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To demonstrate the landscape of model-based economic studies in asthma and highlight where there is room for improvement in the design and reporting of studies.Design: A systematic review of the methodologies of model-based, cost-effectiveness analyses of asthma-related interventions was conducted. Models were evaluated for adherence to best-practice modeling and reporting guidelines and assumptions about the natural history of asthma.Methods: A systematic search of English articles was performed in MEDLINE, EMBASE, and citations within reviewed articles. Studies were summarized and evaluated based on their adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). We also studied the underlying assumptions about disease progression, heterogeneity in disease course, comorbidity, and treatment effects.Results: Forty-five models of asthma were included (33 Markov models, 10 decision trees, 2 closed-form equations). Novel biological treatments were evaluated in 12 studies. Some of the CHEERS' reporting recommendations were not satisfied, especially for models published in clinical journals. This was particularly the case for the choice of the modeling framework and reporting on heterogeneity. Only 13 studies considered any subgroups, and 2 explicitly considered the impact of comorbidities. Adherence to CHEERS requirements and the quality of models generally improved over time. Conclusion:It would be difficult to replicate the findings of contemporary model-based evaluations of asthma-related interventions given that only a minority of studies reported the essential parameters of their studies. Current asthma models generally lack consideration of disease heterogeneity and do not seem to be ready for evaluation of precision medicine technologies.

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Background The transition from pediatric to adult care is associated with changes centered around the patient taking responsibility for their health. As the incidence of childhood-onset inflammatory bowel disease (IBD) is increasing, it is important to address gaps in transition literature—specifically, the indicators signifying achievement of transition success. The study objective was to define transition success according to patients, parents, and health care providers involved in IBD transition. Methods This study used the method of qualitative description to conduct semi-structured interviews with patients, parents, and health care providers. During interviews, demographic information was collected, and interviews were recorded and transcribed. Data analysis was conducted independently of each group using latent content analysis. Participant recruitment continued until thematic saturation was reached within each group. Results Patients, parents, and health care providers all defined transition success with the theme of independence in one’s care. The theme of disease management emerged within parent and provider groups, whereas the theme of relationship with/ trust in adult care team was common to patients and parents. Additional themes of care team management, general knowledge, care stability, and health outcomes emerged within specific groups. Conclusion This study demonstrated differences between how patients, parents, and health care providers view transition success. This finding reveals the value of using a multifaceted definition of transition success with input from all stakeholders. Further research should prioritize the identification of factors common to patients who do not reach transition success as defined by patients, their parents, and providers.

In this paper, we consider the worst-case regret of Linear Thompson Sampling (LinTS) for the linear bandit problem. Russo and Van Roy (2014) show that the Bayesian regret of LinTS is bounded above by O(d √ T ) where T is the time horizon and d is the number of parameters. While this bound matches the minimax lower-bounds for this problem up to logarithmic factors, the existence of a similar worst-case regret bound is still unknown. The only known worst-case regret bound for LinTS, due to Agrawal and Goyal (2013b); Abeille et al. (2017), is O(d √ dT ) which requires the posterior variance to be inflated by a factor of O( √ d). While this bound is far from the minimax optimal rate by a factor of √ d, in this paper we show that it is the best possible one can get, settling an open problem stated in Russo et al. ( 2018). Specifically, we construct examples to show that, without the inflation, LinTS can incur linear regret up to time exp(O(d)). We then demonstrate that, under mild conditions, a slightly modified version of LinTS requires only an O(1) inflation where the constant depends on the diversity of the optimal arm.