Simulation of trajectories in the illness-death model for chronic diseases: discrete event simulation, Doob-Gillespie algorithm and coverage of Wald confidence intervals
Brinks, R.; Hoyer, A.
Show abstract
We compare two approaches for simulating events in the illness-death model in a test example about type 2 diabetes in Germany. The first approach is a discrete event simulation, where relevant events, i.e., onset of disease and death, are simulated for each subject individually. The second approach is the Doob-Gillespie algorithm, which simulates the number of people in each state of the illness-death model at each point in time. The algorithms are compared in terms of bias, variance and speed. Based on the results of the comparison in the test example, we assess coverage of the corresponding Wald confidence intervals.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Future prevalence of type 2 diabetes – a comparative analysis of chronic disease projection methods 95%
- Regular testing of asymptomatic healthcare workers identifies cost-efficient SARS-CoV-2 preventive measures 93%
- Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts 93%
Similar papers in this journal
- Uncertainty and Inconsistency of COVID-19 Non-Pharmaceutical Intervention Effects with Multiple Competitive Statistical Models 94%
- Extended compartmental model for modeling COVID-19 epidemic in Slovenia 93%
- Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects 93%
Similar papers in this journal
- Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant 92%
- Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality 92%
- Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data 92%
Similar papers in this journal
- A Markov Chain approach for ranking treatments in network meta-analysis 93%
- A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data 92%
- Penalized reduced rank regression for multi-outcome survival data supports a common metabolic risk score for age-related diseases 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.