Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Statistics in Medicine. 2019;38(11):2074-2102.
Comprehensive guide to designing and reporting simulation studies. Covers all aspects from setting aims to choosing performance measures and presenting results. Essential reading for anyone planning a simulation study.
Maldonado G, Greenland S. The importance of critically interpreting simulation studies. Epidemiology. 1997;8(4):453-456.
Brief but important commentary on how to critically evaluate simulation studies. Emphasizes that simulation results depend entirely on assumptions and highlights common pitfalls in interpretation.
Rudolph JE, Fox MP, Naimi AI. Simulation as a Tool for Teaching and Learning Epidemiologic Methods. American Journal of Epidemiology. 2021;190(5):900-907.
Practical guide to using simulation for pedagogy in epidemiology. Explains how simulation can build intuition about statistical concepts and provides examples of teaching applications.
Burton A, Altman DG, Royston P, Holder RL. The design of simulation studies in medical statistics. Statistics in Medicine. 2006;25(24):4279-4292.
Detailed recommendations for planning simulation studies in medical statistics. Covers sample size determination, number of simulations needed, and how to structure simulation experiments.
Mooney C. Conveying truth with the artificial: using simulated data to teach statistics in social sciences. Social Science Computer Review. 1995;13(1):1-5.
Early paper on using simulation for teaching statistics. Makes the case that simulation helps students understand abstract statistical concepts by making them concrete and manipulable.
Hodgson T, Burke M. On simulation and the teaching of statistics. Teaching Statistics. 2000;22(3):91-96.
Practical discussion of how simulation can enhance statistics education. Focuses on how simulation helps students develop statistical intuition and understand sampling distributions.
Notes from a statistics course at NC State. Includes useful equations for calculating simulation error and other estimands. Good technical reference for simulation methodology.
Introductory lecture slides on simulation from Emory. Provides accessible overview of simulation concepts and applications in epidemiology.
Casella G, Robert CP. Monte Carlo Statistical Methods. 2nd ed. New York, NY: Springer; 2004.
Technical and challenging text, but chapters 1-3 provide excellent foundation in Monte Carlo methods. Covers random variable generation, Monte Carlo integration, and variance reduction techniques. Best suited for readers with strong mathematical background.