Non-parametric estimation of mixed discrete choice models

In this paper, different strands of literature are combined in order to obtain algorithms for semi-parametric estimation of discrete choice models that include the modelling of unobserved heterogeneity by using mixing distributions for the parameters defining the preferences. The models use the theory on non-parametric maximum likelihood estimation (NP-MLE) that has been developed for general mixing models. The expectation-maximization (EM) techniques used in the NP-MLE literature are combined with strategies for choosing appropriate approximating models using adaptive grid techniques. \\ Jointly this leads to techniques for specification and estimation that can be used to obtain a consistent specification of the mixing distribution. Additionally, also algorithms for the estimation are developed that help to decrease problems due to the curse of dimensionality. \\ The proposed algorithms are demonstrated in a small scale simulation study to be useful for the specification and estimation of mixture models in the discrete choice context providing some information on the specification of the mixing distribution. The simulations document that some aspects of the mixing distribution such as the expectation can be estimated reliably. They also demonstrate, however, that typically different approximations to the mixing distribution lead to similar values of the likelihood and hence are hard to discriminate. Therefore it does not appear to be possible to reliably infer the most appropriate parametric form for the estimated mixing distribution.Comment: Paper presented at the International Choice Modelling Conference (ICMC2019) in Kobe, Japa

Hierarchisierung von Risikofaktoren für schwere COVID-19-Erkrankungsverläufe im Kontext der COVID-19-Schutzimpfungen

Angesichts der derzeitigen Impfstoffknappheit geht mit den bundesweiten Schutzimpfungen gegen COVID-19 die Notwendigkeit einer Priorisierung bestimmter Bevölkerungsgruppen einher. Basierend auf den Empfehlungen der STIKO sollen zunächst Personen mit besonders hohem Risiko für schwere oder tödliche COVID-19-Verläufe oder beruflicher Exposition geimpft werden. Diese Empfehlungen stützen sich überwiegend auf internationale Studien - für den deutschen Versorgungskontext steht nur begrenzt Evidenz zur Bedeutung relevanter Risikofaktoren für einen schweren COVID-19-Verlauf zur Verfügung. Das Ziel der im Epidemiologischen Bulletin 19/2021 vorgestellten Studie war es, die Relevanz ausgewählter Vorerkrankungen für einen schweren COVID-19-Verlauf in der in Deutschland lebenden Bevölkerung empirisch zu überprüfen, Erkrankungen hinsichtlich ihres Risikos für einen schweren COVID-19-Verlauf zu ordnen und damit eine einfache, im Versorgungsalltag unkompliziert umsetzbare und dabei möglichst effektive Grundlage für die Impfrangfolge in der ambulanten ärztlichen Versorgung bilden

Model Selection and Model Averaging in Computational Challenging Econometric Models

Batram M. Model Selection and Model Averaging in Computational Challenging Econometric Models. Bielefeld: Universität Bielefeld; 2023

On consistency of the MACML approach to discrete choice modelling

Batram M, Bauer D. On consistency of the MACML approach to discrete choice modelling. Journal of Choice Modelling. 2018;30:1-16

Market access and value-based pricing of digital health applications in Germany

Gensorowsky D, Witte J, Batram M, Greiner W. Market access and value-based pricing of digital health applications in Germany. Cost Effectiveness and Resource Allocation. 2022;20(1): 25.**Abstract** In December 2019, the Digital Health Care Act (“Digitale-Versorgung-Gesetz”) introduced a general entitlement to the provision and reimbursement of digital health applications (DiGA) for insured persons in the German statutory health insurance. As establishing a new digital service area within the solidarity-based insurance system implies several administrative and regulatory challenges, this paper aims to describe the legal framework for DiGA market access and pricing as well as the status quo of the DiGA market. Furthermore, we provide a basic approach to deriving value-based DiGA prices. To become eligible for reimbursement, the Federal Institute for Drugs and Medical Devices evaluates the compliance of a DiGA with general requirements (e.g., safety and data protection) and its positive healthcare effects (i.e., medical benefit or improvements of care structure and processes) in a fast-track process. Manufacturers may provide evidence for the benefits of their DiGA either directly with the application for the fast-track process or generate it during a trial phase that includes temporary reimbursement. After one year of \]reimbursement, the freely-set manufacturer price is replaced by a price negotiated between the National Association of Statutory Health Insurance Funds and the manufacturer. By February 2022, 30 DiGA had successfully completed the fast-track process. 73% make use of the trial phase and have not yet proven their benefit. Given this dynamic growth of the DiGA market and the low minimum evidence standards, fair pricing remains the central point of contention. The regulatory framework makes the patient-relevant benefits of a DiGA a pricing criterion to be considered in particular. Yet, it does not indicate how the benefits of a DiGA should be translated into a reasonable price. Our evidence-based approach to value-based DiGA pricing approximates the SHI’s willingness to pay by the average cost-effectiveness of one or more established therapy in a field of indication and furthermore considers the positive healthcare effects of a DiGA. The proposed approach can be fitted into DiGA pricing processes under the given regulatory framework and can provide objective guidance for price negotiations. However, it is only one piece of the pricing puzzle, and numerous methodological and procedural issues related to DiGA pricing are still open. Thus, it remains to be seen to what extent DiGA prices will follow the premise of value-based pricing

AMNOG-Report 2022: Orphan Drugs – Erstattungs- und Versorgungsherausforderungen

Greiner W, Batram M, Gensorowsky D, Witte J. AMNOG-Report 2022: Orphan Drugs – Erstattungs- und Versorgungsherausforderungen. Beiträge zur Gesundheitsökonomie und Versorgungsforschung. Vol 38. Heidelberg: medhochzwei ; 2022

Influenza Vaccination Coverage Rates in Germany

Damm O, Krefft A, Witte J, Batram M, Greiner W. Influenza Vaccination Coverage Rates in Germany. Value in health. 2020;23(Suppl. 2):S563

Inpatient Burden of Influenza in the Elderly in Germany

Damm O, Kramer R, Witte J, Batram M, Greiner W. Inpatient Burden of Influenza in the Elderly in Germany. In: Emerging Frontiers and Opportunities. Value in Health . Vol 25. Elsevier ; 2022: S86

Using Motifs for Population Synthesis in Multi-agent Mobility Simulation Models

Büscher S, Batram M, Bauer D. Using Motifs for Population Synthesis in Multi-agent Mobility Simulation Models. In: Steland A, Rafajlowicz E, Okhrin O, eds. Stochastic Models, Statistics and Their Applications. Springer Proceedings in Mathematics & Statistics. Vol 294. Cham: Springer; 2019: 335-350