A gentle tutorial on power simulations in suicide research: Worked examples in R

Area of Science:

• Psychiatry
• Psychology
• Biostatistics

Background:

• Traditional power analysis is essential for research planning but often inaccurate for complex suicide data.
• Irregular suicide data and intricate models challenge the validity of conventional power formulas.
• Ignoring these limitations can lead to suboptimal sample sizes and misinterpretation of results.

Purpose of the Study:

• To introduce Monte Carlo power simulations as a superior alternative for suicide research.
• To provide practical guidance and examples for implementing power simulations.
• To enhance the accuracy and reliability of power analysis in suicide studies.

Main Methods:

• Monte Carlo power simulations are presented as a robust method for sample size determination.
• The tutorial explains the underlying principles of simulations for building intuition.
• Worked examples using R are provided for practical application.

Main Results:

• Monte Carlo simulations yield accurate results, even when traditional assumptions are violated.
• These simulations provide a reliable alternative to traditional power formulas in complex scenarios.
• The methods ensure more precise power calculations for suicide research.

Conclusions:

• Monte Carlo power simulations are recommended for suicide researchers facing data irregularities and complex models.
• The tutorial offers guidelines for conducting and reporting simulations effectively.
• Researchers can adapt provided R code examples for their specific simulation needs.