Successes and failures in UK/US development of simulation

Murphy, Stephen P.; Perera, Terrence

doi:10.1016/s0928-4869(01)00048-9

Cited by 13 publications

(3 citation statements)

References 4 publications

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“…Research evidence on the stakeholder engagement within the healthcare simulation domain is very limited, and mainly in the form of scattered information throughout the literature on the challenges of simulation modeling in general 5,18,21,25–27 or in some specific contexts such as healthcare. 22,23,28–33 However, there is a clear gap on the “causal study” of “low stakeholder engagement”.…”

Section: Background and Theoretical Frameworkmentioning

confidence: 99%

Causal factors of low stakeholder engagement: a survey of expert opinions in the context of healthcare simulation projects

et al. 2015

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While simulation methods have proved to be very effective in identifying efficiency gains, low stakeholder engagement creates a significant limitation on the achievement of simulation modeling projects in practice. This study reports causal factors-at two hierarchical levels (i.e., primary and secondary)-that could significantly affect low stakeholder engagement in healthcare simulation projects. A self-completed questionnaire was administered online to 91 experts in the field from whom 37 responded. The results were reinforced using a bootstrapped sample (n = 1000). Data were analyzed using descriptive statistics, Kendal's tau-b correlations, and non-linear multiple regression. Based on our research, while such factors as ''communication gap'', ''stakeholders' high workload'', and ''too much complexity involved'' represent the most significant primary causal factors, some others such as ''reluctance to change'' proved interestingly insignificant. The research suggests that high-impact public health projects can exemplify the areas that will potentially generate engagement in the healthcare simulation domain.

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Section: Background and Theoretical Frameworkmentioning

confidence: 99%

Causal factors of low stakeholder engagement: a survey of expert opinions in the context of healthcare simulation projects

et al. 2015

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“…A resampling study, however, samples data from a large empirical complete dataset. The data in the smaller samples are observations from real patients [ 7 ] and thus reflect the appropriate level of diversity and variability found in realistic populations [ 8 ]. The initial dataset needs to be sufficiently large to permit numerous samples of reasonable size to be selected without seriously endangering any future assumption of independence; it can be from one large study (e.g.…”

Section: Introductionmentioning

confidence: 99%

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

Marshall

Altman

Holder

2010

BMC Med Res Methodol

114

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BackgroundThe appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.MethodsObserved data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.ResultsCC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.ConclusionsVery few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.

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“…2,3 One reason is arguably that production simulation projects tend to be slow in providing the clients with model results. 4,5 This is a significant disadvantage, since manufacturing development and design projects usually rely on rapid responses from analyses. 6 Hence, renouncing precision in favor of quick response, organizations are tempted to choose less complex tools.…”

Section: Introductionmentioning

confidence: 99%

Automated input data management: evaluation of a concept for reduced time consumption in discrete event simulation

2012

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Input data management is a crucial and time-consuming part of a simulation project. Consequently, improvement of this process has substantial potential to increase the rapidity of simulation projects, thus enabling more detailed analyses in design and development of production systems. This paper presents the development of a concept and an associated software demonstrator called the Generic Data Management Tool (GDM-Tool), which automates several critical and time-consuming data input activities. More specifically, raw data are extracted from multiple sources, with different data structures, and transformed to simulation input through data cleaning, calculations and distribution fitting, all done in one automated process. Finally, the simulation input is presented in the Core Manufacturing Simulation Data format, for further use in simulation applications. As a first step towards validation, the GDM-Tool was evaluated during a case study in the automotive industry. The results show that the time needed for input data management was reduced by 78%, as relative to a traditional manual approach.

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Successes and failures in UK/US development of simulation

Cited by 13 publications

References 4 publications

Causal factors of low stakeholder engagement: a survey of expert opinions in the context of healthcare simulation projects

Causal factors of low stakeholder engagement: a survey of expert opinions in the context of healthcare simulation projects

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

Automated input data management: evaluation of a concept for reduced time consumption in discrete event simulation

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