chapter_06.qmd

---
title: "Confounding adjustment using propensity score methods"
authors:   
  - name: Tammy Jiang
    affiliations:
      - ref: biogen
  - name: Thomas Debray
    orcid: 0000-0002-1790-2719
    affiliations:
      - ref: smartdas
affiliations:
  - id: smartdas
    name: Smart Data Analysis and Statistics B.V.
    city: Utrecht
  - id: biogen
    name: Biogen
    city: Cambridge, MA, USA
format:
  html:
    toc: true
    number-sections: true
execute:
  cache: true
bibliography: 'https://api.citedrive.com/bib/0d25b38b-db8f-43c4-b934-f4e2f3bd655a/references.bib?x=eyJpZCI6ICIwZDI1YjM4Yi1kYjhmLTQzYzQtYjkzNC1mNGUyZjNiZDY1NWEiLCAidXNlciI6ICIyNTA2IiwgInNpZ25hdHVyZSI6ICI0MGFkYjZhMzYyYWE5Y2U0MjQ2NWE2ZTQzNjlhMWY3NTk5MzhhNzUxZDNjYWIxNDlmYjM4NDgwOTYzMzY5YzFlIn0=/bibliography.bib'
---

``` {r}
#| message: false
#| warning: false
#| echo: false

# List of required packages
required_packages <- c("dplyr", "cobalt", "data.table", "ggplot2",
                       "knitr", "lmtest", "marginaleffects", "MASS",
                       "survey", "tableone", "truncnorm", "sandwich",
                       "MatchIt", "boot", "WeightIt", "survey", "optmatch")

# Install required packages
for (pkg in required_packages) {
    if (!requireNamespace(pkg, quietly = TRUE)) {
        install.packages(pkg)
    }
}
```

```{r}
#| echo: false
#| message: false
#| warning: false

library(dplyr)
library(cobalt)
library(data.table)
library(ggplot2)
library(knitr)
library(lmtest)
library(marginaleffects)
library(MASS)
library(stats)
library(survey)
library(tableone)
library(truncnorm)
library(sandwich)
```

```{r}
#| echo: false
#| message: false
#| include: false
# Function to generate data from PS guidelines manuscript

#F0. Function to simulate MS data
generate_data <- function(
    n, 
    seed = NA,
    beta = c(-0.2, -0.2, -0.2, -0.2, -0.2), # to create heterogeneity treatment effect
    beta.x = c(-1.54, -0.01, 0.06, 0.25, 0.5, 0.13, 0.0000003), # to calculate the outcome 
    percentiles = seq(0, 1, by = 0.2)) {
  
  #' Generate simulated count data with settings based on real-world data
  #' Assume randomized treatment and independent covariates
  #'
  #' @param n sample size; integer
  #' @param seed randomization seed; integer
  #' @param beta coefficients characterizing treatment effect heterogeneity; vector of length 5
  #'             beta[1]*trt*I(high responder to DMT1) +
  #'             beta[2]*trt*I(moderate responder to DMT1) +
  #'             beta[3]*trt*I(neutral) +
  #'             beta[4]*trt*I(moderate responder to DMT0) +
  #'             beta[5]*trt*I(high responder to DMT0)
  #'             In the absence of treatment effect heterogeneity, set all beta[1:5] with the same values
  #' @param beta.x coefficients for main effects of other covariates in the rate; vector of length 7
  #'               beta.x[1] (intercept)
  #'               beta.x[2]*ageatindex_centered
  #'               beta.x[3]*female
  #'               beta.x[4]*prerelapse_num
  #'               beta.x[5]*prevDMTefficacy== hiiumhigh (reference low efficacy)
  #'               beta.x[6]*prevDMTefficacy== none (reference low efficacy)
  #'               beta.x[7]*premedicalcost
  #' @param percentiles percentiles (cumulative, monotone increasing) to define each responder subgroup; vector of floats of length 6
  #' @return data - a data frame of \code{n} rows and 8 columns of simulated data; data.frame
  
  
  set.seed(seed)
  
  if (percentiles[1] != 0 | percentiles[length(percentiles)] != 1 | length(percentiles) != 6) {
    stop("Wrong values of percentiles!")
  }
  
  # Create an empty shell
  ds <- data.table(NULL)
  
  # Define X, A, and time
  ds[, female := rbinom(n = n, size = 1, prob = 0.75)]
  ds[, ageatindex_centered := round(rtruncnorm(n, a = 18, b = 64, mean = 48, sd = 12), 0) - 48 ] # rounded to integers
  ds[, prerelapse_num := rpois(n = n, lambda = 0.44)]
  ds[, prevDMTefficacy := sample(x = c("None", "Low_efficacy", "Medium_high_efficacy"), #previous DM treatment efficacy
                                 size = n, replace = TRUE, prob = c(0.45, 0.44, 0.11))]
  ds[, prevDMTefficacy := factor(prevDMTefficacy, labels = c("None", "Low_efficacy", "Medium_high_efficacy"))]
  ds[, premedicalcost := pmin(round(exp(rnorm(n = n, mean = 8.9, sd = 1.14)), 2), 600000)] # rounded to 2 decimal points
  ds[, numSymptoms := sample(x = c("0", "1", ">=2"),
                             size = n, replace = TRUE, prob = c(0.67, 0.24, 0.09))] # nuisance variable; do not include in the score
  ds[, numSymptoms := factor(numSymptoms, labels = c("0", "1", ">=2"))]
  ds[, finalpostdayscount := ceiling(rgamma(n = n, shape = 0.9, scale = 500))] # rounded up to integers
  ds[, finalpostdayscount := ifelse(finalpostdayscount > 2096, 2096, finalpostdayscount)] # truncate at the max follow up day, 2096
  ds[, finalpostdayscount := ifelse((finalpostdayscount > 2090) & (runif(1, 0, 1) < .5), 29, finalpostdayscount)] # mimic the 1 month peak;  move roughly half of the large values to 29
  
  # Define treatment allocation 
  
  XB <- model.matrix(~.,ds) %*% c(1.22,0.3,-0.1,0.2, 0.35,0.7,-0.00005,0.17,0.02,0)# ~75% people allocated in DMT1 arm based on (age,female,prerelapse_num,DMT efficacy,costs,numSymptoms)
  pi <- exp(XB)/(1 + exp(XB))
  ds[, trt := as.numeric(runif(n) <= pi)]
  ds[, treatment := as.factor(ifelse(trt == 1, "DMT1", "DMT0"))] 
  
  # Define Y (using all above PS predictors except for numSymptoms)
  xmat.score <- as.matrix(model.matrix(~ ageatindex_centered + female + prerelapse_num + prevDMTefficacy + premedicalcost, ds)) 
  gamma <- matrix(c(-0.33, # Intercept
                    -0.001, # Age
                    0.05, # female
                    -0.002, # prerelapse_num
                    0.33, 0.02, # Medium/high and none DMT efficacy
                    -0.0000005), nrow = 7) # premedicalcost
  score = exp(xmat.score %*% gamma)
  ds[, Iscore := cut(score, quantile(score, percentiles), include.lowest = T, labels = seq(1, 5))]
  
  xmat.rate <- as.matrix(model.matrix(~ ageatindex_centered + female + prerelapse_num + prevDMTefficacy + premedicalcost +
                                        Iscore + trt + trt*Iscore, ds))
  
  betas <- matrix(c(beta.x[1], # Intercept
                    beta.x[2], # Age
                    beta.x[3], # female
                    beta.x[4], # prerelapse_num
                    beta.x[5], 
                    beta.x[6], # Medium/high and none DMT efficacy
                    beta.x[7], # premedicalcost
                    # Previous beta.x: -1.54 Intercept, -0.01 Age, 0.06 female, 0.47 prerelapse_num, 0.68, 0.13 Medium/high and none DMT efficacy, 0.000003 premedicalcost
                    0, 0, 0, 0, # Iscore categories 2:5 (reference group is Iscore1, high responders to DMT1)
                    beta[1], beta[2] - beta[1], beta[3] - beta[1], beta[4] - beta[1], beta[5] - beta[1]))
  rate <-  exp(xmat.rate %*% betas)
  ds[, y := rnegbin(n = n, mu = rate*finalpostdayscount/365.25, theta = 3)] # post treatment number of relapses
  
  ds[, Iscore := factor(Iscore, labels = c("High A1","Moderate A1","Neutral","Moderate A0","High A0"))]
  ds[, years := finalpostdayscount / 365.25]
  ds[, age := ageatindex_centered + 48]
  data <- ds[,c("age","female", "prevDMTefficacy", "premedicalcost", "numSymptoms", "prerelapse_num", "treatment", "y", "years","Iscore")]
  return(data)
}
```

```{r}
#| echo: false
#| include: false
dat <- generate_data(n = 10000, seed = 1854)
dat$female <- as.factor(dat$female)
dat$prevDMTefficacy <- as.factor(dat$prevDMTefficacy)
```

## Introduction

The purpose of this document is to provide example R code that demonstrates how to estimate the propensity score and implement matching, stratification, weighting, and regression adjustment for the continuous propensity score. In this example using simulated data, we have two disease modifying therapies (DMT1 and DMT0) and the outcome is the number of post-treatment multiple sclerosis relapses during follow-up. We will estimate the average treatment effect in the treated (ATT) using propensity score matching, stratification, and weighting. We will estimate the average treatment effect in the population (ATE) using regression adjustment for the continuous propensity score. The treatment effects can be interpreted as annualized relapse rate ratios (ARR).

We consider an example dataset with the following characteristics:

```{r}
head(dat)
```

## Comparing baseline characteristics

-   `DMT1` is the treatment group and `DMT0` is the control group
-   `prevDMTefficacy` is previous DMT efficacy (none, low efficacy, and medium/high efficacy)
-   `prerelapse_num` is the number of previous MS relapses

```{r}
#| echo: false
#| include: false
#| results: hide
vars <- c("age", "female", "prevDMTefficacy", "prerelapse_num")

tab1 <- CreateTableOne(vars, data = dat , 
                       factorVars = c("female", "prevDMTefficacy"), 
                       strata = "treatment", 
                       test = FALSE)

tab1.print <- print(tab1, catDigits = 2, contDigits = 2)
```

```{r}
#| echo: false
kable(tab1.print)
```

## Estimating the propensity score

### Logistic regression

We sought to restore balance in the distribution of baseline covariates in patients treated with DMT1 (index treatment) and DMT0 (control tratment). We fit a multivariable logistic regression model in which treatment was regressed on baseline characteristics including age, sex, previous DMT efficacy, and previous number of relapses.

```{r}
# Fit logistic regression model
ps.model <- glm(treatment ~ age + female + prevDMTefficacy + prerelapse_num, 
                data = dat, family = binomial())

# Summary of logistic regression model
summary(ps.model)

# Extract propensity scores
dat$ps <- predict(ps.model, data = dat, type = "response")
```

### Assessing overlap

We examined the degree of overlap in the distribution of propensity scores across treatment groups using histograms and side-by-side box plots.

```{r}
# Histogram
ggplot(dat, aes(x = ps, fill = as.factor(treatment), color = as.factor(treatment))) + 
  geom_histogram(alpha = 0.3, position='identity', bins = 15) + 
  facet_grid(as.factor(treatment) ~ .) + 
  xlab("Probability of Treatment") + 
  ylab("Count") +
  ggtitle("Propensity Score Distribution by Treatment Group") +
  theme(legend.position = "bottom", legend.direction = "vertical")

# Side-by-side box plots
ggplot(dat, aes(x=as.factor(treatment), y=ps, fill=as.factor(treatment))) +
  geom_boxplot() + 
  ggtitle("Propensity Score Distribution by Treatment Group") +
  ylab("Probability of Treatment") + 
  xlab("Treatment group") +
  theme(legend.position = "none")

# Distribution of propensity scores by treatment groups
summary(dat$ps[dat$treatment == "DMT1"])

summary(dat$ps[dat$treatment == "DMT0"])
```

## Propensity score matching

```{r}
#| echo: false

# Keep track of all results
out <- data.frame(method = character(), 
                  estimand = character(), 
                  estimate = numeric(),
                  est.cil = numeric(),
                  est.ciu = numeric())
```

### 1:1 Optimal full matching without replacement

```{r}
#| message: false
library(MatchIt)

# Use MatchIt package for PS matching
opt <- matchit(treatment ~ age + female + prevDMTefficacy + prerelapse_num, 
               data = dat, 
               method = "full",
               estimand = "ATT")

opt
```

### Assess balance after matching

```{r}
summary(opt)

plot(summary(opt))

# black line is treated group, grey line is control group
plot(opt, type = "density", which.xs = vars) 
```

### Estimating the ATT

We can estimate the ATT in the matched sample using Poisson regression in which the number of post-treatment relapses is regressed on treatment status and follow-up time for each patient (captured by the variable `years`). More details are provided at [this link](https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html).

```{r}
# Matched data
matched.data <- match.data(opt)

# Poisson regression model
opt.fit <- glm(y ~ treatment + offset(log(years)), 
            family = poisson(link = "log"),
            data = matched.data, 
            weights = weights)

# Treatment effect estimation
opt.comp <- avg_comparisons(opt.fit,
                            variables = "treatment",
                            vcov = ~subclass,
                            newdata = subset(matched.data, treatment == "DMT1"),
                            wts = "weights",
                            comparison  = "ratio")

opt.comp |> tidy()
```

```{r}
#| echo: false
#| include: false
est_att_mean <- (opt.comp |> tidy()) %>% pull(estimate)
est_att_clb <- (opt.comp |> tidy()) %>% pull(conf.low)
est_att_cub <- (opt.comp |> tidy()) %>% pull(conf.high)

out <- out %>% add_row(data.frame(method = "Optimal full matching", 
                  estimand = "ATT", 
                  estimate = est_att_mean,
                  est.cil = est_att_clb,
                  est.ciu = est_att_cub))
```

As indicated in the summary output above, the annualized relapse rate ratio for DMT1 vs DMT0 among patients treated with DMT0 (ATT) is given as `r round(est_att_mean,2)` with a 95% confidence interval ranging from `r round(est_att_clb,2)` to `r round(est_att_cub,2)`.

## Propensity score stratification

### Divide sample into quintiles of propensity scores

We will form five mutually exclusive groups of the estimated propensity score.

```{r}
# Divide the PS scores into five strata of roughly equal size
breaks <- quantile(dat$ps, probs = seq(0, 1, by = 0.2))

dat <- dat %>% mutate(ps.strata = cut(ps, 
                                      breaks = breaks,
                                      labels = seq(1:5),
                                      include.lowest = TRUE))

# Number of patients in each stratum
table(dat$ps.strata)
```

### Assess balance within each propensity score stratum

Within each propensity score stratum, treated and control patients should have similar values of the propensity score and the distribution of baseline covariates should be approximately balanced between treatment groups.

#### Propensity Score Stratum #1

```{r}
#| results: hide
tab1.strata1 <- CreateTableOne(vars, data = dat %>% filter(ps.strata == 1), 
                               factorVars = c("female", "prevDMTefficacy"), 
                               strata = "treatment", 
                               test = FALSE)

tab1.strata1.print <- print(tab1.strata1, 
                            catDigits = 2, 
                            contDigits = 2, 
                            smd = TRUE)

tab1.strata1.print
```

```{r}
#| echo: false
kable(tab1.strata1.print)
```

#### Propensity Score Stratum #2

```{r}
#| results: hide
tab1.strata2 <- CreateTableOne(vars, data = dat %>% filter(ps.strata == 2), 
                               factorVars = c("female", "prevDMTefficacy"), 
                               strata = "treatment", test = FALSE)

tab1.strata2.print <- print(tab1.strata2, catDigits = 2, contDigits = 2, 
                            smd = TRUE)
```

```{r}
#| echo: false
kable(tab1.strata2.print)
```

#### Propensity Score Stratum #3

```{r}
#| results: hide
tab1.strata3 <- CreateTableOne(vars, data = dat %>% filter(ps.strata == 3), 
                               factorVars = c("female", "prevDMTefficacy"), 
                               strata = "treatment", test = FALSE)

tab1.strata3.print <- print(tab1.strata3, catDigits = 2, contDigits = 2, 
                            smd = TRUE)
```

```{r}
#| echo: false
kable(tab1.strata3.print)
```

#### Propensity Score Stratum #4

```{r}
#| results: hide
tab1.strata4 <- CreateTableOne(vars, data = dat %>% filter(ps.strata == 4), 
                               factorVars = c("female", "prevDMTefficacy"), 
                               strata = "treatment", test = FALSE)

tab1.strata4.print <- print(tab1.strata4, catDigits = 2, contDigits = 2, 
                            smd = TRUE)
```

```{r}
#| echo: false
kable(tab1.strata4.print)
```

#### Propensity Score Stratum #5

```{r}
#| results: hide
tab1.strata5 <- CreateTableOne(vars, data = dat %>% filter(ps.strata == 5), 
                               factorVars = c("female", "prevDMTefficacy"), 
                               strata = "treatment", test = FALSE)

tab1.strata5.print <- print(tab1.strata5, catDigits = 2, contDigits = 2, 
                            smd = TRUE)
```

```{r}
#| echo: false
kable(tab1.strata5.print)
```

### Estimating and pooling of stratum-specific treatment effects

The overall ATT across strata can be estimated by weighting stratum-specific estimates by the proportion of treated patients in each stratum over all treated patients in the sample.

We first define a function `att.strata.function()` to calculate stratum-specific estimates of the treatment effect:

```{r}
#| message: false
att.strata.function <- function(data, stratum, confint = TRUE) {

  fit <- glm("y ~ treatment + offset(log(years))",
      family = poisson(link = "log"),
      data = data %>% filter(ps.strata == stratum))

  arr <- round(as.numeric(exp(coef(fit)["treatmentDMT1"])), digits = 3)
  ll <- ul <- NA
  
  if (confint) {
    ll <- round(exp(confint(fit))["treatmentDMT1",1], digits = 3)
    ul <- round(exp(confint(fit))["treatmentDMT1",2], digits = 3)
  }
  
  return(c("stratum" = stratum,
           "arr" = arr,
           "ci_lower"  = ll,
           "ci_upper"  = ul))
}

arr.strata <- as.data.frame(t(sapply(1:5, att.strata.function, data = dat)))
arr.strata
```

Subsequently, we define a function `weights.strata.function()` to calculate the weights for each stratum. The weight is the proportion of treated patients in each stratum over all treated patients in the sample:

```{r}
weights.strata.function <- function(data, stratum) {
  n_DMT1_stratum <- nrow(data %>% filter(ps.strata == stratum & treatment == "DMT1"))
  n_DMT1_all <- nrow(data %>% filter(treatment == "DMT1"))
  weight <- n_DMT1_stratum/n_DMT1_all
  return(c("stratum" = stratum, "weight" = weight))
}

weights.strata <- as.data.frame(t(sapply(1:5, weights.strata.function, data = dat)))
weights.strata
```

```{r}
# Create table with ARRs and weights for each PS stratum
arr.weights.merged <- merge(arr.strata, weights.strata, by = "stratum")

# Calculate the weighted ARR for each stratum
arr.weights.merged <- arr.weights.merged %>%
  mutate(weighted.arr = as.numeric(arr) * weight)

# Sum the weighted ARRs across strata to get the overall ATT
sum(arr.weights.merged$weighted.arr)
```

```{r}
#| echo: false
out <- out %>% add_row(data.frame(method = "Propensity score stratification", 
                  estimand = "ATT", 
                  estimate = sum(arr.weights.merged$weighted.arr),
                  est.cil = NA,
                  est.ciu = NA))
```

We now define a new function `ps.stratification.bootstrap()` that integrates estimation of the ATT and the PS weights for bootstrapping purposes:

```{r}
#| message: false
ps.stratification.bootstrap <- function(data, inds) {
  d <- data[inds,]
  
  d$ps.strata <- cut(d$ps, 
                     breaks = c(quantile(dat$ps, probs = seq(0, 1, by = 0.2))),
                     labels = seq(5),
                     include.lowest = TRUE)
  
  arr.strata <- as.data.frame(t(sapply(1:5, att.strata.function, 
                                       data = d, confint = FALSE)))
  
  weights.strata <- as.data.frame(t(sapply(1:5, weights.strata.function, data = d)))
  
  return(arr.strata$arr[1] * weights.strata$weight[1] + 
           arr.strata$arr[2] * weights.strata$weight[2] +
           arr.strata$arr[3] * weights.strata$weight[3] + 
           arr.strata$arr[4] * weights.strata$weight[4] +
           arr.strata$arr[5] * weights.strata$weight[5])                                                  
}
```

We can now estimate the treatment effect and its confidence interval using the bootstrap procedure:

```{r}
#| warning: false
#| message: false
library(boot)

set.seed(1854)
arr.stratification.boot <- boot(data = dat, 
                                statistic = ps.stratification.bootstrap, 
                                R = 1000)
```

We can summarize the bootstrap samples as follows:

```{r}
# Bootstrap estimate of the ARR
median(arr.stratification.boot$t)

# Bootstrap 95% CI of the ARR
boot.ci(arr.stratification.boot, conf = 0.95, type = "perc")
```

```{r}
#| echo: false
bci <- boot.ci(arr.stratification.boot, conf = 0.95, type = "perc")
out <- out %>% add_row(data.frame(method = "Propensity score stratification (with bootstrapping)", 
                  estimand = "ATT", 
                  estimate = median(arr.stratification.boot$t),
                  est.cil = bci$perc[1,4],
                  est.ciu = bci$perc[1,5]))
```

## Propensity score weighting

### Calculate propensity score weights for ATT

Propensity score weighting reweights the study sample to generate an artificial population (i.e., pseudo-population) in which the covariates are no longer associated with treatment, thereby removing confounding by measured covariates. For the ATT, the weight for all treated patients is set to one. Conversely, the weight for patients in the control group is set to the propensity score divided by one minus the propensity score, that is, (PS/(1 − PS)). We estimated stabilized weights to address extreme weights.

```{r}
#| message: false
#| 
library(WeightIt)

w.out <- weightit(treatment ~ age + female + prevDMTefficacy + prerelapse_num,
                  data = dat,
                  method = "ps",
                  estimand = "ATT")
                  #stabilize = TRUE)

w.out
summary(w.out)
plot(summary(w.out))
```

### Assess balance in the weighted sample

```{r}
bal.tab(w.out, stats = c("m", "v"), thresholds = c(m = .05))
```

### Estimate the ATT

One way to estimate the ATT is to use the survey package. The function `svyglm()` generates model-robust (Horvitz-Thompson-type) standard errors by default, and thus does not require additional adjustments.

```{r}
library(survey)

weighted.data <- svydesign(ids = ~1, data = dat, weights = ~w.out$weights)

weighted.fit <- svyglm(y ~ treatment + offset(log(years)),
                       family = poisson(link = "log"),
                       design = weighted.data)

exp(coef(weighted.fit)["treatmentDMT1"])
exp(confint(weighted.fit))["treatmentDMT1",] 
```

```{r}
#| echo: false
out <- out %>% add_row(data.frame(method = "Propensity score weighting", 
                  estimand = "ATT", 
                  estimate = exp(coef(weighted.fit)["treatmentDMT1"]),
                  est.cil = exp(confint(weighted.fit))["treatmentDMT1","2.5 %"] ,
                  est.ciu = exp(confint(weighted.fit))["treatmentDMT1","97.5 %"]))
```

As indicated above, propensity score weighting yielded an ATT estimate of `r round(exp(coef(weighted.fit)["treatmentDMT1"]),2)` (95% CI: `r paste(round(exp(confint(weighted.fit))["treatmentDMT1",] ,2), collapse = "; ")`).

An alternative approach is to use `glm()` to estimate the treatment effect and calculate robust standard errors.

```{r}
# Alternative way to estimate treatment effect
weighted.fit2 <- glm(y ~ treatment + offset(log(years)),
                     family = poisson(link = "log"),
                     data = dat,
                     weights = w.out$weights)

# Extract the estimated ARR
exp(coef(weighted.fit2))["treatmentDMT1"]

# Calculate robust standard error and p-value of the log ARR
coeftest(weighted.fit2, vcov. = vcovHC)["treatmentDMT1",]

# Derive 95% confidence interval of the ARR
exp(lmtest::coefci(weighted.fit2, 
       level = 0.95, # 95% confidence interval
       vcov. = vcovHC)["treatmentDMT1",])
```

```{r}
#| echo: false
est_att_wfit2_mean <- exp(coef(weighted.fit2))["treatmentDMT1"]
est_att_wfit2_clb <- exp(coefci(weighted.fit2, level = 0.95, vcov. = vcovHC)["treatmentDMT1","2.5 %"])
est_att_wfit2_cub <- exp(coefci(weighted.fit2, level = 0.95, vcov. = vcovHC)["treatmentDMT1","97.5 %"])

out <- out %>% add_row(data.frame(method = "Propensity score weighting (robust SE)", 
                  estimand = "ATT", 
                  estimate = est_att_wfit2_mean,
                  est.cil = est_att_wfit2_clb,
                  est.ciu = est_att_wfit2_cub))
```

Using this approach, the ATT estimate was `r round(est_att_wfit2_mean, 2)` (95% CI: `r paste(round(c(est_att_wfit2_clb, est_att_wfit2_cub),2), collapse = "; " )`).

## Regression adjustment for the propensity score for the ATE

In this approach, a regression model is fitted to describe the observed outcome as a function of the received treatment and the estimated propensity score:

```{r}
ps.reg.fit <- glm(y ~ treatment + ps + offset(log(years)),
                  family = poisson(link = "log"),
                  data = dat)

summary(ps.reg.fit)

# ATE
exp(coef(ps.reg.fit))["treatmentDMT1"] 
```

```{r}
#| echo: false
est_att_psreg_mean <- exp(coef(ps.reg.fit))["treatmentDMT1"] 
est_att_psreg_clb <- exp(confint(ps.reg.fit, level = 0.95)["treatmentDMT1","2.5 %"])
est_att_psreg_cub <- exp(confint(ps.reg.fit, level = 0.95)["treatmentDMT1","97.5 %"])

out <- out %>% add_row(data.frame(method = "PS regression adjustment", 
                  estimand = "ATE", 
                  estimate = est_att_psreg_mean,
                  est.cil = est_att_psreg_clb,
                  est.ciu = est_att_psreg_cub))
```

Bootstrapped confidence intervals can be obtained as follows:

```{r}
# Function to bootstrap for 95% CIs
ps.reg.bootstrap <- function(data, inds) {
  d <- data[inds,]
  
  fit <- glm(y ~ treatment + ps + offset(log(years)),
              family = poisson(link = "log"),
              data = d)
  
  return(exp(coef(fit))["treatmentDMT1"])
}

set.seed(1854)

# Generate 1000 bootstrap replicates
arr.boot <- boot(dat, statistic = ps.reg.bootstrap, R = 1000) 

# Extract the median annualized relapse rate across 1000 bootstrap replicates
median(arr.boot$t) 

# Bootstrap 95% CI of the ARR
boot.ci(arr.boot, conf = 0.95, type = "perc")
```

```{r}
#| echo: false
bci <- boot.ci(arr.boot, conf = 0.95, type = "perc")
out <- out %>% add_row(data.frame(method = "PS regression adjustment (bootstrapping)", 
                  estimand = "ATE", 
                  estimate = median(arr.boot$t),
                  est.cil = bci$perc[1,4],
                  est.ciu = bci$perc[1,5]))
```

## Overview

```{r}
#| echo: false
#| 
kable(out, col.names = c("Method", "Estimand", "Estimate", "95% CI (lower)", "95% CI (upper)"))
```

## Version info {.unnumbered}
This chapter was rendered using the following version of R and its packages:

```{r}
#| echo: false
#| message: false
#| warning: false
sessionInfo()
```

## References {.unnumbered}