Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer

Background. Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches. Methods. Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study. Results. In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches’ performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance. Conclusions. Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).

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AI-Based Discrete-Event Simulations for Manufacturing Schedule Optimization

Proceedings of the 2020 4th International Conference on Algorithms, Computing and Systems ◽

10.1145/3423390.3426725 ◽

2020 ◽

Author(s):

Thomas Krause

Keyword(s):

Discrete Event ◽

Discrete Event Simulations ◽

Schedule Optimization ◽

Event Simulations

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Treatment breaks in first line treatment of advanced colorectal cancer: an individual patient data meta-analysis

Cancer Treatment Reviews ◽

10.1016/j.ctrv.2021.102226 ◽

2021 ◽

pp. 102226

Author(s):

Richard Adams ◽

Kaitlyn Goey ◽

Benoist Chibaudel ◽

Miriam Koopman ◽

Cornelis Punt ◽

...

Keyword(s):

Colorectal Cancer ◽

Advanced Colorectal Cancer ◽

Individual Patient Data ◽

Meta Analysis ◽

Patient Data ◽

Line Treatment ◽

First Line ◽

First Line Treatment

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COSIDIA: An Approach for Real-Time Parallel Discrete Event Simulations Tailored for Wireless Networks

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation ◽

10.1145/3437959.3459250 ◽

2021 ◽

Author(s):

Christina Obermaier ◽

Raphael Riebl ◽

Christian Facchi ◽

Ali H. Al-Bayatti ◽

Sarmadullah Khan

Keyword(s):

Wireless Networks ◽

Real Time ◽

Discrete Event ◽

Discrete Event Simulations ◽

Parallel Discrete Event ◽

Event Simulations

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When does the use of individual patient data in network meta-analysis make a difference? A simulation study

BMC Medical Research Methodology ◽

10.1186/s12874-020-01198-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Steve Kanters ◽

Mohammad Ehsanul Karim ◽

Kristian Thorlund ◽

Aslam Anis ◽

Nick Bansback

Keyword(s):

Simulation Study ◽

Treatment Effect ◽

Mean Squared Error ◽

Individual Patient Data ◽

Meta Analysis ◽

Model Performance ◽

Patient Data ◽

Meta Regression ◽

Treatment Comparisons ◽

The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

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Antithetic Bias Reduction for Discrete-Event Simulations

Journal of the Operational Research Society ◽

10.1057/jors.1987.71 ◽

1987 ◽

Vol 38 (5) ◽

pp. 431-437 ◽

Cited By ~ 8

Author(s):

Z. Şeyda Deligönül

Keyword(s):

Discrete Event ◽

Bias Reduction ◽

Discrete Event Simulations ◽

Event Simulations

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Pathway Complexity of Model Virus Capsid Assembly Systems

Computational and Mathematical Methods in Medicine ◽

10.1080/17486700802168379 ◽

2008 ◽

Vol 9 (3-4) ◽

pp. 277-293 ◽

Cited By ~ 16

Author(s):

Navodit Misra ◽

Daniel Lees ◽

Tiequan Zhang ◽

Russell Schwartz

Keyword(s):

Discrete Event ◽

Reaction Pathways ◽

Assembly Systems ◽

Capsid Assembly ◽

Reaction Systems ◽

Discrete Event Simulations ◽

Differential Equation Models ◽

Assembly Kinetics ◽

Pathway Models ◽

Event Simulations

As computational and mathematical studies become increasingly central to studies of complicated reaction systems, it will become ever more important to identify the assumptions our models must make and determine when those assumptions are valid. Here, we examine that question with respect to viral capsid assembly by studying the ‘pathway complexity’ of model capsid assembly systems, which we informally define as the number of reaction pathways and intermediates one must consider to accurately describe a given system. We use two model types for this study: ordinary differential equation models, which allow us to precisely and deterministically compare the accuracy of capsid models under different degrees of simplification, and stochastic discrete event simulations, which allow us to sample use of reaction intermediates across a wide parameter space allowing for an extremely large number of possible reaction pathways. The models provide complementary information in support of a common conclusion that the ability of simple pathway models to adequately explain capsid assembly kinetics varies considerably across the space of biologically meaningful assembly parameters. These studies provide grounds for caution regarding our ability to reliably represent real systems with simple models and to extrapolate results from one set of assembly conditions to another. In addition, the analysis tools developed for this study are likely to have broader use in the analysis and efficient simulation of large reaction systems.

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