) estimates which were obtained from experts using the Delphi method. Two expert panels, one from a single nonacademic hospital and one from different nonacademic hospitals in the Netherlands, were invited to estimate QoL weights. We compared estimated model results (disability adjusted life years (DALY)/month of surgical delay) based on the QoL estimates from the two panels by calculating the mean difference and the correlation between the ranks of the different surgical procedures. The eventual model was based on the combined QoL estimates from both panels.

RESULTS

Pacemaker implantation was associated with the most DALY/month of surgical delay (0.054 DALY/month, 95% CI: 0.025-0.103) and hemithyreoidectomy with the least DALY/month (0.006 DALY/month, 95% CI: 0.002-0.009). The overall mean difference of QoL estimates between the two panels was 0.005 (95% CI -0.014-0.004). The correlation between ranks was 0.983 (p < 0.001).

CONCLUSIONS

Our study provides an overview of incurred health loss due to surgical delay for surgeries frequently performed in nonacademic hospitals. The quality of life estimates currently used in our model are robust and validate towards a different group of experts. These results enrich our earlier published results on academic surgeries and contribute to prioritising a more complete set of surgeries.