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Related Experiment Video

Updated: Aug 9, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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The iterative bisection procedure: a useful tool for determining parameter values in data-generating processes in

Peter C Austin1,2,3

  • 1ICES, 2075 Bayview Avenue, Toronto, ON, G106M4N 3M5, Canada. peter.austin@ices.on.ca.

BMC Medical Research Methodology
|February 21, 2023
PubMed
Summary

This study introduces an iterative bisection method to find parameters for Monte Carlo simulations. This procedure efficiently generates simulated data with precise, desired characteristics for research.

Keywords:
Data-generating processMonte Carlo simulationsSimulations

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Area of Science:

  • Biostatistics
  • Computational Statistics
  • Simulation Methodology

Background:

  • Monte Carlo simulations are crucial for generating data with specific characteristics in research.
  • Accurate data generation processes are essential for reliable simulation study designs.

Purpose of the Study:

  • To present an iterative bisection procedure for determining data-generating process parameters.
  • To enable the simulation of data with precisely specified characteristics.

Main Methods:

  • An iterative bisection procedure was developed to identify parameter values.
  • The method was applied to four distinct simulation scenarios, including logistic and Cox proportional hazards models.
  • Scenarios involved specifying outcome prevalence, treatment relative risk, model c-statistic, and hazard ratios.

Main Results:

  • The bisection procedure demonstrated rapid convergence across all four scenarios.
  • Identified parameter values successfully produced simulated data matching the specified characteristics.
  • The method proved effective for diverse data-generating processes and outcome types.

Conclusions:

  • The iterative bisection procedure is a robust method for parameter estimation in data generation.
  • This technique facilitates the creation of simulated datasets with targeted statistical properties.
  • It enhances the reliability and specificity of Monte Carlo simulation studies.