## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

# Check if suggested packages are available
has_matchit <- requireNamespace("MatchIt", quietly = TRUE)
has_weightit <- requireNamespace("WeightIt", quietly = TRUE)
has_survival <- requireNamespace("survival", quietly = TRUE)
has_ggplot2 <- requireNamespace("ggplot2", quietly = TRUE)

# Set evaluation based on package availability
eval_matching <- has_matchit && has_survival
eval_weighting <- has_weightit && has_survival
eval_plots <- has_ggplot2

## ----setup, message = FALSE, warning = FALSE----------------------------------
library(mccount)
library(dplyr)
library(WeightIt)
library(MatchIt)
library(patchwork)

# Create example data with binary treatment
bladder_nested <- survival::bladder1 |>
  mutate(status = if_else(status > 2, 2, status)) |> 
  filter(treatment %in% c("placebo", "thiotepa")) |>
  tidyr::nest(.by = c(id, treatment, number, size)) |> 
  mutate(treatment_binary = if_else(treatment == "thiotepa", 1, 0))

## ----iptw---------------------------------------------------------------------
# Estimate propensity scores and create IPTW weights using WeightIt
weight_obj <- weightit(
  treatment_binary ~ number + size, 
  data = bladder_nested,
)

# Extract weights
bladder_nested$iptw_weights <- weight_obj$weights

bladder_example <- bladder_nested |> 
  tidyr::unnest(data)

## ----weighted_mcc-------------------------------------------------------------
# Estimate MCC with IPTW weights
mcc_weighted <- mcc(
  data = bladder_example,
  id_var = "id",
  time_var = "stop",
  cause_var = "status",
  by = "treatment",
  weights = "iptw_weights",
  method = "equation"
)

# Display results
summary(mcc_weighted)

## ----comparison---------------------------------------------------------------
# Estimate unweighted MCC for comparison
mcc_unweighted <- mcc(
  data = bladder_example,
  id_var = "id",
  time_var = "stop",
  cause_var = "status",
  by = "treatment",
  method = "equation"
)

# Extract final MCC values for comparison
weighted_final <- mcc_final_values(mcc_weighted)
unweighted_final <- mcc_final_values(mcc_unweighted)

# Create comparison table
comparison_table <- data.frame(
  Method = c("Unweighted", "IPTW Weighted"),
  Control_MCC = c(
    cards::round5(unweighted_final[1], digits = 2),
    cards::round5(weighted_final[1], digits = 2)
  ),
  Treated_MCC = c(
    cards::round5(unweighted_final[2], digits = 2),
    cards::round5(weighted_final[2], digits = 2)
  )
)

knitr::kable(comparison_table)

## ----nn_matching--------------------------------------------------------------
# Perform 1:1 nearest neighbor matching
match_nn <- matchit(
  treatment_binary ~ size + number,
  data = bladder_nested
)

## ----nn_matching cont---------------------------------------------------------
# Extract matched data (no additional weights needed)
matched_nn_data <- match.data(match_nn) |> 
  tidyr::unnest(data)

# Estimate MCC on matched data without additional weights
mcc_nn_matched <- mcc(
  data = matched_nn_data,
  id_var = "id",
  time_var = "stop", 
  cause_var = "status",
  by = "treatment_binary",
  method = "equation"
  # No weights argument needed for simple 1:1 matching (all weights are 1)
)

summary(mcc_nn_matched)

## ----full_matching------------------------------------------------------------
# Perform full matching (creates matching weights)
match_obj <- matchit(
  treatment_binary ~ size + number,
  data = bladder_nested,
  method = "full",      # Full matching creates weights
  estimand = "ATE"
)

# Check matching balance
summary(match_obj)

# Extract matched data with weights
matched_data <- match_data(match_obj) |> 
  tidyr::unnest(data)

# The 'weights' column contains the matching weights
head(matched_data[c("id", "treatment", "weights")])

## ----matched_mcc--------------------------------------------------------------
# Estimate MCC using matching weights
mcc_matched <- mcc(
  data = matched_data,
  id_var = "id",
  time_var = "stop",
  cause_var = "status",
  by = "treatment",
  weights = "weights",  # Use matching weights from MatchIt
  method = "equation"
)

summary(mcc_matched)

## ----plotting_comparison------------------------------------------------------
p_unwt <- plot(mcc_unweighted) +
  geom_line_mcc(mcc_unweighted) +
  labs(subtitle = element_blank(), color = "Treatment") +
  scale_y_continuous(limits = c(0, 2.75)) +
  ggtitle("Unweighted")

p_wt <- plot(mcc_weighted) +
  geom_line_mcc(mcc_weighted) +
  ggtitle("IPTW") +
  labs(subtitle = "Estimand: ATE", color = "Treatment") +
  scale_y_continuous(limits = c(0, 2.75)) +
  theme(axis.title.y = element_blank())

p_mwt <- plot(mcc_matched) +
  geom_line_mcc(mcc_matched) +
  ggtitle("Full Matching") +
  labs(subtitle = "Estimand: ATE", color = "Treatment") +
  scale_y_continuous(limits = c(0, 2.75)) +
  theme(axis.title.y = element_blank())

combined <- p_unwt | p_wt | p_mwt

combined + 
  plot_layout(guides = "collect") &
  theme(legend.position = "bottom")