Monte Carlo simulations are computer experiments designed to study the performance of statistical methods under known data-generating conditions (Morris, White, & Crowther, 2019). Methodologists use simulations to examine questions such as: (1) how does ordinary least squares (OLS) regression perform if errors are heteroskedastic? (2) how does the presence of missing data affect treatment effect estimates from a propensity score analysis? (3) how does cluster robust variance estimation perform when the number of clusters is small? To answer such questions, we conduct experiments by simulating thousands of datasets based on pseudo-random sampling (Morris et al., 2019). We generate datasets under known conditions. For example, we can generate data where the errors are heteroskedastic or there is missing data present following Missing at Random (MAR) mechanism. Then, we apply statistical methods, like OLS or propensity score estimation using logistic regression, to each of the datasets and extract results like regression coefficients, p-values, and confidence intervals. We then analyze the performance of these estimation methods using criteria such as bias, Type 1 error rate, or root mean squared error. Performance measures from simulation studies are estimates, based on a finite number of repeated samples from a data-generating process, and thus include some random error. When interpreting performance measures, it is important to consider how large the estimation error is. This is typically quantified as Monte Carlo standard error (MCSE).

The goal of `simhelpers`

is to assist in running
simulation studies. This package provides a set of functions that
calculate various performance measures and associated MCSE. This
vignette focuses on the set of functions for calculating performance
measures. Below, we explain three major categories of performance
criteria: (1) absolute criteria, (2) relative criteria, and (3) criteria
to evaluate hypothesis testing. We provide formulas used to calculate
the performance measures and MCSE and demonstrate how to use the
functions in the `simhelpers`

package.

The notation and explanations of the performance measures largely follow notes from Dr. James Pustejovsky’s Data Analysis, Simulation and Programming in R course (Spring, 2019).

Bias characterizes whether an estimator tends to lie above or below the true parameter, on average. Variance characterizes the precision of an estimator, as the average squared deviation of the estimator from its average. Note that variance is the inverse of precision. Therefore, the higher the variance, the lower the precision. Mean squared error (MSE) and root mean squared error (RMSE) characterize the accuracy of the estimates. MSE and RMSE measure how far off, on average, an estimator is from the true parameter. Absolute criteria are in the same scale as the estimate. MSE is in squared deviation scale and RMSE is in the scale of the estimates.

Let \(T\) denote an estimator for a parameter \(\theta\). By running a simulation study, we obtain \(K\) estimates \(T_1,...,T_K\) (or realizations of the estimator) for each data generating condition. We can calculate the following sample statistics for the estimates:

- Sample mean: \(\bar{T} = \frac{1}{K}\sum_{k=1}^K T_k\)
- Sample variance: \(S_T^2 = \frac{1}{K - 1}\sum_{k=1}^K \left(T_k - \bar{T}\right)^2\)
- Sample skewness (standardized): \(g_T = \frac{1}{K S_T^3}\sum_{k=1}^K \left(T_k - \bar{T}\right)^3\)
- Sample kurtosis (standardized): \(k_T = \frac{1}{K S_T^4} \sum_{k=1}^K \left(T_k - \bar{T}\right)^4\)

Table 1 below shows each of the performance criteria, its interpretation, its formal definition, how the criterion is estimated in a simulation study, and the formula for the MCSE of the estimated performance measure.

Criterion | Measure | Definition | Estimate | MCSE |
---|---|---|---|---|

Bias | Difference from true parameter | \(\text{E}(T) - \theta\) | \(\bar{T} - \theta\) | \(\sqrt{S_T^2/ K}\) |

Variance | Precision | \(\text{E}\left[(T - \text{E}(T))^2\right]\) | \(S_T^2\) | \(S_T^2 \sqrt{\frac{k_T - 1}{K}}\) |

MSE | Accuracy | \(\text{E}\left[(T - \theta)^2\right]\) | \(\frac{1}{K}\sum_{k=1}^{K}\left(T_k - \theta\right)^2\) | \(\sqrt{\frac{1}{K}\left[S_T^4 (k_T - 1) + 4 S_T^3 g_T(\bar{T} - \theta) + 4 S_T^2 (\bar{T} - \theta)^2\right]}\) |

RMSE | Accuracy | \(\sqrt{\text{E}\left[(T - \theta)^2\right]}\) | \(\sqrt{\frac{1}{K}\sum_{k=1}^{K}\left(T_k - \theta\right)^2}\) | \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(RMSE_{(j)} - RMSE\right)^2}\) |

The equation for the MCSE of RMSE is derived using the jack-knife technique, which involves excluding a replicate \(j\) and calculating RMSE (Efron & Stein, 1981). The formula for RMSE is:

\[\sqrt{\frac{1}{K}\sum_{k=1}^{K}\left(T_k - \theta\right)^2}\]

This is approximately equivalent to:

\[RMSE = \sqrt{(\bar{T} - \theta)^2 + S^2_T}\]

The jack-knife RMSE will be calculated as:

\[RMSE_{(j)} = \sqrt{(\bar{T}_{(j)} - \theta)^2 + S^2_{T(j)}}\]

Here \(\bar{T}_{(j)}\) and \(S^2_{T(j)}\) indicate the mean and variance of the estimates leaving out replicate \(j\). Instead of calculating each jack-knife estimate, we use algebraic tricks to calculate \(\bar{T}_{(j)}\) and \(S^2_{T(j)}\) as follows:

\[\bar{T}_{(j)} = \frac{1}{K-1} \left(K \bar{T} - T_j\right)\]

\[S_{T(j)}^2 = \frac{1}{K - 2} \left[(K - 1) S_T^2 - \frac{K}{K - 1}\left(T_j - \bar{T}\right)^2\right]\]

Then, the jack-knife MCSE of RMSE will be calculated as:

\[MCSE_{RMSE(JK)} = \sqrt{\frac{K -1}{K}\sum_{j=1}^K \left(RMSE_{(j)} - RMSE\right)^2}\]

We use the `welch_res`

dataset included in the package. It
contains the results from an example simulation study comparing the
heteroskedasticity-robust Welch t-test to the usual two-sample t-test
assuming equal variances. The code used to generate the data and derive
results can be found here.
We varied the sample sizes per group. We set the sample size of Group 1
to 50 and we varied sample size of Group 2 to be 50 and 70. We varied
the mean difference parameter when generating data for two groups. We
set the values to 0, 0.5, 1 and 2. We generated the data with slightly
unequal variances. With each simulated dataset, we ran the usual t-test,
which assumes homogeneity of variance, and we also ran Welch t-test,
which does not assume homogeneity of variance. We extracted the
estimates (mean differences), variances of the estimates, p-values, and
the lower and upper bounds of the confidence intervals.

```
library(simhelpers)
library(dplyr)
library(tibble)
library(knitr)
library(dplyr)
library(kableExtra)
welch_res %>%
glimpse()
#> Rows: 16,000
#> Columns: 11
#> $ n1 <dbl> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50…
#> $ n2 <dbl> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50…
#> $ mean_diff <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ iterations <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000…
#> $ seed <dbl> 204809087, 204809087, 204809087, 204809087, 204809087, 204…
#> $ method <chr> "t-test", "Welch t-test", "t-test", "Welch t-test", "t-tes…
#> $ est <dbl> 0.025836000, 0.025836000, 0.005158587, 0.005158587, -0.079…
#> $ var <dbl> 0.09543914, 0.09543914, 0.08481717, 0.08481717, 0.08179330…
#> $ p_val <dbl> 0.9335212, 0.9335804, 0.9859039, 0.9859109, 0.7807543, 0.7…
#> $ lower_bound <dbl> -0.5872300, -0.5899041, -0.5727856, -0.5741984, -0.6473703…
#> $ upper_bound <dbl> 0.6389020, 0.6415761, 0.5831027, 0.5845155, 0.4877263, 0.4…
```

Below, we calculate the absolute performance criteria for the
estimates of the mean differences. We present the results by sample
sizes, mean difference, and the t-test method. The
`calc_absolute()`

function is designed to work with the `tidyeval`

workflow
(Wickham et al., 2019). The first
argument, `res_dat`

, requires a data frame or a tibble
containing the results from a simulation study. The second argument,
`estimates`

, requires the name of the column containing the
estimates like mean difference or regression coefficients. The third
argument, `true_param`

, requires the name of the column
containing the true parameters. The fourth argument,
`perfm_criteria`

, lets the user specify which criteria to
evaluate. The criteria can be specified using a character or character
vector. If the user only wants bias, they can specify
`perfm_criteria = "bias"`

. If the user wants bias and root
mean squared error, they can specify
`perfm_criteria = c("bias", "rmse")`

. By default, the
function returns bias, variance, mean squared error (mse), and root mean
squared error (rmse),
`perfm_criteria = c("bias", "variance", "mse", "rmse")`

. In
the example below, we ask for all the available criteria.

We calculate the absolute performance measures only for the
conventional t-test results because the mean difference is identical for
the Welch t-test. We use `dplyr`

syntax to group by the sample sizes and mean difference that were used
to generate the data. We provide examples using `do()`

and `group_modify()`

to run `calc_absolute()`

on results for each combination of
the conditions. Results are rounded to five decimal places.

If there are any convergence issues with estimation, please make sure
to write
your estimation function to output `NA`

for the values of the
point estimates, variance estimates, p-values, or confidence interval
estimates. The functions in the `simhelpers`

package will
calculate performance criteria and MCSE after deleting any missing
estimates and will output the number of iterations, `K`

,
which will exclude iterations with `NA`

values for estimates.
Note that `K`

may differ by condition.

```
# using do()
welch_res %>%
filter(method == "t-test") %>% # filter just conventional t-test res
group_by(n1, n2, mean_diff) %>% # grouping
do(calc_absolute(., estimates = est, true_param = mean_diff)) %>% # run the function
kable(digits = 5) # create a kable table
```

n1 | n2 | mean_diff | K_absolute | bias | bias_mcse | var | var_mcse | mse | mse_mcse | rmse | rmse_mcse |
---|---|---|---|---|---|---|---|---|---|---|---|

50 | 50 | 0.0 | 1000 | -0.00890 | 0.01000 | 0.09999 | 0.00425 | 0.09997 | 0.00425 | 0.31618 | 0.00839 |

50 | 50 | 0.5 | 1000 | -0.00555 | 0.01034 | 0.10698 | 0.00473 | 0.10690 | 0.00473 | 0.32696 | 0.00891 |

50 | 50 | 1.0 | 1000 | -0.00726 | 0.00988 | 0.09766 | 0.00423 | 0.09761 | 0.00423 | 0.31243 | 0.00840 |

50 | 50 | 2.0 | 1000 | 0.00725 | 0.00981 | 0.09631 | 0.00440 | 0.09627 | 0.00439 | 0.31028 | 0.00862 |

50 | 70 | 0.0 | 1000 | -0.00266 | 0.00867 | 0.07521 | 0.00330 | 0.07514 | 0.00331 | 0.27411 | 0.00744 |

50 | 70 | 0.5 | 1000 | -0.00514 | 0.00895 | 0.08002 | 0.00391 | 0.07997 | 0.00391 | 0.28278 | 0.00826 |

50 | 70 | 1.0 | 1000 | -0.01124 | 0.00899 | 0.08081 | 0.00358 | 0.08086 | 0.00358 | 0.28435 | 0.00775 |

50 | 70 | 2.0 | 1000 | -0.00079 | 0.00883 | 0.07805 | 0.00345 | 0.07798 | 0.00345 | 0.27924 | 0.00760 |

```
# using group_modify()
welch_res %>%
filter(method == "t-test") %>% # filter just conventional t-test res
mutate(params = mean_diff) %>% # group_modify cannot take in a group column as an argument
group_by(n1, n2, mean_diff) %>% # grouping
group_modify(~ calc_absolute(.x, estimates = est, true_param = params)) %>%
kable(digits = 5)
```

n1 | n2 | mean_diff | K_absolute | bias | bias_mcse | var | var_mcse | mse | mse_mcse | rmse | rmse_mcse |
---|---|---|---|---|---|---|---|---|---|---|---|

50 | 50 | 0.0 | 1000 | -0.00890 | 0.01000 | 0.09999 | 0.00425 | 0.09997 | 0.00425 | 0.31618 | 0.00839 |

50 | 50 | 0.5 | 1000 | -0.00555 | 0.01034 | 0.10698 | 0.00473 | 0.10690 | 0.00473 | 0.32696 | 0.00891 |

50 | 50 | 1.0 | 1000 | -0.00726 | 0.00988 | 0.09766 | 0.00423 | 0.09761 | 0.00423 | 0.31243 | 0.00840 |

50 | 50 | 2.0 | 1000 | 0.00725 | 0.00981 | 0.09631 | 0.00440 | 0.09627 | 0.00439 | 0.31028 | 0.00862 |

50 | 70 | 0.0 | 1000 | -0.00266 | 0.00867 | 0.07521 | 0.00330 | 0.07514 | 0.00331 | 0.27411 | 0.00744 |

50 | 70 | 0.5 | 1000 | -0.00514 | 0.00895 | 0.08002 | 0.00391 | 0.07997 | 0.00391 | 0.28278 | 0.00826 |

50 | 70 | 1.0 | 1000 | -0.01124 | 0.00899 | 0.08081 | 0.00358 | 0.08086 | 0.00358 | 0.28435 | 0.00775 |

50 | 70 | 2.0 | 1000 | -0.00079 | 0.00883 | 0.07805 | 0.00345 | 0.07798 | 0.00345 | 0.27924 | 0.00760 |

Relative criteria can be useful for describing an estimator’s performance, especially if the performance varies in proportion to the true value of the target parameter. It can be only used when \(|\theta| > 0\) as we cannot divide by \(0\) (Morris et al., 2019).

To derive the MCSE for relative RMSE, we again used the jack-knife technique. The formula to calculate relative RMSE is:

\[rRMSE = \sqrt{\frac{(\bar{T} - \theta)^2 + S_T^2}{\theta^2}}\]

The jack-knife RMSE will be calculated as:

\[rRMSE_{(j)} = \sqrt{\frac{(\bar{T}_{(j)} - \theta)^2 + S_{T(j)}^2}{\theta^2}}\]

Here \(\bar{T}_{(j)}\) and \(S^2_{T(j)}\) are calculated as specified above when we described the algebra trick to estimate these values. The MCSE is then calculated as specified in the table below.

Table 2 below shows each of the relative performance criteria, its interpretation, its formal definition, how the criterion is estimated in a simulation study, and its MCSE formula.

Criterion | Measure | Definition | Estimate | MCSE |
---|---|---|---|---|

Relative Bias | Relative difference from true parameter | \(\text{E}(T) / \theta\) | \(\bar{T} / \theta\) | \(\sqrt{S_T^2 / (K\theta^2)}\) |

Relative MSE | Accuracy | \(\text{E}\left[(T - \theta)^2\right]/ \theta^2\) | \(\frac{(\bar{T} - \theta)^2 + S_T^2}{\theta^2}\) | \(\sqrt{\frac{1}{K\theta^2}\left[S_T^4 (k_T - 1) + 4 S_T^3 g_T(\bar{T} - \theta) + 4 S_T^2 (\bar{T} - \theta)^2\right]}\) |

Relative RMSE | Accuracy | \(\sqrt{\text{E}\left[(T - \theta)^2\right]/ \theta^2}\) | \(\sqrt{\frac{(\bar{T} - \theta)^2 + S_T^2}{\theta^2}}\) | \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rRMSE_{(j)} - rRMSE)^2\right)}\) |

Below, we calculate the relative criteria for the mean difference
estimates. Note that when the mean difference is 0, the relative
measures cannot be calculated and the function returns `NA`

values. The syntax for `calc_relative()`

is similar to the
one that we used earlier for `calc_absolute()`

. The
`perfm_criteria`

argument allows the user to specify which
criteria to evaluate:
`perfm_criteria = c("relative bias", "relative mse", "relative rmse")`

.

```
# using group_modify()
welch_res %>%
filter(method == "t-test") %>%
mutate(params = mean_diff) %>%
group_by(n1, n2, mean_diff) %>%
group_modify(~ calc_relative(.x, estimates = est, true_param = params)) %>%
kable(digits = 5)
```

n1 | n2 | mean_diff | K_relative | rel_bias | rel_bias_mcse | rel_mse | rel_mse_mcse | rel_rmse | rel_rmse_mcse |
---|---|---|---|---|---|---|---|---|---|

50 | 50 | 0.0 | 1000 | NA | NA | NA | NA | NA | NA |

50 | 50 | 0.5 | 1000 | 0.98890 | 0.02069 | 0.42803 | 0.00947 | 0.65424 | 0.01451 |

50 | 50 | 1.0 | 1000 | 0.99274 | 0.00988 | 0.09771 | 0.00423 | 0.31259 | 0.00679 |

50 | 50 | 2.0 | 1000 | 1.00363 | 0.00491 | 0.02409 | 0.00219 | 0.15522 | 0.00355 |

50 | 70 | 0.0 | 1000 | NA | NA | NA | NA | NA | NA |

50 | 70 | 0.5 | 1000 | 0.98972 | 0.01789 | 0.32018 | 0.00783 | 0.56585 | 0.01388 |

50 | 70 | 1.0 | 1000 | 0.98876 | 0.00899 | 0.08094 | 0.00358 | 0.28449 | 0.00631 |

50 | 70 | 2.0 | 1000 | 0.99961 | 0.00442 | 0.01951 | 0.00172 | 0.13969 | 0.00309 |

Variance estimators are always positive, and so relative criteria are often used to characterize their performance. For variance estimators, we have \(V\) denoting the sampling variance of a point estimator \(T\). To assess the relative criteria for \(V\), we need to divide it by the true value of the sampling variance of \(T\), \(\lambda = \text{Var}(T)\), which we may not be able to calculate directly. In such scenario, we can use the sample variance of \(T\) across the replications, \(S_T^2\), to estimate the true sampling variance.

The relative bias would then be estimated by \(rB = \bar{V} / S_T^2\), the average of the variance estimates divided by the sample variance of the point estimates. To estimate MCSE of the relative bias of the variance estimator, we need to account for the uncertainty in the estimation of the true sampling variance. One way to do so is to use the jack-knife technique that we described above, which entails excluding a replicate \(j\) and calculating relative bias \(\bar{V}_{(j)}/ S_{T(j)}^2\). The Monte Carlo standard error can then be calculated as:

\[ MCSE\left(rB\right) = \sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rB_{(j)} - rB\right)^2} \] which can be written as:

\[ MCSE\left(rB\right) = \sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(\frac{\bar{V}_{(j)}}{S_{T(j)}^2} - \frac{\bar{V}}{S_T^2}\right)^2} \]

We reformulate the MCSE using some algebra tricks similar to how we reformulated them for the RMSE MCSE formulas above.

\[ \begin{aligned} \bar{V}_{(j)} &= \frac{1}{K - 1}\left(K \bar{V} - V_j\right) \\ S_{T(j)}^2 &= \frac{1}{K - 2} \left[(K - 1) S_T^2 - \frac{K}{K - 1}\left(T_j - \bar{T}\right)^2\right] \end{aligned} \]

Similarly, we can estimate the MCSE of relative MSE and RMSE using the jack-knife technique. To estimate the MSE we need to estimate \(S_{V(j)}^2\), which represents the sample variance of the variance estimates leaving replicate \(j\) out. We calculate \(S_{V(j)}^2\) as

\[S_{V(j)}^2 = \frac{1}{K - 2} \left[(K - 1) S_V^2 - \frac{K}{K - 1}\left(V_j - \bar{V}\right)^2\right].\]

We can then estimate the jack-knife relative MSE and RMSE following the same logic as we described above and then calculate the MCSE.

Table 3 below lists relative performance measures for variance estimators.

Criterion | Measure | Definition | Estimate | MCSE |
---|---|---|---|---|

Relative Bias | Relative difference from true parameter | \(\text{E}(V) / \lambda\) | \(\bar{V} / S_T^2\) | \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rB_{(j)} - rB\right)^2}\) |

Relative MSE | Accuracy | \(\text{E}\left[(V - \lambda)^2\right]/ \lambda^2\) | \(\frac{(\bar{V} - S_T^2)^2 + S_V^2}{S_T^4}\) | \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rMSE_{(j)} - rMSE\right)^2}\) |

Relative RMSE | Accuracy | \(\sqrt{\text{E}\left[(V - \lambda)^2\right]/ \lambda^2}\) | \(\sqrt{\frac{(\bar{V} - S_T^2)^2 + S_V^2}{S_T^4}}\) | \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rRMSE_{(j)} - rRMSE\right)^2}\) |

The function `calc_relative_var()`

calculates the relative
performance criteria estimates and corresponding jack-knife MCSEs for a
variance estimator. The function requires `res_dat`

, a data
frame or tibble containing simulation results, `estimates`

,
the name of the column containing point estimates,
`var_estimates`

, the name of the column containing the
variance estimates, and `perfm_criteria`

, the criteria to be
evaluated:
`perfm_criteria = c("relative bias", "relative mse", "relative rmse")`

.

Below we demonstrate the use of the `calc_relative_var()`

function. The variance estimates are expected to be different when
derived from the conventional t-test as opposed to when they are derived
from the Welch t-test. We group by the sample sizes, mean difference,
and the t-test method. Note the difference between the conventional
t-test and the Welch t-test results, especially when the group sample
sizes are unequal.

If there are any convergence issues with estimation, please again
make sure that the estimation function returns `NA`

values
for the variance estimate and the point estimate. The
`calc_relative_var()`

function will omit the pair of point
estimate and variance estimate if either value is `NA`

before
calculating the performance measure and MCSE.

```
welch_res %>%
group_by(n1, n2, mean_diff, method) %>%
group_modify(~ calc_relative_var(.x, estimates = est, var_estimates = var)) %>%
kable(digits = 5)
```

n1 | n2 | mean_diff | method | K_relvar | rel_bias_var | rel_bias_var_mcse | rel_mse_var | rel_mse_var_mcse | rel_rmse_var | rel_rmse_var_mcse |
---|---|---|---|---|---|---|---|---|---|---|

50 | 50 | 0.0 | Welch t-test | 1000 | 0.99449 | 0.04264 | 0.02941 | 0.00259 | 0.17148 | 0.00756 |

50 | 50 | 0.0 | t-test | 1000 | 0.99449 | 0.04264 | 0.02941 | 0.00259 | 0.17148 | 0.00756 |

50 | 50 | 0.5 | Welch t-test | 1000 | 0.93705 | 0.04202 | 0.02839 | 0.00313 | 0.16849 | 0.00929 |

50 | 50 | 0.5 | t-test | 1000 | 0.93705 | 0.04202 | 0.02839 | 0.00313 | 0.16849 | 0.00929 |

50 | 50 | 1.0 | Welch t-test | 1000 | 1.02571 | 0.04508 | 0.03103 | 0.00534 | 0.17616 | 0.01512 |

50 | 50 | 1.0 | t-test | 1000 | 1.02571 | 0.04508 | 0.03103 | 0.00534 | 0.17616 | 0.01512 |

50 | 50 | 2.0 | Welch t-test | 1000 | 1.04180 | 0.04785 | 0.03211 | 0.00717 | 0.17918 | 0.01990 |

50 | 50 | 2.0 | t-test | 1000 | 1.04180 | 0.04785 | 0.03211 | 0.00717 | 0.17918 | 0.01990 |

50 | 70 | 0.0 | Welch t-test | 1000 | 1.02720 | 0.04555 | 0.02079 | 0.00452 | 0.14420 | 0.01561 |

50 | 70 | 0.0 | t-test | 1000 | 1.25701 | 0.05583 | 0.10150 | 0.03217 | 0.31859 | 0.05013 |

50 | 70 | 0.5 | Welch t-test | 1000 | 0.96512 | 0.04765 | 0.01795 | 0.00157 | 0.13398 | 0.00586 |

50 | 70 | 0.5 | t-test | 1000 | 1.18047 | 0.05831 | 0.06131 | 0.02440 | 0.24760 | 0.04864 |

50 | 70 | 1.0 | Welch t-test | 1000 | 0.95326 | 0.04270 | 0.01921 | 0.00245 | 0.13859 | 0.00885 |

50 | 70 | 1.0 | t-test | 1000 | 1.16667 | 0.05225 | 0.05714 | 0.02032 | 0.23905 | 0.04221 |

50 | 70 | 2.0 | Welch t-test | 1000 | 0.98888 | 0.04413 | 0.01938 | 0.00126 | 0.13919 | 0.00451 |

50 | 70 | 2.0 | t-test | 1000 | 1.21007 | 0.05409 | 0.07709 | 0.02594 | 0.27765 | 0.04640 |

When doing hypothesis tests, we are often interested in whether the Type 1 error rate is adequately controlled and whether the test has enough power to detect an effect size of substantive interest. The rejection rate of a hypothesis test captures the proportion of times the p-value is below a specified \(\alpha\) level—that is, the proportion of times we reject the null hypothesis. When the specified effect size is 0, we can examine Type 1 error rates and when the magnitude of the effect is greater than 0, we can examine power. We are also interested in confidence interval coverage, the proportion of intervals that contain the true parameter, and the interval width, which is an indicator of the precision of the interval estimator.

Table 4 below presents the performance criteria used to evaluate hypothesis tests. In the table, let \(P_k\) denote the p-value from simulation replication \(k\), for \(k = 1,...,K\). Suppose that the confidence intervals are for the target parameter \(\theta\) and have coverage level \(\beta\). Let \(A_k\) and \(B_k\) denote the lower and upper end-points of the confidence interval from simulation replication \(k\), and let \(W_k = B_k − A_k\), for \(k = 1,...,K\).

Criterion | Measure | Definition | Estimate | MCSE |
---|---|---|---|---|

Rejection Rate | Type 1 error or power | \(\rho_\alpha = Pr(P_k) < \alpha\) | \(r_\alpha = \frac{1}{K} \sum_{k=1}^K I(P_k < \alpha)\) | \(\sqrt{r_\alpha(1 - r_\alpha) / K}\) |

Coverage | Proportion of intervals containing true parameter | \(\omega_\beta = Pr(A \leq \theta \leq B)\) | \(c_\beta = \frac{1}{K}\sum_{k=1}^K I(A_k \leq \theta \leq B_k)\) | \(\sqrt{c_\beta (1 - c_\beta) / K}\) |

Width | Precision | \(\text{E}(W) = \text{E}(B - A)\) | \(\bar{W} = \bar{B} - \bar{A}\) | \(\sqrt{S_W^2 / K}\) |

Below we calculate the rejection rates for the hypothesis tests
conducted using the conventional two-sample t-test and the Welch t-test.
The null hypothesis is that the two groups have the same means. The
`calc_rejection()`

function requires a data frame or tibble
containing simulation results as the first argument. The second
argument, `p_values`

, requires the name of the column
containing p-values. The third argument, `alpha`

, lets the
user specify a value for \(\alpha\).
The default value is set to the conventional 0.05.

```
# using group_modify()
welch_res %>%
group_by(n1, n2, mean_diff, method) %>%
group_modify(~ calc_rejection(.x, p_values = p_val)) %>%
kable(digits = 5)
```

n1 | n2 | mean_diff | method | K_rejection | rej_rate | rej_rate_mcse |
---|---|---|---|---|---|---|

50 | 50 | 0.0 | Welch t-test | 1000 | 0.047 | 0.00669 |

50 | 50 | 0.0 | t-test | 1000 | 0.048 | 0.00676 |

50 | 50 | 0.5 | Welch t-test | 1000 | 0.335 | 0.01493 |

50 | 50 | 0.5 | t-test | 1000 | 0.340 | 0.01498 |

50 | 50 | 1.0 | Welch t-test | 1000 | 0.871 | 0.01060 |

50 | 50 | 1.0 | t-test | 1000 | 0.876 | 0.01042 |

50 | 50 | 2.0 | Welch t-test | 1000 | 1.000 | 0.00000 |

50 | 50 | 2.0 | t-test | 1000 | 1.000 | 0.00000 |

50 | 70 | 0.0 | Welch t-test | 1000 | 0.039 | 0.00612 |

50 | 70 | 0.0 | t-test | 1000 | 0.027 | 0.00513 |

50 | 70 | 0.5 | Welch t-test | 1000 | 0.426 | 0.01564 |

50 | 70 | 0.5 | t-test | 1000 | 0.341 | 0.01499 |

50 | 70 | 1.0 | Welch t-test | 1000 | 0.937 | 0.00768 |

50 | 70 | 1.0 | t-test | 1000 | 0.904 | 0.00932 |

50 | 70 | 2.0 | Welch t-test | 1000 | 1.000 | 0.00000 |

50 | 70 | 2.0 | t-test | 1000 | 1.000 | 0.00000 |

Below we calculate the confidence interval coverage rates and widths
for the estimates of the mean difference. The
`calc_coverage()`

function requires a data frame or tibble
containing the simulation results as the first argument. The second and
third arguments, `lower_bound`

and `upper_bound`

,
take in the name of the columns that contain the lower and upper bound
estimates of the confidence intervals. The `true_param`

argument requires the name of the column containing the true parameters.
Like `calc_absolute()`

, `calc_relative()`

and
`calc_relative_var()`

, `calc_coverage()`

also has
an argument, `perfm_criteria`

, where the user can specify
which criteria to evaluate:
`perfm_criteria = c("coverage", "width")`

.

```
# using group_modify()
welch_res %>%
mutate(params = mean_diff) %>%
group_by(n1, n2, mean_diff, method) %>%
group_modify(~ calc_coverage(.x, lower_bound = lower_bound, upper_bound = upper_bound, true_param = params)) %>%
kable(digits = 5)
```

n1 | n2 | mean_diff | method | K_coverage | coverage | coverage_mcse | width | width_mcse |
---|---|---|---|---|---|---|---|---|

50 | 50 | 0.0 | Welch t-test | 1000 | 0.953 | 0.00669 | 1.25266 | 0.00343 |

50 | 50 | 0.0 | t-test | 1000 | 0.952 | 0.00676 | 1.24697 | 0.00338 |

50 | 50 | 0.5 | Welch t-test | 1000 | 0.944 | 0.00727 | 1.25800 | 0.00336 |

50 | 50 | 0.5 | t-test | 1000 | 0.943 | 0.00733 | 1.25223 | 0.00332 |

50 | 50 | 1.0 | Welch t-test | 1000 | 0.953 | 0.00669 | 1.25741 | 0.00339 |

50 | 50 | 1.0 | t-test | 1000 | 0.952 | 0.00676 | 1.25169 | 0.00335 |

50 | 50 | 2.0 | Welch t-test | 1000 | 0.959 | 0.00627 | 1.25859 | 0.00335 |

50 | 50 | 2.0 | t-test | 1000 | 0.958 | 0.00634 | 1.25287 | 0.00331 |

50 | 70 | 0.0 | Welch t-test | 1000 | 0.961 | 0.00612 | 1.09943 | 0.00241 |

50 | 70 | 0.0 | t-test | 1000 | 0.973 | 0.00513 | 1.21433 | 0.00288 |

50 | 70 | 0.5 | Welch t-test | 1000 | 0.952 | 0.00676 | 1.09938 | 0.00234 |

50 | 70 | 0.5 | t-test | 1000 | 0.972 | 0.00522 | 1.21412 | 0.00276 |

50 | 70 | 1.0 | Welch t-test | 1000 | 0.940 | 0.00751 | 1.09791 | 0.00239 |

50 | 70 | 1.0 | t-test | 1000 | 0.963 | 0.00597 | 1.21280 | 0.00283 |

50 | 70 | 2.0 | Welch t-test | 1000 | 0.951 | 0.00683 | 1.09886 | 0.00245 |

50 | 70 | 2.0 | t-test | 1000 | 0.971 | 0.00531 | 1.21376 | 0.00289 |

Generally, the associated MCSE should be small compared to the performance measure. Below is an example from Frane (2015) of how to write up MCSE results as part of simulation study results. Frane (2015) compared various methods to control Type 1 error rates when conducting analysis on multiple outcome variables. In describing the simulation results, he wrote:

Only Bonferroni and MP strictly controlled the PFER; the observed maximum PFER was 0.050 for Bonferroni, 0.051 for Sidák, 0.061 for Holm, 0.100 for Hochberg, and 0.050 for MP (estimated SE ≤ 0.0002 for all estimates).

The PFER refers to per family error rate, the expected number of Type 1 errors out of \(m\) comparisons. MP refers to multivariate analysis of variance (MANOVA) protected tests. The SE refers to the MCSE. The number of replications was 1,000,000 in Frane (2015). We note that the number is quite high and generally 1,000 to 10,000 replications are enough. The resulting MCSEs in Frane (2015), therefore, are exceedingly small compared to the values for PFER.

For more information for performance criteria and MCSE, we recommend Morris et al. (2019).

Efron, B., & Stein, C. (1981). The jackknife estimate of variance.
*The Annals of Statistics*, 586–596.

Frane, A. V. (2015). Power and type i error control for univariate
comparisons in multivariate two-group designs. *Multivariate
Behavioral Research*, *50*(2), 233–247.

Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using
simulation studies to evaluate statistical methods. *Statistics in
Medicine*, *38*(11), 2074–2102.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D.,
François, R., … Yutani, H. (2019). Welcome to the tidyverse. *Journal of Open Source
Software*, *4*(43), 1686. https://doi.org/10.21105/joss.01686