Decision modelling for economic evaluation of liver transplantation

Abstract

As the gap between a shortage of organs and the immense demand for liver grafts persists, every available donor liver needs to be optimized for utility, urgency and equity. To overcome this challenge, decision modelling might allow us to gather evidence from previous studies as well as compare the costs and consequences of alternative options. For public health policy and clinical intervention assessment, it is a potentially powerful tool. The most commonly used types of decision analytical models include decision trees, the Markov model, microsimulation, discrete event simulation and the system dynamic model. Analytic models could support decision makers in the field of liver transplantation when facing specific problems by synthesizing evidence, comprising all relevant options, generalizing results to other contexts, extending the time horizon and exploring the uncertainty. For modeling studies of economic evaluation for transplantation, understanding the current nature of the disease is crucial, as well as the selection of appropriate modelling techniques. The quality and availability of data is another key element for the selection and development of decision analytical models. In addition, good practice guidelines should be complied, which is important for standardization and comparability between economic outputs.

Status of AI-Enabled Clinical Decision Support Systems Implementations in China

“…This is a common global problem where the lack of high quality and available data creates a barrier for practitioners to receive the “right information” from an AI + CDSS. 35 36 The complex nature of medical data makes data quality problems more evident because missing, incorrect, or vague information may lead to useless or even harmful results. 37 The vast quantity of data, high dimensionality, data sparsity and deviations or systematic errors in medical data makes it difficult to use machine learning methods to perform accurate pattern recognition and generate predictive clinical models.…”

Estimation of lifetime costs for patients receiving a transplant: the case of liver transplantation related to hepatitis B in Italy

“…Due to the complexity of patient follow-up, a microsimulation approach was chosen to build the model, since it is considered more appropriate for complex chronic diseases where patient-level information is relevant ( 14 ). Microsimulations allow the replication of the healthcare trajectory of individual patients ( 15 ), which can be affected by more than one complication/comorbidity at a time, and to keep track of each patient’s individual history. Patients enter the model in the initial state and proceed individually through various transition states based on the probabilities of transition and are subject to events with a time-varying probability of occurrence.…”

Methods for deriving risk difference (absolute risk reduction) from a meta-analysis

“…This method estimates risk difference by simulating trials of individual patients and aggregating individual results to generate group means and distributions (similar to Monte Carlo simulation). Thus, the main feature of this approach is “constructing the desired patient group from individual sampling,” which means simulating one individual at a time, and the mean results of the whole cohort are calculated from all the aggregated individual simulation results 22. Each time the model runs, it selects a patient with a baseline risk drawn randomly from a distribution that is defined by the mean and standard deviation of the available baseline risk.…”