Additive models and tree-based regression models are two main classes of statistical models used to predict the scores on a continuous response variable. It is known that additive models become very complex in the presence of higher order interaction effects, whereas some tree-based models, such as CART, have problems capturing linear main effects of continuous predictors. To overcome these drawbacks, the regression trunk model has been proposed: a multiple regression model with main effects and a parsimonious amount of higher order interaction effects. The interaction effects can be represented by a small tree: a regression trunk. This article proposes a new algorithmSimultaneous Threshold Interaction Modeling Algorithm (STIMA)-to estimate a regression trunk model that is more general and more efficient than the initial one (RTA) and is implemented in the R-package stima. Results from a simulation study show that the performance of STIMA is satisfactory for sample sizes of 200 or higher. For sample sizes of 300 or higher, the 0.50 SE rule is the best pruning rule for a regression trunk in terms of power and Type I error. For sample sizes of 200, the 0.80 SE rule is recommended. Results from a comparative study of eight regression methods applied to ten benchmark datasets suggest that STIMA and GUIDE are the best performers in terms of cross-validated prediction error. STIMA appeared to be the best method for datasets containing many categorical variables. The characteristics of a regression trunk model are illustrated using the Boston house price dataset.Supplemental materials for this article, including the R-package stima, are available online.
We present an overview of the main methodological features and the goals of pharmacoeconomic models that are classified in three major categories: regression models, decision trees, and Markov models. In particular, we focus on Markov models and define a semi-Markov model on the cost utility of a vaccine for Dengue fever discussing the key components of the model and the interpretation of its results. Next, we identify some criticalities of the decision rule arising from a possible incorrect interpretation of the model outcomes. Specifically, we focus on the difference between median and mean ICER and on handling the willingness-to-pay thresholds. We also show that the life span of the model and an incorrect hypothesis specification can lead to very different outcomes. Finally, we analyse the limit of Markov model when a large number of states is considered and focus on the implementation of tools that can bypass the lack of memory condition of Markov models. We conclude that decision makers should interpret the results of these models with extreme caution before deciding to fund any health care policy and give some recommendations about the appropriate use of these models.
Background Sars-Cov-2 is a novel corona virus associated with significant morbidity and mortality. Remdesivir and Dexamethasone are two treatments that have shown to be effective against the Sars-Cov-2 associated disease. However, a cost-effectiveness analysis of the two treatments is still lacking. Objective The cost-utility of Remdesivir, Dexamethasone and a simultaneous use of the two drugs with respect to standard of care for treatment Covid-19 hospitalized patients is evaluated, together with the effect of Remdesivir compared to the base model but based on alernative assumptions. Methods A decision tree for an hypothetical cohort of Covid-19 hospitalized patients, from an health care perspective and a one year horizon is specified. Efficacy data are retrieved from a literature review of clinical trials, whilst costs and utility are obtained from other published studies. Results Remdesivir, if health care costs are related to the days of hospitalization, is a cost saving strategy. Dexamethasone is cost effective with an ICER of <DOLLAR/>5208/QALY, and the concurrent use of Remdesivir and Dexamethasone is the most favorable strategy for higher level of willingness to pay thresholds. Moreover, if Remdesivir has a positive effect on mortality the utility is three times higher respect to base case. Whereas, if health care costs are not related to the length of patient hospitalization Remdesivir has an ICER respect to standard of care of <DOLLAR/>384412.8/QALY gained, which is not cost effective. We also find that Dexaamethasone is cost effective respect to standard care if we compute the cost for live saved with an ICER of <DOLLAR/>313.79 for life saved. The uncertainty of the model parameters is also tested through both a one-way deterministic sensitivity analysis and a probabilistic sensitivity analysis. Conclusion We find that the use of Remdesivir and/or Dexamethasone is effective from an economic standpoint.
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