ArticleBMC medical research methodology2023
The iterative bisection procedure: a useful tool for determining parameter values in data-generating processes in Monte Carlo simulations.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Impact of the Number and the Size of Clusters on Prediction Performance of the Stratified and the Conditional Shared Gamma Frailty Cox Proportional Hazards Models.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Agreement between heuristic shrinkage factor and optimal shrinkage factors in logistic regression for risk prediction: a simulation study across different sample sizes and settings.Diagnostic and prognostic research · 2026Article
- A general sample size framework for developing or updating a predictive algorithm: with application to clinical prediction models.BMC medical research methodology · 2026Article
- Using Propensity Score Weighting With Clustered Data When the Treatment Is Applied at the Level of the Cluster and Outcomes Are Assessed at the Level of the Individual: The Observational Analog of Cluster Randomization Trials.Statistics in medicine · 2026Article
- The Impact of Two Data-Generating Processes for Competing Risk Data on the Discrimination and Calibration of Two Types of Competing Risk Regression Models.Statistics in medicine · 2026Article
- Optimal pooling strategies for respiratory virus testing: A comparative cost-effectiveness analysis.PLOS global public health · 2026Article
- Propensity score weighting analysis with complex survey data for estimating population-level treatment effects on survival: a simulation study.Health services & outcomes research methodology · 2025Article
- A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes.Diagnostic and prognostic research · 2025Article
- Inverse Probability of Treatment Weighting Using the Propensity Score With Competing Risks in Survival Analysis.Statistics in medicine · 2025Article
- Multiple imputation with competing risk outcomes.Computational statistics · 2025Article
- The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.Statistical methods in medical research · 2024Article
- Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable.Pharmaceutical statisticsArticle
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Abstract
backgroundData-generating processes are key to the design of Monte Carlo simulations. It is important for investigators to be able to simulate data with specific characteristics.
methodsWe described an iterative bisection procedure that can be used to determine the numeric values of parameters of a data-generating process to produce simulated samples with specified characteristics. We illustrated the application of the procedure in four different scenarios: (i) simulating binary outcome data from a logistic model such that the prevalence of the outcome is equal to a specified value; (ii) simulating binary outcome data from a logistic model based on treatment status and baseline covariates so that the simulated outcomes have a specified treatment relative risk; (iii) simulating binary outcome data from a logistic model so that the model c-statistic has a specified value; (iv) simulating time-to-event outcome data from a Cox proportional hazards model so that treatment induces a specified marginal or population-average hazard ratio.
resultsIn each of the four scenarios the bisection procedure converged rapidly and identified parameter values that resulted in the simulated data having the desired characteristics.
conclusionAn iterative bisection procedure can be used to identify numeric values for parameters in data-generating processes to generate data with specified characteristics.
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