Contents
How are simulation studies used to evaluate statistical methods?
Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some “truth” (usually some parameter/s of interest) is known from the process of generating the data.
How to calculate the required number of simulations?
The typical way to determine the required number of simulations is by computing the variance of the simulation σ ^ N 2 for N paths, then the standard error is σ ^ N N, see section on error estimation of MC in “Monte Carlo Methods in Finance” by Peter Jackel, also a chapter “Evaluating a definite integral” in Sobol’s little book
How is a simulation study used in medicine?
With a view to describing recent practice, we review 100 articles taken from Volume 34 of Statistics in Medicine, which included at least one simulation study and identify areas for improvement. Simulation studies are computer experiments that involve creating data by pseudo-random sampling from known probability distributions.
How to analyze a simulation of a dataset?
· Separate scripts used to analyze simulated datasets from scripts to analyze estimates datasets. · Start small and build up code, including plenty of checks. · Set the random number seed once per simulation repetition. · Store the random number states at the start of each repetition.
How to run a simulation in meta-analysis?
I’m running a simulation in meta-analysis to compare two methods that estimate the same problem parameters. One of them is a likelihood-based approach. In this kind of problems, one has to fix in advance a number of parameters, such as nn number of data sets we need to apply the method on, the true value of the parameters, etc.
When do simulation studies come to their own?
It is not always possible, or may be difficult, to obtain analytic results. Simulation studies come into their own when methods make wrong assumptions or data are messy because they can assess the resilience of methods in such situations.