The `bayes_adcock()`

function determines the assurance
under precision-based conditions discussed in Adcock (1997). Recall in
the frequentist setting, if \(X_i \sim
N(\theta, \sigma^2)\) for \(i = 1,...,
n\) observations and variance \(\sigma^2\) is known, the precision can be
calculated using \(d =
z_{1-\alpha/2}\frac{\sigma}{\sqrt{n}}\), where \(z_{1-\alpha/2}\) is the critical value for
the \(100(1-\alpha/2)\%\) quartile of
the standard normal distribution. Simple rearranging leads to the
following expression for sample size: \[\begin{equation}
n = z_{1-\alpha/2}^2\frac{\sigma^2}{d^2}\;.
\end{equation}\]

**Analysis Stage**

Given a random sample with mean \(\bar{x}\), suppose the goal is to estimate population mean \(\theta\).

To formulate this problem in the Bayesian setting, suppose \(x_1, \cdots, x_n\) is a random sample from \(N(\theta, \sigma^2)\) and the sample mean is distributed as \(\bar{x}|\theta, \sigma^2 \sim N(\theta, \sigma^2/n)\). The analysis objective is to observe that the absolute difference between \(\bar{x}\) and \(\theta\) falls within a margin of error no greater than \(d\). Given data \(\bar{x}\) and a pre-specified confidence level \(\alpha\), the assurance can be formally expressed as \[\begin{equation} \delta = P_{\bar{x}}\{\bar{x}: P(|\bar{x} - \theta| \leq d) \geq 1-\alpha\}\;. \end{equation}\] We assign \(\theta \sim N(\theta_0^{(a)}, \sigma^2/n_a)\) as the analysis prior, where \(n_a\) quantifies the amount of prior information we have for \(\theta\). The posterior of \(\theta\) is obtained by taking the product of the prior and likelihood, resulting in \[\begin{equation} N\Bigg(\bar{x} {\left| \theta, \frac{\sigma^2}{n}\right.}\Bigg) \times N\Bigg(\theta {\left | \theta_0^{(a)}, \frac{\sigma^2}{n_a}\right.}\Bigg) = N\Bigg(\theta {\left | \lambda, \frac{\sigma^2}{n_a+n}\right.}\Bigg), \end{equation}\] where \(\lambda = \frac{n\bar{x}+n_a\theta_0^{(a)}}{n_a+n}\). From here we can further evaluate the condition using parameters from the posterior of \(\theta\) to obtain a more explicit version of the analysis stage objective. From \(P(|\bar{x} - \theta| \leq d) = P(\bar{x} - d \leq \theta \leq \bar{x} + d)\), we can standardize all components of the inequality using the posterior distribution of \(\theta\), eventually leading to \[\begin{equation} \label{eq:adcock_region_simplified} \delta = \left\{ \bar{x}: \Phi\left[\frac{\sqrt{n_a + n}}{\sigma} (\bar{x} + d - \lambda)\right] - \Phi\left[\frac{\sqrt{n_a+n}}{\sigma}(\bar{x} - d - \lambda)\right] \geq 1-\alpha \right\}. \end{equation}\]

**Design Stage**

In the design stage, we need to construct a protocol for sampling
data that will be used to evaluate the analysis objective. This is
achieved by first setting a separate design stage prior on \(\theta\) such that \(\theta \sim N(\theta_0^{(d)},
\sigma^2/n_d)\), where \(n_d\)
quantifies our degree of belief towards the population from which the
sample will be drawn. Given that \(\bar{x}|\theta, \sigma^2 \sim N(\theta,
\sigma^2/n)\), the marginal distribution of \(\bar{x}\) can be computed using
straightforward substitution based on \(\bar{x} = \theta + \epsilon;\) \(\epsilon \sim N(0, \sigma^2/n)\) and \(\theta = \theta_0^{(d)} + \omega;\) \(\omega \sim N(0, \sigma^2/n_d)\).

Substitution \(\theta\) into the
expression for \(\bar{x}\) gives us
\(\bar{x}=\theta_0^{(d)} + (\omega +
\epsilon);\) \((\omega + \epsilon) \sim
N\Big(0, \frac{\sigma^2(n_d+n)}{n_dn}\Big) = N(0, \sigma^2/p)\),
where \(1/p = 1/n_d + 1/n\). The
marginal of \(\bar{x}\) is therefore
\(N(\bar{x}|\theta_0^{(d)},
\sigma^2/p)\), where we will be iteratively drawing samples from
to check if they satisfy the condition outlined in the analysis
stage.

First, load in the bayesassurance package.

`library(bayesassurance)`

Specify the following inputs:

`n`

: sample size (either vector or scalar).`d`

: precision level in in analysis objective \(|\bar{x} - \theta| \leq d\)`mu_beta_a`

: analysis stage mean`mu_beta_d`

: design stage mean`n_a`

: sample size at analysis stage. Quantifies the amount of prior information we have for parameter \(\theta\).`n_d`

: sample size at design stage. Quantifies the amount of prior information we have for where the data is being generated from.`sig_sq`

: known variance \(\sigma^2\).`alpha`

: significance level`mc_iter`

: number of MC samples evaluated under the analysis objective.

As an example, we assign the following set of arbitrary inputs to
pass into `bayes_adcock()`

and save the output as
`out`

. Notice that we assign a vector of values to sample
size `n`

and choose arbitrary values for the remaining
parameters.

```
library(ggplot2)
<- seq(20, 145, 5)
n
set.seed(20)
<- bayesassurance::bayes_adcock(n = n, d = 0.20, mu_beta_a = 0.64, mu_beta_d = 0.9,
out n_a = 20, n_d = 10, sig_sq = 0.265, alpha = 0.05,
mc_iter = 10000)
```

Within `out`

contains a list of three objects:

`assurance_table`

: table of sample sizes and corresponding assurance values.`assur_plot`

: plot of assurance values. Returned if length of`n`

is greater than 1.`mc_iter`

: number of MC samples generated.

The first six entries of the resulting power table is shown by
calling `head(out$assurance_table)`

.

`head(out$assurance_table)`

n | Assurance |
---|---|

20 | 0.2378 |

25 | 0.3009 |

30 | 0.3664 |

35 | 0.4376 |

40 | 0.5267 |

45 | 0.5981 |

The plot of assurance points is produced using the
`ggplot2`

package. It displays the inputted sample sizes on
the x-axis and the resulting assurance values on the y-axis.

`$assurance_plot out`

To demonstrate how the frequentist setting is a special case of the Bayesian framework constructed under precision-based conditions discussed above, we refer back to the fundamental sample size formula, \[\begin{equation} n = z_{1-\alpha/2}^2\frac{\sigma^2}{d^2}\;, \end{equation}\] and perform some simple rearranging to obtain \[\begin{equation} \label{eq:freq_cond} 2\Phi\Big[\frac{\sqrt{n}}{\sigma}d\Big]-1 \geq 1-\alpha\;, \end{equation}\] which we denote as the frequentist condition. Comparing this to the assurance formula we had derived before, \[\begin{equation} \delta = \left\{ \bar{x}: \Phi\left[\frac{\sqrt{n_a + n}}{\sigma} (\bar{x} + d - \lambda)\right] - \Phi\left[\frac{\sqrt{n_a+n}}{\sigma} (\bar{x} - d - \lambda)\right] \geq 1-\alpha \right\}. \end{equation}\] we notice that both expressions have \(1-\alpha\) isolated on the right hand side. Taking \(n_a = 0\) in our assurance formula, simplification proceeds as \[\begin{align*} &\delta = \left\{ \bar{x}: \Phi\left[\frac{\sqrt{n_a + n}}{\sigma} (\bar{x} + d - \lambda)\right] - \Phi\left[\frac{\sqrt{n_a+n}}{\sigma}(\bar{x} - d - \lambda)\right] \geq 1-\alpha \right\} \\ &\xrightarrow[]{n_a = 0} \left\{ \bar{x}: \Phi\left[\frac{\sqrt{n}} {\sigma}d\right] - \Phi \left[-\frac{\sqrt{n}}{\sigma}d\right] \geq 1 - \alpha \right\} = \left\{ \bar{x}: 2\Phi \left[\frac{\sqrt{n}}{\sigma}d\right] - 1 \geq 1 - \alpha \right\}. \end{align*}\] Notice that the resulting term on the left hand side is now equivalent to that of the frequentist condition. This suggests that if we let \(\theta\) take on a weak analysis prior, i.e. \(n_a = 0\), we revert back to the frequentist setting in the analysis stage.

We construct a simple function that returns the left hand side of the frequentist condition expressed above.

```
<- function(n, d, sig_sq){
freq_cond <- sqrt(sig_sq)
sigma <- 2 * pnorm(d / (sigma / sqrt(n))) - 1
lhs return(lhs)
}
```

Minor modifications can be implemented in the
`bayes_adcock()`

function such that only the left-hand term
is returned (function edits not shown). We will denote this modified
function as `adcock_lhs()`

. Assigning \(n_a = 0\), \(d =
0.15\) and arbitrary values to all other inputs, we obtain
left-hand side values for both frequentist and Bayesian conditions,
which we denote as `lhs1`

and `lhs2`

respectively.
Plots are subsequently produced using the `ggplot2`

package.

```
library(ggplot2)
<- seq(5, 300, 5)
n <- 1e-8
n_a <- n_a
n_d <- 0.15
d set.seed(1)
<- runif(1, 0, 1)
sig_sq <- runif(1, 0, 1)
mu_beta_d <- runif(1, 0, 1)
mu_beta_a
<- freq_cond(n, d, sig_sq)
lhs1 <- sapply(n, function(i) adcock_lhs(n = i, d, mu_beta_a, mu_beta_d, n_a,
lhs2
n_d, sig_sq))
<- as.data.frame(cbind(n, lhs1))
df1 <- as.data.frame(cbind(n, lhs2))
df2
<- ggplot(df1) + geom_line(data = df1, alpha = 0.5, aes(x = n, y = lhs1,
p color="Adcock Cond"), lwd = 1.2)
<- p + geom_point(data = df2, alpha = 0.5, aes(x = n, y = lhs2,
p1 color="Bayesian Sim"),lwd=1.2) + ylab("Probability of Meeting Analysis Objective") +
xlab("Sample Size n") + ggtitle("Comparison Between Adcock Conditiion and Bayesian
Simulation Results")
<- p1 + geom_label(
p1 label="d = 0.15",
x=25,
y=0.98,
color = "black", size = 3
)
```

We repeat this using various precision values to showcase the overlapping scenario. Notice that the overall assurance values tend to decrease as the precision is lowered.

The `bayes_sim_betabin()`

measures the assurance of
observing a significant difference between two proportions.

Let \(p_i, i = 1, 2\) denote two independent proportions. In the frequentist setting, suppose we want to test \(H_0: p_1 - p_2 = 0\) vs. \(H_a: p_1 - p_2 \neq 0\). The goal is to detect whether there is a significant difference between the two proportions. One way to check this condition is to see if \(0\) is contained within the confidence bands of the true difference in proportions, i.e. \[ 0 \in \displaystyle (\hat{p_1} - \hat{p_2}) \pm z_{1-\alpha/2}(SE(\hat{p_1})^2 + SE(\hat{p_2})^2)^{1/2},\] where \(z_{1-\alpha/2}\) denotes the critical region, and \(SE(\hat{p_i})\) denotes the standard error of \(p_i\). An interval without \(0\) contained within the confidence bands suggests there exists a significant difference between the two proportions.

The Bayesian setting uses posterior credible intervals as an analog to the frequentist confidence interval approach. In this setting, two distinct priors are assigned to \(p_1\) and \(p_2\) such that \(p_i \sim Beta(\alpha_i, \beta_i)\) for \(i = 1,2\).

Let \(X\) be a random variable taking on values \(x = 0, 1,..., n\) denoting the number of favorable outcomes out of \(n\) trials. The proportion of favorable outcomes is \(p = x/n\). Suppose a Beta prior is assigned to \(p\) such that \(p \sim Beta(\alpha, \beta)\). The prior mean and variance are respectively \(\mu_{prior} = \frac{\alpha}{\alpha + \beta}\) and \(\sigma^2_{prior} = \frac{\alpha\beta}{(\alpha + \beta)^2(\alpha + \beta + 1)}\). Conveniently, given that \(p\) is assigned a Beta prior, the posterior of \(p\) also takes on a Beta distribution with mean and variance \[\begin{align} \begin{split} \mu_{posterior} &= \frac{\alpha + x}{\alpha + \beta + n}\\ \sigma^2_{posterior} &= \frac{(\alpha + x)(\beta + n - x)}{(\alpha + \beta + n)^2(\alpha + \beta + n + 1)}. \end{split} \end{align}\]

**Analysis Stage**

Within the analysis stage, we assign two beta priors for \(p_1\) and \(p_2\) such that \(p_i \sim Beta(\alpha_i, \beta_i), i = 1, 2\). Letting \(p_{d} = p_1 - p_2\) and \(p_{\text{post}}\) and \(\text{var}(p)_{\text{post}}\) respectively denote the posterior mean and variance of \(p_{d}\), it is straightforward to deduce that \(p_{\text{post}} = \frac{\alpha_1 + x_1}{\alpha_1 + \beta_1 + n_1} - \frac{\alpha_2 + x_2}{\alpha_2 + \beta_2 + n_2}\) and \(\text{var}(p)_{\text{post}} = \frac{(\alpha_1 + x_1)(\beta_1 + n_1 - x_1)}{(\alpha_1 + \beta_1 + n_1)^2(\alpha_1 + \beta_1 + n_1 + 1)} + \frac{(\alpha_2 + x_2)(\beta_2 + n_2 - x_2)}{(\alpha_2 + \beta_2 + n_2)^2(\alpha_2 + \beta_2 + n_2 + 1)}.\) The resulting \(100(1-\alpha)\%\) credible interval therefore equates to \(p_{\text{post}} \pm z_{1-\alpha/2} \sqrt{\text{var}(p)_{\text{post}}}\), which, similar to the frequentist setting, would be used to check whether \(0\) is contained within the credible interval bands as part of our inference procedure. This translates to become our analysis objective, where we are interested in assessing if each iterated sample outputs a credible interval that does not contain \(0\). We can denote this region of interest as \(R(p)\) such that \[\begin{equation} \label{eq: region_cond} %R(p; \alpha_1, \alpha_2, \beta_1, \beta_2, n_1, n_2, x_1, x_2) R(p) = \left\{p: 0 \not\in \left(p_{\text{post}} - z_{1-\alpha/2} \sqrt{\text{var}(p)_{\text{post}}}, \quad p_{\text{post}} + z_{1-\alpha/2} \sqrt{\text{var}(p)_{\text{post}}}\right)\right\}. \end{equation}\] It follows that the corresponding assurance for assessing a significant difference in proportions can be computed as \[ \delta = P \left\{p_d: 0 \not\in \left(p_{\text{post}} - z_{1-\alpha/2} \sqrt{\text{var}(p)_{\text{post}}}, \quad p_{\text{post}} + z_{1-\alpha/2} \sqrt{\text{var}(p)_{\text{post}}}\right) \geq 1-\alpha \right\}. \]

**Design Stage**

In the design stage, frequency counts, \(x_1\) and \(x_2\), are observed from samples of size \(n_1\) and \(n_2\) based on given probabilities, \(p_1\) and \(p_2\), that are passed in the analysis stage. Once \(p_1\) and \(p_2\) are assigned, \(x_1\) and \(x_2\) are randomly generated from their corresponding binomial distributions such that \(x_i \sim \text{Bin}(n_i, p_i), i = 1,2\). The posterior credible intervals are subsequently computed to undergo assessment in the analysis stage. These steps are repeated iteratively starting from the generation of \(x_1\) and \(x_2\) values. The proportion of iterations with results that fall in region \(R(p)\) equates to the assurance.

First, load in the bayesassurance package.

`library(bayesassurance)`

Specify the following inputs:

`n1`

: sample size of first group (scalar)`n2`

: sample size of second group (scalar)`p1`

: proportion of successes in first group. Takes on a`NULL`

assignment if unknown.`p2`

: proportion of successes in second group. Takes on a`NULL`

assignment if unknown.`alpha_1`

,`beta_1`

: shape parameters for the distribution of`p1`

if`p1`

is not known or given, i.e. \(p1 \sim \text{Beta}(\alpha_1, \beta_1)\)`alpha_2`

,`beta_2`

: shape parameters for the distribution of`p2`

if`p2`

is not known or given, i.e. \(p2 \sim \text{Beta}(\alpha_2, \beta_2)\)`sig_level`

: significance level`alt`

: a character string specifying the alternative hypothesis when comparing`p1`

and`p2`

. The options are “two.sided” (default), “greater” or “less”.

As an example, we assign the following set of arbitrary inputs to
pass into `bayes_sim_betabin()`

and save the result as
`out`

.
We let `n1 = n2`

for the sake
of simplicity and choose arbitrary values for the remaining
parameters.

```
<- seq(600, 1000, 10)
n
set.seed(30)
<- bayesassurance::bayes_sim_betabin(n1 = n, n2 = n, p1 = 0.25, p2 = 0.2,
out alpha_1 = 0.5, beta_1 = 0.5, alpha_2 = 0.5,
beta_2 = 0.5, sig_level = 0.05, alt = "two.sided")
```

Within `out`

contains a list of assurance values. We can
create a simple table to show the corresponding sample sizes.

`head(out$assurance_table)`

n1 | n2 | Assurance |
---|---|---|

600 | 600 | 0.5482 |

610 | 610 | 0.5564 |

620 | 620 | 0.5662 |

630 | 630 | 0.5616 |

640 | 640 | 0.5736 |

650 | 650 | 0.5840 |

We will demonstrate cases where the Bayesian and frequentist settings align and perform similarly in behavior.

**Case 1: Known Probabilities**

If \(p_1\) and \(p_2\) are known, there is no need rely on the Beta distribution to randomly generate values. We still, however, need to assign shape parameters in the Bayesian setting as they are needed to compute the posterior credible intervals. We will use Haldane’s priors (flat priors) for the Beta distribution, in which \(\alpha\) and \(\beta\) are set to 0.5.

Recall the sample size formula for assessing differences in proportions in the frequentist setting, \[ n = \frac{(z_{1-\alpha/2} + z_{\beta})^2(p_1(1-p_1) + p_2(1-p_2))}{(p_1 - p_2)^2}, \] where \(n = n_1 = n_2\). Simple rearragements and noting that \(-(z_{1-\alpha/2} + z_{\beta}) = z_{1-\beta} - z_{1-\alpha/2}\) lead us to obtain \[\begin{multline*} %\frac{\sqrt{n}(p_1 - p_2)}{\sqrt{p_1(1-p_1) + p_2(1-p_2)}} = z_{1-\beta} - z_{1-\alpha/2} \frac{\sqrt{n}(p_1 - p_2)}{\sqrt{p_1(1-p_1) + p_2(1-p_2)}} + z_{1-\alpha/2} = z_{1-\beta} \implies \text{Power} = 1 - \beta = \\ \Phi\left(\frac{\sqrt{n}(p_1 - p_2)}{\sqrt{p_1(1-p_1) + p_2(1-p_2)}} + z_{1-\alpha/2}\right). \end{multline*}\]

We create a simple function corresponding to this power formula:

```
<- function(n, p1, p2, sig_level){
propdiffCI_classic <- p1 - p2
p <- pnorm(sqrt(n / ((p1*(1-p1)+p2*(1-p2)) / (p)^2)) - qnorm(1-sig_level/2))
power return(power)
}
```

We define a vector of sample sizes \(n\) that are assigned to both \(n_1\) and \(n_2\) in the Bayesian setting. Arbitrary values are assigned to \(p_1\) and \(p_2\) along with vague shape parameters, i.e. \(\alpha_1 = \alpha_2 = \beta_1 = \beta_2 = 0.5\). We plot the power curve and assurance points on the same plot.

```
#########################################################
# alpha1 = 0.5, beta1 = 0.5, alpha2 = 0.5, beta2 = 0.5 ##
#########################################################
<- seq(40, 1000, 10)
n
<- sapply(n, function(i) propdiffCI_classic(n=i, 0.25, 0.2, 0.05))
power_vals <- as.data.frame(cbind(n, power_vals))
df1
<- bayes_sim_betabin(n1 = n, n2 = n, p1 = 0.25,
assurance_out p2 = 0.2, alpha_1 = 0.5, beta_1 = 0.5, alpha_2 = 0.5, beta_2 = 0.5,
sig_level = 0.05, alt = "two.sided")
<- assurance_out$assurance_table
df2 colnames(df2) <- c("n1", "n2", "Assurance")
<- ggplot(df1, alpha = 0.5, aes(x = n, y = power_vals, color="Frequentist Power"))
p1 <- p1 + geom_line(alpha = 0.5, aes(x = n, y = power_vals,
p1 color="Frequentist Power"), lwd = 1.2)
<- p1 + geom_point(data = df2, alpha = 0.5, aes(x = .data$n1, y = .data$Assurance,
p2 color = "Bayesian Assurance"), lwd = 1.2) + ylab("Power/Assurance") +
xlab(~ paste("Sample Size n = ", "n"[1], " = ", "n"[2])) +
ggtitle("Power/Assurance Curves of Difference in Proportions")
<- p2 + geom_label(aes(525, 0.6,
p2 #label="~alpha[1] == ~beta[1] == 0.5~and~alpha[2] == ~beta[2] == 0.5"),
label = "~p[1]-p[2] == 0.05"), parse = TRUE,
color = "black", size = 3)
```

Notice that the power and simulated assurance points overlap. This relies on the fact that the normal distribution can be used to approximate binomial distributions for large sample sizes given that the Beta distribution is approximately normal when its parameters \(\alpha\) and \(\beta\) are set to be equal.

We repeat these steps for different \(p_1\) and \(p_2\) values while maintaining the same shape parameters.

**Case 2: Unknown Probabilities**

The following code segments are plots demonstrate overlapping behaviors of the frequentist and Bayesian settings when \(p_1\) and \(p_2\) are not known. We try this for various shape parameters, recalling the fact that the normal distribution can be used to approximate binomial distributions for large sample sizes given that the Beta distribution is approximately normal when its parameters \(\alpha\) and \(\beta\) are set to be equal.

We will modify the frequentist power function to include shape parameters as inputs.

```
<- function(n, p1, p2, alpha_1, beta_1, alpha_2, beta_2, sig_level){
propdiffCI_classic set.seed(1)
if(is.null(p1) == TRUE & is.null(p2) == TRUE){
<- rbeta(n=1, alpha_1, beta_1)
p1 <- rbeta(n=1, alpha_2, beta_2)
p2 else if(is.null(p1) == TRUE & is.null(p2) == FALSE){
}<- rbeta(n=1, alpha_1, beta_1)
p1 else if(is.null(p1) == FALSE & is.null(p2) == TRUE){
}<- rbeta(n=1, alpha_2, beta_2)
p2
}<- p1 - p2
p
<- pnorm(sqrt(n / ((p1*(1-p1)+p2*(1-p2)) / (p)^2)) - qnorm(1-sig_level/2))
power return(power)
}
```

The following code chunk assigns `NULL`

values to \(p_1\) and \(p_2\) in both functions and assigns equal
sets of shape parameters for the Beta distributions.

```
################################################
# alpha1 = 2, beta1 = 2, alpha2 = 6, beta2 = 6 #
################################################
library(ggplot2)
<- propdiffCI_classic(n=n, p1 = NULL, p2 = NULL, 2, 2, 6, 6, 0.05)
power_vals <- as.data.frame(cbind(n, power_vals))
df1
<- bayes_sim_betabin(n1 = n, n2 = n,
assurance_vals p1 = NULL, p2 = NULL, alpha_1 = 2, beta_1 = 2, alpha_2 = 6,
beta_2 = 6, sig_level = 0.05, alt = "two.sided")
<- assurance_vals$assurance_table
df2
<- ggplot(df1, aes(x = n, y = power_vals))
p1 <- p1 + geom_line(alpha = 0.5, aes(x = n, y = power_vals,
p1 color="Frequentist Power"), lwd = 1.2)
<- p1 + geom_point(data = df2, alpha = 0.5, aes(x = n1, y = Assurance,
p2 color = "Bayesian Assurance"), lwd = 1.2) +
ylab("Power/Assurance") + xlab(~ paste("Sample Size n = ", "n"[1], " = ", "n"[2])) +
ggtitle("Power/Assurance Curves for Difference in Proportions",
subtitle = expression(paste(p[1], "~ Beta(", alpha[1], ", ", beta[1], "); ",
2], "~ Beta(", alpha[2], ", ", beta[2], ")")))
p[
<- p2 + geom_label(aes(75, 1.03, label="~alpha[1] == ~beta[1] == 0.5~and~alpha[2] == ~beta[2] == 0.5"), parse=TRUE,
p2 color = "black", size = 2.5) + ylim(0.45, 1.03) + xlim(40, 350)
```

We repeat these steps for various shape parameters. Notice that the assurance points do not align perfectly to the corresponding power curves as we rely on an approximate relationship rather than identifying prior assignments that allow direct ties to the frequentist case. The approximations are still very similar.