---
title: "Common multiple comparison procedures illustrated using graphicalMCP"
output: rmarkdown::html_vignette
bibliography: "`r system.file('references.bib', package='graphicalMCP')`"
vignette: >
  %\VignetteIndexEntry{Common multiple comparison procedures illustrated using graphicalMCP}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  fig.align = "center",
  collapse = TRUE,
  comment = "#>",
  fig.dim = c(3, 3),
  eval = requireNamespace("igraph", quietly = TRUE)
)
```

```{r setup, warning=FALSE, message=FALSE}
library(graphicalMCP)
```

# Introduction

In confirmatory clinical trials, regulatory guidelines mandate the strong control of the family-wise error rate at a prespecified level $\alpha$. Many multiple comparison procedures (MCPs) have been proposed for this purpose. The graphical approaches are a general framework that include many common MCPs as special cases. In this vignette, we illustrate how to use graphicalMCP to perform some common MCPs.

To test $m$ hypotheses using a graphical MCP, each hypothesis $H_i$ receives a weight $0\leq w_i\leq 1$ (called hypothesis weight), where $\sum_{i=1}^{m}w_i\leq 1$. From $H_i$ to $H_j$, there could be a directed and weighted edge $0\leq g_{ij}\leq 1$, which means that when $H_i$ is rejected, its hypothesis weight will be propagated (or transferred) to $H_j$ and $g_{ij}$ determines how much of the propagation. We also require $\sum_{j=1}^{m}g_{ij}\leq 1$ and $g_{ii}=0$.

# Bonferroni-based procedures

## Bonferroni test

A Bonferroni test splits $\alpha$ equally among hypotheses by testing every hypothesis at a significance level of $\alpha$ divided by the number of hypotheses. Thus it rejects a hypothesis if its p-value is less than or equal to its significance level. There is no propagation between any pair of hypothesis.

```{r bonferroni}
set.seed(1234)
alpha <- 0.025
m <- 3
bonferroni_graph <- bonferroni(m)
# transitions <- matrix(0, m, m)
# bonferroni_graph <- graph_create(rep(1 / m, m), transitions)

plot(
  bonferroni_graph,
  layout = igraph::layout_in_circle(
    as_igraph(bonferroni_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    bonferroni_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

## Weighted Bonferroni test

A weighted Bonferroni test splits $\alpha$ among hypotheses by testing every hypothesis at a significance level of $w_i\alpha$. Thus it rejects a hypothesis if its p-value is less than or equal to its significance level. When $w_i=w_j$ for all $i,j$, this means an equal split and the test is the Bonferroni test. There is no propagation between any pair of hypothesis.

```{r weighted-bonferroni}
set.seed(1234)
alpha <- 0.025
hypotheses <- c(0.5, 0.3, 0.2)
weighted_bonferroni_graph <- bonferroni_weighted(hypotheses)
# m <- length(hypotheses)
# transitions <- matrix(0, m, m)
# weighted_bonferroni_graph <- graph_create(hypotheses, transitions)

plot(
  weighted_bonferroni_graph,
  layout = igraph::layout_in_circle(
    as_igraph(bonferroni_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    weighted_bonferroni_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

## Holm Procedure

Holm (or Bonferroni-Holm) procedures improve over Bonferroni tests by allowing propagation [@holm-1979-simple]. In other words, transition weights between hypotheses may not be zero. So it is uniformly more powerful than Bonferroni tests.

```{r bonferroni-holm}
set.seed(1234)
alpha <- 0.025
m <- 3
holm_graph <- bonferroni_holm(m)
# transitions <- matrix(1 / (m - 1), m, m)
# diag(transitions) <- 0
# holm_graph <- graph_create(rep(1 / m, m), transitions)

plot(holm_graph,
  layout = igraph::layout_in_circle(
    as_igraph(holm_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <- graph_test_shortcut(holm_graph, p = p_values, alpha = alpha)

test_results$outputs$rejected
```

## Weighted Holm Procedure

Weighted Holm (or weighted Bonferroni-Holm) procedures improve over weighted Bonferroni tests by allowing propagation [@holm-1979-simple]. In other words, transition weights between hypotheses may not be zero. So it is uniformly more powerful than weighted Bonferroni tests.

```{r weighted-bonferroni-holm}
set.seed(1234)
alpha <- 0.025
hypotheses <- c(0.5, 0.3, 0.2)
weighted_holm_graph <- bonferroni_holm_weighted(hypotheses)
# m <- length(hypotheses)
# transitions <- matrix(1 / (m - 1), m, m)
# diag(transitions) <- 0
# weighted_holm_graph <- graph_create(hypotheses, transitions)

plot(weighted_holm_graph,
  layout = igraph::layout_in_circle(
    as_igraph(holm_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <- graph_test_shortcut(weighted_holm_graph, p = p_values, alpha = alpha)

test_results$outputs$rejected
```

## Fixed sequence procedure

Fixed sequence (or hierarchical) procedures pre-specify an order of testing [@maurer-1995-multiple,@westfall-2001-optimally]. For example, the procedure will test $H_1$ first. If it is rejected, it will test $H_2$; otherwise the testing stops. If $H_2$ is rejected, it will test $H_3$; otherwise the testing stops. For each hypothesis, it will be tested at the full $\alpha$ level, when it can be tested.

```{r fixed-sequence}
set.seed(1234)
alpha <- 0.025
m <- 3
fixed_sequence_graph <- fixed_sequence(m)
# transitions <- rbind(
#   c(0, 1, 0),
#   c(0, 0, 1),
#   c(0, 0, 0)
# )
# fixed_sequence_graph <- graph_create(c(1, 0, 0), transitions)

plot(fixed_sequence_graph, nrow = 1, asp = 0.5, vertex.size = 50)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    fixed_sequence_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

## Fallback procedure

Fallback procedures have one-way propagation (like fixed sequence procedures) but allow hypotheses to be tested at different significance levels [@wiens-2003-fixed].

```{r fallback}
set.seed(1234)
alpha <- 0.025
hypotheses <- c(0.5, 0.3, 0.2)
m <- length(hypotheses)
fallback_graph <- fallback(hypotheses)
# transitions <- rbind(
#   c(0, 1, 0),
#   c(0, 0, 1),
#   c(0, 0, 0)
# )
# fallback_graph <- graph_create(hypotheses, transitions)

plot(fallback_graph, nrow = 1, asp = 0.5, vertex.size = 50)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    fallback_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

Further they can be improved to allow propagation from later hypotheses to earlier hypotheses, because it is possible that a later hypothesis is rejected before an earlier hypothesis can be rejected. There are two versions of improvement: `fallback_improved_1` due to @wiens-2005-fallback and `fallback_improved_2` due to @bretz-2009-graphical respectively.

```{r fallback-improved}
set.seed(1234)
alpha <- 0.025
hypotheses <- c(0.5, 0.3, 0.2)
m <- length(hypotheses)
fallback_improved_1_graph <- fallback_improved_1(hypotheses)
# transitions <- rbind(
#   c(0, 1, 0),
#   c(0, 0, 1),
#   c(hypotheses[seq_len(m - 1)] / sum(hypotheses[seq_len(m - 1)]), 0)
# )
# fallback_improved_1_graph <- graph_create(hypotheses, transitions)
plot_layout <- rbind(
  c(0, 0.8),
  c(0.8, 0.8),
  c(0.4, 0)
)

plot(
  fallback_improved_1_graph,
  layout = plot_layout,
  asp = 0.5,
  edge_curves = c(pairs = 0.5),
  vertex.size = 70
)

epsilon <- 0.0001
fallback_improved_2_graph <- fallback_improved_2(hypotheses, epsilon)
# transitions <- rbind(
#   c(0, 1, 0),
#   c(1 - epsilon, 0, epsilon),
#   c(1, 0, 0)
# )
# fallback_improved_2_graph <- graph_create(hypotheses, transitions)
plot_layout <- rbind(
  c(0, 0.8),
  c(0.8, 0.8),
  c(0.4, 0)
)

plot(
  fallback_improved_2_graph,
  layout = plot_layout,
  eps = 0.0001,
  asp = 0.5,
  edge_curves = c(pairs = 0.5),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    fallback_improved_2_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

## Serial gatekeeping procedure

Serial gatekeeping procedures involve ordered multiple families of hypotheses, where all hypotheses of a family of hypotheses must be rejected before proceeding in the test sequence. The example below considers a primary family consisting of two hypotheses $H_1$ and $H_2$ and a secondary family consisting of a single hypothesis $H_3$. In the primary family, the Holm procedure is applied. If both $H_1$ and $H_2$ are rejected, $H_3$ can be tested at level $\alpha$; otherwise $H_3$ cannot be rejected. To allow the conditional propagation to $H_3$, an $\varepsilon$ edge is used from $H_2$ to $H_3$. It has a very small transition weight so that $H_2$ propagates most of its hypothesis weight to $H_1$ (if not already rejected) and retains a small (non-zero) weight for $H_3$ so that if $H_1$ has been rejected, all hypothesis weight of $H_2$ will be propagated to $H_3$. Here $\varepsilon$ is assigned to be 0.0001 and in practice, the value could be adjusted but it should be much smaller than the smallest p-value observed.

```{r serial-gatekeeping}
set.seed(1234)
alpha <- 0.025
m <- 3
epsilon <- 0.0001

transitions <- rbind(
  c(0, 1, 0),
  c(1 - epsilon, 0, epsilon),
  c(0, 0, 0)
)

serial_gatekeeping_graph <- graph_create(c(0.5, 0.5, 0), transitions)

plot_layout <- rbind(
  c(0, 0.8),
  c(0.8, 0.8),
  c(0.4, 0)
)

plot(
  serial_gatekeeping_graph,
  layout = plot_layout,
  eps = 0.0001,
  asp = 0.5,
  edge_curves = c(pairs = 0.5),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    serial_gatekeeping_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

## Parallel gatekeeping procedure

Parallel gatekeeping procedures also involve multiple ordered families of hypotheses, where any null hypotheses of a family of hypotheses must be rejected before proceeding in the test sequence [@dmitrienko-2003-gatekeeping]. The example below considers a primary family consisting of two hypotheses $H_1$ and $H_2$ and a secondary family consisting of two hypotheses $H_3$ and $H_4$. In the primary family, the Bonferroni test is applied. If any of $H_1$ and $H_2$ is rejected, $H_3$ and $H_4$ can be tested at level $\alpha/2$ using the Holm procedure; if both $H_1$ and $H_2$ are rejected, $H_3$ and $H_4$ can be tested at level $\alpha$ using the Holm procedure; otherwise $H_3$ and $H_4$ cannot be 
rejected.

```{r parallel-gatekeeping}
set.seed(1234)
alpha <- 0.025
m <- 4

transitions <- rbind(
  c(0, 0, 0.5, 0.5),
  c(0, 0, 0.5, 0.5),
  c(0, 0, 0, 1),
  c(0, 0, 1, 0)
)

parallel_gatekeeping_graph <- graph_create(c(0.5, 0.5, 0, 0), transitions)

plot(parallel_gatekeeping_graph, vertex.size = 70)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    parallel_gatekeeping_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

The above parallel gatekeeping procedure can be improved by adding $\varepsilon$ edges from secondary hypotheses to primary hypotheses, because it is possible that both secondary hypotheses are rejected but there is still a remaining primary hypothesis 
not rejected [@bretz-2009-graphical].

```{r parallel-gatekeeping-improved}
set.seed(1234)
alpha <- 0.025
m <- 4
epsilon <- 0.0001

transitions <- rbind(
  c(0, 0, 0.5, 0.5),
  c(0, 0, 0.5, 0.5),
  c(epsilon, 0, 0, 1 - epsilon),
  c(0, epsilon, 1 - epsilon, 0)
)

parallel_gatekeeping_improved_graph <-
  graph_create(c(0.5, 0.5, 0, 0), transitions)

plot(parallel_gatekeeping_improved_graph, eps = 0.0001, vertex.size = 70)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    parallel_gatekeeping_improved_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

## Successive procedure

Successive procedures incorporate successive relationships between hypotheses. For example, the secondary hypothesis is not tested until the primary hypothesis has been rejected. This is similar to using the fixed sequence procedure as a component of a graph. The example below considers two primary hypotheses $H_1$ and $H_2$ and two secondary hypotheses $H_3$ and $H_4$. Primary hypotheses $H_1$ and $H_2$ receive the equal hypothesis weight of 0.5; secondary hypotheses $H_3$ and $H_4$ receive the hypothesis weight of 0. A secondary hypothesis $H_3 (H_4)$ can be tested only if the corresponding primary hypothesis $H_1 (H_2)$ has been rejected. This represents the successive relationships between $H_1$ and $H_3$, and $H_2$ and $H_4$, respectively [@maurer-2011-multiple]. If both $H_1$ and $H_3$ are rejected, their hypothesis weights are propagated to $H_2$ and $H_4$, and vice versa.

```{r simple-successive-1}
set.seed(1234)
alpha <- 0.025
m <- 4
simple_successive_graph <- simple_successive_1()
# transitions <- rbind(
#   c(0, 0, 1, 0),
#   c(0, 0, 0, 1),
#   c(0, 1, 0, 0),
#   c(1, 0, 0, 0)
# )
# simple_successive_graph <- graph_create(c(0.5, 0.5, 0, 0), transitions)

plot(simple_successive_graph, layout = "grid", nrow = 2, vertex.size = 70)

p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    simple_successive_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

The above graph could be generalized to allow propagation between primary hypotheses [@maurer-2011-multiple]. A general successive graph is illustrate below with a variable to determine the propagation between $H_1$ and $H_2$.

```{r simple-successive-var}
set.seed(1234)
alpha <- 0.025
m <- 4

successive_var <- simple_successive_var <- function(gamma) {
  graph_create(
    c(0.5, 0.5, 0, 0),
    rbind(
      c(0, gamma, 1 - gamma, 0),
      c(gamma, 0, 0, 1 - gamma),
      c(0, 1, 0, 0),
      c(1, 0, 0, 0)
    )
  )
}

successive_var_graph <- successive_var(0.5)
plot(successive_var_graph, layout = "grid", nrow = 2, vertex.size = 70)
p_values <- runif(m, 0, alpha)

test_results <-
  graph_test_shortcut(
    successive_var_graph,
    p = p_values,
    alpha = alpha
  )

test_results$outputs$rejected
```

# Hochberg-based procedures

## Hochberg procedure

Hochberg procedure [@hochberg-1988-sharper] is a closed test procedure which uses Hochberg tests for every intersection hypothesis. According to @xi-2019-symmetric, the graph for Hochberg procedures is the same as the graph for Holm procedures. Thus to perform Hochberg procedure, we just need to specify `test_type` to be `hochberg`.

```{r hochberg}
set.seed(1234)
alpha <- 0.025
m <- 3
hochberg_graph <- hochberg(m)

plot(
  hochberg_graph,
  layout = igraph::layout_in_circle(
    as_igraph(hochberg_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <- graph_test_closure(
  hochberg_graph,
  p = p_values,
  alpha = alpha,
  test_types = "hochberg"
)

test_results$outputs$rejected
```

# Simes-based procedures

## Hommel procedure

Hommel procedure [@hommel-1988-stagewise] is a closed test procedure which uses Simes tests for every intersection hypothesis. According to @xi-2019-symmetric, the graph for Hommel procedures is the same as the graph for Holm procedures. Thus to perform Hommel procedure, we just need to specify `test_type` to be `simes`.

```{r hommel}
set.seed(1234)
alpha <- 0.025
m <- 3
hommel_graph <- hommel(m)

plot(
  hommel_graph,
  layout = igraph::layout_in_circle(
    as_igraph(hommel_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)

test_results <- graph_test_closure(
  hommel_graph,
  p = p_values,
  alpha = alpha,
  test_types = "simes"
)

test_results$outputs$rejected
```

# Parametric procedures

## Šidák test
The Šidák test is similar to the equally weighted Bonferroni test but it assumes test statistics are independent of each other [@vsidak-1967-rectangular]. Thus it can be performed as a parametric procedure with the identity correlation matrix. Its graph is the same as the graph for the equally weighted Bonferroni test.
```{r sidak}
set.seed(1234)
alpha <- 0.025
m <- 3
sidak_graph <- sidak(m)

plot(
  sidak_graph,
  layout = igraph::layout_in_circle(
    as_igraph(bonferroni_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)
corr <- diag(m)

test_results <- graph_test_closure(
  sidak_graph,
  p = p_values, alpha = alpha,
  test_types = "parametric",
  test_corr = list(corr)
)

test_results$outputs$rejected
```

## Dunnett test

Single-step Dunnett tests are an improvement from Bonferroni tests by incorporating the correlation structure between test statistics [@dunnett-1955-multiple]. Thus their graphs are the same as Bonferroni tests. Assume an equi-correlated case, where the correlation between any pair of test statistics is the same, e.g., 0.5. Then we can perform the single-step Dunnett test by specifying `test_type` to be `parametric` and providing the correlation matrix.

```{r dunnett-test}
set.seed(1234)
alpha <- 0.025
m <- 3
dunnett_graph <- dunnett_single_step(m)

plot(
  dunnett_graph,
  layout = igraph::layout_in_circle(
    as_igraph(dunnett_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)
corr <- matrix(0.5, m, m)
diag(corr) <- 1

test_results <- graph_test_closure(
  dunnett_graph,
  p = p_values, alpha = alpha,
  test_types = "parametric",
  test_corr = list(corr)
)

test_results$outputs$rejected
```

## Weighted Dunnett test

Weighted single-step Dunnett tests are an improvement from weighted Bonferroni tests by incorporating the correlation structure between test statistics [@xi-2017-unified]. Thus their graphs are the same as weighted Bonferroni tests. Assume an equi-correlated case, where the correlation between any pair of test statistics is the same, e.g., 0.5. Then we can perform the weighted single-step Dunnett test by specifying `test_type` to be `parametric` and providing the correlation matrix.

```{r weighted-dunnett-test}
set.seed(1234)
alpha <- 0.025
hypotheses <- c(0.5, 0.3, 0.2)
dunnett_graph <- dunnett_single_step_weighted(hypotheses)

plot(
  dunnett_graph,
  layout = igraph::layout_in_circle(
    as_igraph(dunnett_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)
corr <- matrix(0.5, m, m)
diag(corr) <- 1

test_results <- graph_test_closure(
  dunnett_graph,
  p = p_values, alpha = alpha,
  test_types = "parametric",
  test_corr = list(corr)
)

test_results$outputs$rejected
```

## Dunnett procedure

Dunnett procedures are a closed test procedure and an improvement from Holm procedures by incorporating the correlation structure between test statistics [@xi-2017-unified]. Thus their graphs are the same as Holm procedures. Assume an equi-correlated case, where the correlation between any pair of test statistics is the same, e.g., 0.5. Then we can perform the step-down Dunnett procedure by specifying `test_type` to be `parametric` and providing the correlation matrix.

```{r dunnett-procedure}
set.seed(1234)
alpha <- 0.025
hypotheses <- c(0.5, 0.3, 0.2)
dunnett_graph <- dunnett_closure_weighted(hypotheses)

plot(
  dunnett_graph,
  layout = igraph::layout_in_circle(
    as_igraph(dunnett_graph),
    order = c(2, 1, 3)
  ),
  vertex.size = 70
)

p_values <- runif(m, 0, alpha)
corr <- matrix(0.5, m, m)
diag(corr) <- 1

test_results <- graph_test_closure(
  dunnett_graph,
  p = p_values, alpha = alpha,
  test_types = "parametric",
  test_corr = list(corr)
)

test_results$outputs$rejected
```

# Reference
