New PDF release: Discrete event simulation for health technology assessment

By J. Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak

ISBN-10: 1482218259

ISBN-13: 9781482218251

Discover easy methods to practice DES to difficulties Encountered in HTA

Discrete occasion simulation (DES) has ordinarily been utilized in the engineering and operations examine fields. using DES to notify judgements approximately wellbeing and fitness applied sciences continues to be in its infancy. Written via experts on the vanguard of this sector, Discrete occasion Simulation for well-being know-how Assessment is the 1st publication to make all of the principal techniques of DES appropriate for future health expertise review (HTA). available to rookies, the e-book calls for no necessities and describes the ideas with as little jargon as possible.

The ebook first covers the fundamental techniques and their implementation. It subsequent offers an absolutely labored out instance utilizing either a extensively on hand spreadsheet software (Microsoft Excel) and a well-liked really expert simulation package deal (Arena). It then provides techniques to investigate the simulations, together with the therapy of uncertainty; tackles the improvement of the mandatory equations; explains the options to make sure that the versions are as effective as attainable; and explores the crucial subject of validation. The ebook additionally covers quite a few non-essential but convenient themes, comparable to the animation of a simulation and extensions of DES, and encompasses a genuine case examine regarding screening innovations for breast melanoma surveillance.

This booklet courses you in leveraging DES on your exams of wellbeing and fitness applied sciences. After interpreting the chapters in series, it is possible for you to to build a practical version designed to aid within the overview of a brand new future health technology.

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Extra resources for Discrete event simulation for health technology assessment

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Of course, when an entity necessarily moves directly from one event to trigger the next, the timing of the subsequent event is obvious, and there is no need to instruct the simulation on how to find it since the event’s occurrence will necessarily happen at a given interval after the triggering event. Many events in a simulation, however, are not set to happen immediately after another event. Their occurrence is subject to chance in the sense that any given entity may or may not experience the event, and, if it does, the precise moment when it will happen can vary from one entity to another, even if they are identical in all relevant respects.

A second factor identified in the review concerned the representation of disease progression as a continuous process. Markers of disease progression for many chronic conditions are commonly measured on a continuous scale, for example, glycemia in diabetes and body mass index in obesity. For osteoporosis, BMD is measured on a continuous scale. A particular advantage of modeling disease markers accurately is that such models can be used to extrapolate outcomes from intervention studies that are only powered to detect differences in surrogate disease markers.

One was a comparison of a single cohort Markov model of Alzheimer’s disease with a DES that represented heterogeneity in individual characteristics (Gustavsson et al. 2009). There was more than a 30% difference in the incremental cost per QALY (£46,000 vs. £32,000, respectively). Compared to a cohort-based Markov model for human immunodeficiency virus (HIV) that represented disease progression as a binary measure of viral load (below/ above 400 copies/mL), a DES that represented an additional viral load threshold (<50 copies/mL) and CD4+ T-cell count as a continuous factor better predicted observed results at 5 years (Simpson et al.

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Discrete event simulation for health technology assessment by J. Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak

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