---
title: "Conditional error spending functions"
output: rmarkdown::html_vignette
bibliography: gsDesign.bib
vignette: >
  %\VignetteIndexEntry{Conditional error spending functions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  dev = "svg",
  fig.width = 7,
  fig.asp = 1,
  fig.align = "center",
  out.width = "65%"
)
```

## Introduction

We describe conditional error spending functions for group sequential designs.
These functions are used to calculate conditional error spending boundaries
for group sequential designs using spending functions proposed by @xi2019additive.
Note that for all spending functions, for $t>1$ we define the spending function
as if $t = 1$.
For $\gamma \in [0, 1]$, we define

$$
z_\gamma = \Phi^{-1}(1 - \gamma)
$$

where $\Phi$ is the standard normal cumulative distribution function.

There are 3 spending functions proposed by @xi2019additive, which we will
refer to as Method 1 (`sfXG1()`), Method 2 (`sfXG2()`), and Method 3 (`sfXG3()`).
When there is a single interim analysis, conditional error from Method 1 is
almost exactly the same as the parameter $\gamma$ but it allows a narrower
range of $\gamma$. Method 2 provides less accurate approximation but allows
a wider range of $\gamma$.
Method 3 was proposed to approximate Pocock bounds with equal bounds on the Z-scale.
We replicate spending function bounds of @xi2019additive along with
corresponding conditional error computations below.
We also compare results to other commonly used spending functions.

## Implementation in gsDesign

```{r, message=FALSE, warning=FALSE}
library(gsDesign)
library(tibble)
library(dplyr)
library(gt)
```

### Method 1

For $\gamma \in [0.5, 1)$, the spending function is defined as

$$
\alpha_\gamma(t) = 2 - 2\times \Phi\left(\frac{z_{\alpha/2} - z_\gamma\sqrt{1-t}}{\sqrt t} \right).
$$

Recalling the range $\gamma \in [0.5, 1)$, we plot this spending function for
$\gamma = 0.5, 0.6, 0.75, 0.9$.

```{r}
pts <- seq(0, 1.2, 0.01)
pal <- palette()
```

```{r}
plot(
  pts,
  sfXG1(0.025, pts, 0.5)$spend,
  type = "l", col = pal[1],
  xlab = "t", ylab = "Spending", main = "Xi-Gallo, Method 1"
)
lines(pts, sfXG1(0.025, pts, 0.6)$spend, col = pal[2])
lines(pts, sfXG1(0.025, pts, 0.75)$spend, col = pal[3])
lines(pts, sfXG1(0.025, pts, 0.9)$spend, col = pal[4])
legend(
  "topleft",
  legend = c("gamma=0.5", "gamma=0.6", "gamma=0.75", "gamma=0.9"),
  col = pal[1:4],
  lty = 1
)
```

### Method 2

For $\gamma \in [1 - \Phi(z_{\alpha/2}/2), 1)$, the spending function for
Method 2 is defined as

$$
\alpha_\gamma(t)= 2 - 2\times \Phi\left(\frac{z_{\alpha/2} - z_\gamma(1-t)}{\sqrt t} \right)
$$

For $\alpha=0.025$, we restrict $\gamma$ to
[`r round(1 - pnorm((qnorm(1 - 0.025/2)/2)), 3)`, 1) and plot the
spending function for $\gamma = 0.14, 0.25, 0.5, 0.75, 0.9$.

```{r}
plot(
  pts,
  sfXG2(0.025, pts, 0.14)$spend,
  type = "l", col = pal[1],
  xlab = "t", ylab = "Spending", main = "Xi-Gallo, Method 2"
)
lines(pts, sfXG2(0.025, pts, 0.25)$spend, col = pal[2])
lines(pts, sfXG2(0.025, pts, 0.5)$spend, col = pal[3])
lines(pts, sfXG2(0.025, pts, 0.75)$spend, col = pal[4])
lines(pts, sfXG2(0.025, pts, 0.9)$spend, col = pal[5])
legend(
  "topleft",
  legend = c("gamma=0.14", "gamma=0.25", "gamma=0.5", "gamma=0.75", "gamma=0.9"),
  col = pal[1:5],
  lty = 1
)
```

### Method 3

For $\gamma \in (\alpha/2, 1)$

$$
\alpha_\gamma(t)= 2 - 2\times \Phi\left(\frac{z_{\alpha/2} - z_\gamma(1-\sqrt t)}{\sqrt t} \right).
$$

For $\alpha=0.025$, we restrict $\gamma$ to $(0.0125, 1)$ and plot the
spending function for $\gamma = 0.013, 0.02, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9$.

```{r}
plot(
  pts,
  sfXG3(0.025, pts, 0.013)$spend,
  type = "l", col = pal[1],
  xlab = "t", ylab = "Spending", main = "Xi-Gallo, Method 3"
)
lines(pts, sfXG3(0.025, pts, 0.02)$spend, col = pal[2])
lines(pts, sfXG3(0.025, pts, 0.05)$spend, col = pal[3])
lines(pts, sfXG3(0.025, pts, 0.1)$spend, col = pal[4])
lines(pts, sfXG3(0.025, pts, 0.25)$spend, col = pal[5])
lines(pts, sfXG3(0.025, pts, 0.5)$spend, col = pal[6])
lines(pts, sfXG3(0.025, pts, 0.75)$spend, col = pal[7])
lines(pts, sfXG3(0.025, pts, 0.9)$spend, col = pal[8])
legend(
  "bottomright",
  legend = c(
    "gamma=0.013", "gamma=0.02", "gamma=0.05", "gamma=0.1",
    "gamma=0.25", "gamma=0.5", "gamma=0.75", "gamma=0.9"
  ),
  col = pal[1:8],
  lty = 1
)
```

## Replicating published examples

We replicate spending function bounds of @xi2019additive along with
corresponding conditional error computations.
We have two utility functions. Transposing a tibble and a custom function
to compute conditional error.

```{r}
# Custom function to transpose while preserving names
# From https://stackoverflow.com/questions/42790219/how-do-i-transpose-a-tibble-in-r
transpose_df <- function(df) {
  t_df <- data.table::transpose(df)
  colnames(t_df) <- rownames(df)
  rownames(t_df) <- colnames(df)
  t_df <- t_df %>%
    tibble::rownames_to_column(.data = .) %>%
    tibble::as_tibble(.)
  return(t_df)
}
```

```{r}
ce <- function(x) {
  k <- x$k
  ce <- c(gsCPz(z = x$upper$bound[1:(k - 1)], i = 1:(k - 1), x = x, theta = 0), NA)
  t <- x$timing
  ce_simple <- c(pnorm((last(x$upper$bound) - x$upper$bound[1:(k - 1)] * sqrt(t[1:(k - 1)])) / sqrt(1 - t[1:(k - 1)]),
    lower.tail = FALSE
  ), NA)
  Analysis <- 1:k
  y <- tibble(
    # Analysis = Analysis,
    Z = x$upper$bound,
    "CE simple" = ce_simple,
    CE = ce
  )
  return(y)
}
```

### Method 1

The conditional error spending functions of @xi2019additive for Method 1 and
Method 2 are designed to derive interim efficacy bounds with conditional error
approximately equal to the spending function parameter $\gamma$.
The conditional probability of crossing the final bound $u_K$ given an
interim result $Z_k=u_k$ at analysis $k<K$ under the assumption of
no treatment effect is

$$
p_0(Z_K\ge u_K|Z_k=u_k) = 1 - \Phi\left(\frac{u_K - u_k\sqrt t}{\sqrt{1-t}}\right).
$$

Conditional rejection probabilities accounting for all future analyses
as well as under any assumed treatment effect are explained further in the
[gsDesign technical manual](https://keaven.github.io/gsd-tech-manual/cpb.html#conditional-power-and-conditional-error).
We will see that where there are future interim analyses below, the
conditional error for crossing at least one future efficacy bound is
substantially greater than the simple conditional error ignoring future interims.

We will compare the Method 1 conditional error spending functions of
@xi2019additive with O'Brien-Fleming bounds (`sfu = "OF"`),
exponential spending (`sfu = sfExponential`), and the Lan-DeMets
spending function to approximate O'Brien-Fleming bounds (`sfu = sfLDOF`).
The O'Brien-Fleming bounds are specifically known to the 0.5 (simple)
conditional error as seen in the table below.
The exponential spending function provides the closest approximation of
O'Brien-Fleming bounds with a parameter of 0.76; this was suggested
previously by @anderson2010group.
The other Method 1 spending functions generally have higher than the
targeted simple conditional error; thus, if you wish to use a particular
conditional error at bounds, it may be better to see if a smaller $\gamma$
than the targeted conditional error provides a better match.

```{r}
xOF <- gsDesign(k = 4, test.type = 1, sfu = "OF")
xLDOF <- gsDesign(k = 4, test.type = 1, sfu = sfLDOF)
xExp <- gsDesign(k = 4, test.type = 1, sfu = sfExponential, sfupar = 0.76)
x1.8 <- gsDesign(k = 4, test.type = 1, sfu = sfXG1, sfupar = 0.8)
x1.7 <- gsDesign(k = 4, test.type = 1, sfu = sfXG1, sfupar = 0.7)
x1.6 <- gsDesign(k = 4, test.type = 1, sfu = sfXG1, sfupar = 0.6)
x1.5 <- gsDesign(k = 4, test.type = 1, sfu = sfXG1, sfupar = 0.5)
xx <- rbind(
  transpose_df(ce(xOF)) %>% mutate(gamma = "O'Brien-Fleming"),
  transpose_df(ce(xExp)) %>% mutate(gamma = "Exponential, nu=0.76 to Approximate O'Brien-Fleming"),
  transpose_df(ce(xLDOF)) %>% mutate(gamma = "Lan-DeMets to Approximate O'Brien-Fleming"),
  transpose_df(ce(x1.5)) %>% mutate(gamma = "gamma = 0.5"),
  transpose_df(ce(x1.6)) %>% mutate(gamma = "gamma = 0.6"),
  transpose_df(ce(x1.7)) %>% mutate(gamma = "gamma = 0.7"),
  transpose_df(ce(x1.8)) %>% mutate(gamma = "gamma = 0.8")
)
xx %>%
  gt(groupname_col = "gamma") %>%
  tab_spanner(label = "Analysis", columns = 2:5) %>%
  fmt_number(columns = 2:5, decimals = 3) %>%
  tab_options(data_row.padding = px(1)) %>%
  tab_header(
    title = "Xi-Gallo, Method 1 Spending Function",
    subtitle = "Conditional Error Spending Functions"
  ) %>%
  tab_footnote(
    footnote = "Conditional Error not accounting for future interim bounds.",
    locations = cells_stub(rows = seq(2, 20, 3))
  ) %>%
  tab_footnote(
    footnote = "CE = Conditional Error accounting for all analyses.",
    locations = cells_stub(rows = seq(3, 21, 3))
  )
```

### Method 2

Method 2 provides a wider range of $\gamma$ values targeting conditional error at bounds.
Again, choice of $\gamma$ to get the targeted conditional error may be worth some evaluation.

```{r}
x1.8 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.8)
x1.7 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.7)
x1.6 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.6)
x1.5 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.5)
x1.4 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.4)
x1.3 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.3)
x1.2 <- gsDesign(k = 4, test.type = 1, sfu = sfXG2, sfupar = 0.2)
xx <- rbind(
  transpose_df(ce(x1.2)) %>% mutate(gamma = "gamma = 0.2"),
  transpose_df(ce(x1.3)) %>% mutate(gamma = "gamma = 0.3"),
  transpose_df(ce(x1.4)) %>% mutate(gamma = "gamma = 0.4"),
  transpose_df(ce(x1.5)) %>% mutate(gamma = "gamma = 0.5"),
  transpose_df(ce(x1.6)) %>% mutate(gamma = "gamma = 0.6"),
  transpose_df(ce(x1.7)) %>% mutate(gamma = "gamma = 0.7"),
  transpose_df(ce(x1.8)) %>% mutate(gamma = "gamma = 0.8")
)
xx %>%
  gt(groupname_col = "gamma") %>%
  tab_spanner(label = "Analysis", columns = 2:5) %>%
  fmt_number(columns = 2:5, decimals = 3) %>%
  tab_options(data_row.padding = px(1)) %>%
  tab_footnote(
    footnote = "Conditional Error not accounting for future interim bounds.",
    locations = cells_stub(rows = seq(2, 20, 3))
  ) %>%
  tab_footnote(
    footnote = "CE = Conditional Error accounting for all analyses.",
    locations = cells_stub(rows = seq(3, 21, 3))
  ) %>%
  tab_header(
    title = "Xi-Gallo, Method 2 Spending Function",
    subtitle = "Conditional Error Spending Functions"
  )
```

### Method 3

Method 3 spending functions are designed to approximate Pocock bounds with
equal bounds on the Z-scale.
Two common approximations used for this is the @HwangShihDeCani spending function
with $\gamma = 1$

$$
\alpha_{HSD}(t, \gamma) = \alpha\frac{1-e^{-\gamma t}}{1 - e^{-\gamma}}.
$$

and the @LanDeMets spending function to approximate Pocock bounds

$$
\alpha_{LDP}(t) = \alpha \log(1 +(e -1)t).
$$

We compare these methods in the following table.
While the $\gamma = 0.025$ conditional error spending bounds are close to the
targeted Pocock bounds, the $\gamma = 0.05$ conditional error spending bound is
substantially higher than the targeted value at the first interim.
The traditional Lan-DeMets and Hwang-Shih-DeCani approximations are quite good
approximations of the Pocock bounds.
The one number not reproduced from @xi2019additive is the conditional error
at the first analysis for $\gamma = 0.05$; while here we have computed 0.132,
in the paper this value was 0.133.
There are differences in the computation algorithms that may account for
this difference.
The method used in gsDesign is from Chapter 19 of @JTBook, specifically
designed for numerical integration for group sequential trials.
The method used in @xi2019additive is a more general method approximating
multivariate normal probabilities.

```{r}
xPocock <- gsDesign(k = 4, test.type = 1, sfu = "Pocock")
xLDPocock <- gsDesign(k = 4, test.type = 1, sfu = sfLDPocock)
xHSD1 <- gsDesign(k = 4, test.type = 1, sfu = sfHSD, sfupar = 1)
x3.025 <- gsDesign(k = 4, test.type = 1, sfu = sfXG3, sfupar = 0.025)
x3.05 <- gsDesign(k = 4, test.type = 1, sfu = sfXG3, sfupar = 0.05)
xx <- rbind(
  transpose_df(ce(xPocock)) %>% mutate(gamma = "Pocock"),
  transpose_df(ce(xLDPocock)) %>% mutate(gamma = "Lan-DeMets to Approximate Pocock"),
  transpose_df(ce(xHSD1)) %>% mutate(gamma = "Hwang-Shih-DeCani, gamma = 1"),
  transpose_df(ce(x3.025)) %>% mutate(gamma = "gamma = 0.025"),
  transpose_df(ce(x3.05)) %>% mutate(gamma = "gamma = 0.05 ")
)
xx %>%
  gt(groupname_col = "gamma") %>%
  tab_spanner(label = "Analysis", columns = 2:5) %>%
  fmt_number(columns = 2:5, decimals = 3) %>%
  tab_options(data_row.padding = px(1)) %>%
  tab_footnote(
    footnote = "Conditional Error not accounting for future interim bounds.",
    locations = cells_stub(rows = seq(2, 11, 3))
  ) %>%
  tab_footnote(
    footnote = "CE = Conditional Error accounting for all analyses.",
    locations = cells_stub(rows = seq(3, 12, 3))
  ) %>%
  tab_header(
    title = "Xi-Gallo, Method 3 Spending Function",
    subtitle = "Conditional Error Spending Functions"
  )
```

## Summary

@xi2019additive proposed conditional error spending functions for
group sequential designs are implemented in the gsDesign package.
When there is a single interim analysis, conditional error from Method 1
is almost exactly the same as the parameter $\gamma$ but it allows a narrower
range of $\gamma$. Method 2 provides less accurate approximation but allows
a wider range of $\gamma$.
Using $\gamma = 0.5$ replicates the Lan-DeMets spending function to approximate
O'Brien-Fleming bounds.
An exponential spending function provides a possibly better approximation of
O'Brien-Fleming bounds.
Simply selecting a smaller $\gamma$ than the targeted conditional error may
provide a better match for the targeted bounds.
While Method 3 provides a reasonable approximation of a Pocock bound with
equal bounds on the Z-scale, its stated objective, traditional approximations
of Pocock bounds with the Lan-DeMets and Hwang-Shih-DeCani spending functions
may be slightly better.

Results duplicated findings from the original paper.

## References
