---
title: "Two-sample normal sample size"
output: rmarkdown::html_vignette
bibliography: gsDesign.bib
vignette: >
  %\VignetteIndexEntry{Two-sample normal sample size}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  dev = "svg",
  fig.width = 7.2916667,
  fig.asp = 0.618,
  fig.align = "center",
  out.width = "80%"
)
```

## Introduction

```{r, echo=FALSE, message=FALSE, warning=FALSE}
library(gsDesign)
library(tidyr)
library(knitr)
```

Limited support is provided for 2-sample design with a normally distributed random variable as the outcome.
Users are encouraged to look at guidance such as in @JTBook.
We provide a tool where for a large sample case where a reasonable estimate of standard deviation is available, a reasonable sample size can be computed based straightforward distribution theory outlined below.

## The problem considered

The overall sample size notation used for **gsDesign** is to consider a standardized effect size parameter which is referred to as $\theta$ in @JTBook.
We begin with the 2-sample normal problem where we assume a possibly different standard deviation in each treatment group.
For $j = 1, 2$, we let $X_{j, i}$, $i = 1, 2, \ldots n_j$ represent independent and identically distributed observations following a normal distribution with mean $\mu_j$ and standard deviation $\sigma_j$.
The natural parameter for comparing the two distributions is

$$\delta = \mu_2 - \mu_1$$

and we wish to test if $\delta > 0$ in a one-sided testing scenario to test for superiority of treatment 2 over treatment 1.
We could also consider testing, say, $\delta > \delta_0$ for a non-inferiority scenario with $\delta_0<0$ or **super superiority** if $\delta_0>0$.
While normally a t-test would be used for this, for large sample sizes this would be nearly equivalent to a Z-test defined by:

$$Z=\frac{\bar X_2 - \bar X_1-\delta_0}{\sqrt{\sigma^2_2/n_2 + \sigma_1^2/n_1}}\approx \frac{\bar X_2 - \bar X_1}{\sqrt{s^2_2/n_2 + s_1^2/n_1}}=t$$
where $\bar X_j$ is the sample mean and $s_j^2$ is the sample variance for group $j=1,2$.
The far right hand side of this is Welch's t-test.
For our examples we use this $t$-test and show that the sample size computation based on the $Z$-test above works well for the chosen problems.

## Sample size

Thus, $n_2=rn/(1+r)$, $n_1=n/(1+r)$ and when $r=1$ we have $n_1=n_2=n/2$.
Now that we have completed needed notation, those not interested in the theory behind the sample size and power calculation used may skip the rest of this section.

We let $$\sigma^2=(1+r)(\sigma_1^2+\sigma_2^2/r)$$
and define
$$ \theta= (\delta -\delta_0)/\sigma.$$
Under the given assumptions,
$$Z \sim \text{Normal}\left(\sqrt n\theta,1\right).$$
Under the null hypothesis that $\delta=\delta_0$, we have $Z\sim \text{Normal}(0,1)$.
Thus, regardless of $n$ we have
$$P_0[Z\ge \Phi^{-1}(1-\alpha)]=\alpha.$$
Under the alternate hypothesis that $\delta=\delta_1$ and we denote a corresponding $\theta_1$.
We define the type II error $\beta$ and power $1-\beta$ by

$$
\begin{align}
1-\beta =& P_1[Z\ge \Phi^{-1}(1-\alpha)]\\
=& P[Z-\sqrt n\theta_1\ge \Phi^{-1}(1-\alpha)-\sqrt n\theta_1]\\
=&\Phi(\Phi^{-1}(1-\alpha)-\sqrt n\theta_1)).
\end{align}$$

If the power $1-\beta$ is fixed, we can invert this formula to compute sample size with:

$$n= \left(\frac{\Phi^{-1}(1-\beta)+\Phi^{-1}(1-\alpha)}{\theta_1}\right)^2.$$

For 2-sided testing, we simply substitute $\alpha/2$ for $\alpha$ in the above two formulas.

## Examples

We consider two examples to check the above formulas vs. `nNormal()`.
We then confirm that the approximation is working well by simulating and confirming that the power and Type I error approximations are useful.
Finally, we provide a simple group sequential design example.

### Sample size

We consider an example with $\sigma_2=1.25$, $\sigma_1=1.6$, $\delta=0.8$ and $\delta_0=0$.
We let the sample size ratio be 2 experimental group observations per control observation.
We compute sample size with `nNormal()` assuming one-sided Type I error $\alpha=0.025$ and 90% power ($1-\beta=0.9$).

Checking using the sample size formula above, we have:

```{r}
r <- 2
sigma <- sqrt((1 + r) * (1.6^2 + 1.25^2 / r))
theta <- 0.8 / sigma
((qnorm(.9) + qnorm(.975)) / theta)^2
```

### Power

Now, assume we let the sample size be 200 and compute power under the same scenario.

```{r}
nNormal(delta1 = 0.8, sd = 1.6, sd2 = 1.25, alpha = 0.025, n = 200, ratio = 2)
```

From the power formula above, we duplicate this with:

```{r}
pwr <- pnorm(qnorm(.975) - sqrt(200) * theta, lower.tail = FALSE)
pwr
```

If we want to plot power for a variety of sample sizes, we can input `n` as a vector:

```{r}
n <- 100:200
pwrn <- nNormal(delta1 = 0.8, sd = 1.6, sd2 = 1.25, alpha = 0.025, n = n, ratio = 2)
plot(n, pwrn, type = "l")
```

Alternatively, you could fix sample size at 200 and plot power under different treatment effect assumptions:

```{r}
delta1 <- seq(.5, 1, .025)
pwrdelta1 <- nNormal(delta1 = delta1, sd = 1.6, sd2 = 1.25, alpha = 0.025, n = 200, ratio = 2)
plot(delta1, pwrdelta1, type = "l")
```

### Verification with simulation

Rather than simulate individual observations, we will take advantage of the fact that for $j=1,2$

$$\bar X_j\sim \text{Normal}(\mu_j,\sigma_j^2/n_j)$$

and

$$(n_j-1)s_j^2/\sigma_j^2=\sum_{i=1}^{n_j} (X_{ij}-\bar X_j)/\sigma^2 \sim \chi ^2_{n_j-1}$$

are independent.
Thus, we can simulate trial power with $n=200$ 1 million times with a t-statistic with unequal variances quickly as follows under the alternate hypothesis:

```{r}
nsim <- 1000000
delta <- 0.8
sd1 <- 1.6
sd2 <- 1.25
n1 <- 67
n2 <- 133
deltahat <- rnorm(n = nsim, mean = delta, sd = sd1 / sqrt(n1)) -
  rnorm(n = nsim, mean = 0, sd = sd2 / sqrt(n2))
s <- sqrt(
  sd1^2 * rchisq(n = nsim, df = n1 - 1) / (n1 - 1) / n1 +
    sd2^2 * rchisq(n = nsim, df = n2 - 1) / (n2 - 1) / n2
)
z <- deltahat / s
mean(z >= qnorm(.975))
```

The standard error for this simulation power calculation is approximately

```{r}
sqrt(pwr * (1 - pwr) / nsim)
```

suggesting we should be within less than about 0.001 if the actual power, which suggests the normal power approximation is reasonable for this scenario.

### Group sequential design

Now we derive a group sequential design under the above scenario.
We will largely use default parameters and show two methods.
For the first, we plug in the fixed sample size above as follows:

```{r}
d <- gsDesign(
  k = 2,
  n.fix = nNormal(delta1 = 0.8, sd = 1.6, sd2 = 1.25, alpha = 0.025, beta = .1, ratio = 2),
  delta1 = 0.8
)
d %>%
  gsBoundSummary(deltaname = "Mean difference") %>%
  kable(row.names = FALSE)
```

A textual summary of the design is given by:

```{r,results='asis'}
cat(summary(d))
```

We can get the same answer by plugging in the standardized effect size we computed above:

```{r}
gsDesign(
  k = 2,
  delta = theta,
  delta1 = 0.8
) %>%
  gsBoundSummary(deltaname = "Mean difference") %>%
  kable(row.names = FALSE)
```

We leave it to the reader to verify the properties of the above design using simulation as in the fixed design example.

## References
