Example Code for a Monte Carlo Simulation Study

Code in R and Stata to code, run, and analyse a simulation study is included in this repository. The specific simulation study is described down below.

Structure of this repository

This repository is organised as follows:

R code is included in the folder named R, with three scripts: one for coding the simulation study, one for analysing the results, and one for creating plots and tables with the results;
Stata code is included in the folder named Stata, including three analogous scripts.
Simulated data with the results obtained by the above-mentioned code in R and Stata are included in the folder named data. The R dataset has extension *.RDS, while the Stata dataset has extension *.DTA.

Aims of the simulation study

The aim of this simulation study is pedagogical.

Say we have a study which can be summarised by the following DAG:

library(ggdag)
#> Loading required package: ggplot2
#> 
#> Attaching package: 'ggdag'
#> The following object is masked from 'package:stats':
#> 
#>     filter

confounder_triangle() %>%
  ggdag() +
  theme_dag_blank(base_size = 12)

where X is the exposure, Y the outcome, and Z a confounder.

Note that the exposure and the outcome are independent, there is no edge between X and Y.

We know that there should be no observed association between X and Y once we adjust for Z in our analysis.

However:

Is it true that once we adjust for Z we observe no association between X and Y?
What happens if we fail to adjust for a confounder in our analysis?

This is what we are trying to learn from this simulation study.

Data-generating mechanisms

X is a binary treatment variable, Z is a continuous confounder, and Y is a continuous outcome.

Z is simulated from a standard normal distribution, while X depends on Z according to a logistic model:

\log \frac{P(X = 1)}{P(X = 0)} = \gamma_0 + \gamma_1 Z

Y depends on Z according to a linear model:

Y = \alpha_0 + \alpha_1 Z + \varepsilon

with \varepsilon following a standard normal distribution.

We fix the parameters \gamma_0 = 1, \gamma_1 = 3, \alpha_0 = 10. For \alpha_1, we actually simulate two scenarios:

\alpha_1 = 5: in this scenario, Z is actually a confounder;
\alpha_1 = 0: in the second scenario, we remove the edge between Z and Y. Here Z is not a counfounder anymore.

Finally, we generate N = 200 independent subjects per each simulated dataset.

Estimand

We have a single estimand of interest here, the regression coefficient that quantifies the association between X and Y.

Remember that, according to our data-generating mechanisms, we expect no association between X and Y.

Methods

We estimate the regression coefficient of interest using linear regression. Specifically, we fit the following two models:

A model that fails to adjust for the observed confounder Z (model 1):

Y = \beta_0 + \beta_1 X + \varepsilon

A model that properly adjusts for Z (model 2):

Y = \beta_0 + \beta_1 X + \beta_2 Z + \varepsilon

The coefficient of interest is β₁ according to this notation.

Performance measures

The key performance measure of interest is bias, defined as

E(\hat{\beta_1})

as we know that — according to our DGMs — the true \beta_1 = 0.

We might also be interested in estimating the power of a significance test for \hat{\beta_1} at the \alpha = 0.05 level. In our example, think of that as a test for the null hypothesis \beta_1 = 0.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

guhjy / simulation-study-example