tlf-efficacy.Rmd

# Efficacy

Following [ICH E3 guidance](https://database.ich.org/sites/default/files/E3_Guideline.pdf),
we need to summarize primary and secondary efficacy endpoints,
in Section 11.4, Efficacy Results and Tabulations of Individual Participant.

```{r}
library(haven) # Read SAS data
library(dplyr) # Manipulate data
library(tidyr) # Manipulate data
library(r2rtf) # Reporting in RTF format
library(emmeans) # LS mean estimation
```

For efficacy analysis, we only analyze the change from baseline glucose data at week 24.

```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"}
knitr::include_graphics("tlf/tlf_eff.pdf")
```

## Analysis dataset

To prepare the analysis dataset,
we need both `adsl` and `adlbc` datasets for this analysis.

```{r}
adsl <- read_sas("adam_data/adsl.sas7bdat")
adlb <- read_sas("adam_data/adlbc.sas7bdat")
```

We first define the analysis dataset using
efficacy population flag `EFFFL` and
all records post baseline (`AVISITN > 1`) and on or before Week 24 (`AVISITN <= 24`).
Here the variable `AVISITN` is the numerical analysis visit.
For example, if the analysis visit is recorded as "Baseline", i.e., `AVISIT = Baseline`, then `AVISITN = 0`;
if the analysis visit is recorded as "Week 24", i.e., `AVISIT = Week 24`, then `AVISITN = 24`;
if the analysis visit is blank, then `AVISITN` is also blank.
We will discuss these missing values in Section 6.4.

```{r}
gluc <- adlb %>%
  left_join(adsl %>% select(USUBJID, EFFFL), by = "USUBJID") %>%
  # PARAMCD is parameter code and here we focus on Glucose (mg/dL)
  filter(EFFFL == "Y" & PARAMCD == "GLUC") %>%
  arrange(TRTPN) %>%
  mutate(TRTP = factor(TRTP, levels = unique(TRTP)))

ana <- gluc %>%
  filter(AVISITN > 0 & AVISITN <= 24) %>%
  arrange(AVISITN) %>%
  mutate(AVISIT = factor(AVISIT, levels = unique(AVISIT)))
```

Below is the first few records of the analysis dataset.

- AVAL: analysis value
- BASE: baseline value
- CHG: change from baseline

```{r}
ana %>%
  select(USUBJID, TRTPN, AVISIT, AVAL, BASE, CHG) %>%
  head(4)
```

## Helper functions

To prepare the report data frame, we created a few helper functions
by using the `fmt_num()` function defined in Chapter \@ref(population).

- Format Estimators

```{r}
fmt_est <- function(.mean,
                    .sd,
                    digits = c(1, 2)) {
  .mean <- fmt_num(.mean, digits[1], width = digits[1] + 4)
  .sd <- fmt_num(.sd, digits[2], width = digits[2] + 3)
  paste0(.mean, " (", .sd, ")")
}
```

- Format Confidence Interval

```{r}
fmt_ci <- function(.est,
                   .lower,
                   .upper,
                   digits = 2,
                   width = digits + 3) {
  .est <- fmt_num(.est, digits, width)
  .lower <- fmt_num(.lower, digits, width)
  .upper <- fmt_num(.upper, digits, width)
  paste0(.est, " (", .lower, ",", .upper, ")")
}
```

- Format P-Value

```{r}
fmt_pval <- function(.p, digits = 3) {
  scale <- 10^(-1 * digits)
  p_scale <- paste0("<", digits)
  if_else(.p < scale, p_scale, fmt_num(.p, digits = digits))
}
```

## Summary of observed data

We first summarize observed data at Baseline and Week 24

```{r}
t11 <- gluc %>%
  filter(AVISITN %in% c(0, 24)) %>%
  group_by(TRTPN, TRTP, AVISITN) %>%
  summarise(
    n = n(),
    mean_sd = fmt_est(mean(AVAL), sd(AVAL))
  ) %>%
  pivot_wider(
    id_cols = c(TRTP, TRTPN),
    names_from = AVISITN,
    values_from = c(n, mean_sd)
  )

t11
```

We also summarize observed change from baseline glucose at Week 24.

```{r}
t12 <- gluc %>%
  filter(AVISITN %in% 24) %>%
  group_by(TRTPN, AVISITN) %>%
  summarise(
    n_chg = n(),
    mean_chg = fmt_est(
      mean(CHG, na.rm = TRUE),
      sd(CHG, na.rm = TRUE)
    )
  )

t12
```

## Missing data imputation

In clinical trials, missing data is inevitable.
In this study, there are also missing values in glucose data.

```{r}
count(ana, AVISIT)
```

For simplicity and illustration purpose,
we use the last observation carried forward (LOCF) approach to handle missing data.
LOCF approach is a single imputation approach that is **not recommended**
in real studies.
Interested readers can find more discussion on missing data approaches in the book:
[The Prevention and Treatment of Missing Data in Clinical Trials](https://www.ncbi.nlm.nih.gov/books/NBK209904/pdf/Bookshelf_NBK209904.pdf).

```{r}
ana_locf <- ana %>%
  group_by(USUBJID) %>%
  mutate(locf = AVISITN == max(AVISITN)) %>%
  filter(locf)
```

## ANCOVA model

We start to analyze the imputed data using the ANCOVA model with treatment and baseline glucose as covariates.

```{r}
fit <- lm(CHG ~ BASE + TRTP, data = ana_locf)
summary(fit)
```

Then we use the `emmeans` R package to obtain
within and between group least square mean (LS mean)

```{r}
fit_within <- emmeans(fit, "TRTP")
fit_within
```

```{r}
t13 <- fit_within %>%
  as_tibble() %>%
  mutate(ls = fmt_ci(emmean, lower.CL, upper.CL)) %>%
  select(TRTP, ls)
t13
```

```{r}
fit_between <- pairs(fit_within, reverse = TRUE)
fit_between
```

```{r}
t2 <- fit_between %>%
  as_tibble() %>%
  mutate(
    ls = fmt_ci(
      estimate,
      estimate - 1.96 * SE,
      estimate + 1.96 * SE
    ),
    p = fmt_pval(p.value)
  ) %>%
  filter(str_detect(contrast, "- Placebo")) %>%
  select(contrast, ls, p)

t2
```

## Reporting

We combine `t11`, `t12` and `t13` to get the first part of the report data

```{r}
t1 <- cbind(
  t11 %>% ungroup() %>% select(TRTP, ends_with("0"), ends_with("24")),
  t12 %>% ungroup() %>% select(ends_with("chg")),
  t13 %>% ungroup() %>% select(ls)
)
t1
```

Then we use `r2rtf` to prepare the table format for `t1`.
We also highlight how to handle special characters in this example.

Special characters `^` and `_` are used to define superscript and subscript of text. And `{}` is to define the part that will be impacted.
For example, `{^a}` provide a superscript `a` for footnote notation.
`r2rtf` also supports most LaTeX characters.
Examples can be found on the
[`r2rtf` get started page](https://merck.github.io/r2rtf/articles/r2rtf.html#special-character).
The `text_convert` argument in `r2rtf_*()` functions controls whether convert special characters.

```{r}
t1_rtf <- t1 %>%
  data.frame() %>%
  rtf_title(c(
    "ANCOVA of Change from Baseline Glucose (mmol/L) at Week 24",
    "LOCF",
    "Efficacy Analysis Population"
  )) %>%
  rtf_colheader("| Baseline | Week 24 | Change from Baseline",
    col_rel_width = c(2.5, 2, 2, 4)
  ) %>%
  rtf_colheader(paste(
    "Treatment |",
    paste0(rep("N | Mean (SD) | ", 3), collapse = ""),
    "LS Mean (95% CI){^a}"
  ),
  col_rel_width = c(2.5, rep(c(0.5, 1.5), 3), 2)
  ) %>%
  rtf_body(
    text_justification = c("l", rep("c", 7)),
    col_rel_width = c(2.5, rep(c(0.5, 1.5), 3), 2)
  ) %>%
  rtf_footnote(c(
    "{^a}Based on an ANCOVA model after adjusting baseline value. LOCF approach is used to impute missing values.",
    "ANCOVA = Analysis of Covariance, LOCF = Last Observation Carried Forward",
    "CI = Confidence Interval, LS = Least Squares, SD = Standard Deviation"
  ))

t1_rtf %>%
  rtf_encode() %>%
  write_rtf("tlf/tlf_eff1.rtf")
```

```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"}
knitr::include_graphics("tlf/tlf_eff1.pdf")
```

we also use `r2rtf` to prepare the table format for `t2`

```{r}
t2_rtf <- t2 %>%
  data.frame() %>%
  rtf_colheader("Pairwise Comparison | Difference in LS Mean (95% CI){^a} | p-Value",
    col_rel_width = c(4.5, 4, 2)
  ) %>%
  rtf_body(
    text_justification = c("l", "c", "c"),
    col_rel_width = c(4.5, 4, 2)
  )

t2_rtf %>%
  rtf_encode() %>%
  write_rtf("tlf/tlf_eff2.rtf")
```

```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"}
knitr::include_graphics("tlf/tlf_eff2.pdf")
```

Finally we combine the two parts to get the final table using `r2rtf`.
This is achieved by providing a list of `t1_rtf` and `t2_rtf` as input for
`rtf_encode`.

```{r}
list(t1_rtf, t2_rtf) %>%
  rtf_encode() %>%
  write_rtf("tlf/tlf_eff.rtf")
```

```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"}
knitr::include_graphics("tlf/tlf_eff.pdf")
```

In conclusion, the procedure to generate the above efficacy results table is summarized as follows.

- Step 1: Read the data into R, i.e., `adsl` and `adlb`.
- Step 2: Define the analysis dataset. In this example, we define two analysis datasets. The first dataset is the efficacy population (`gluc`). The second dataset is the collection of all records post baseline and on or before week 24 (`ana`).
- Step 3: Impute the missing values. In our example, we name the `ana` dataset after imputation as `ana_locf`.
- Step 4: Summarize `gluc`, i.e., calculate the mean and standard derivation, and then format it into an RTF table.
- Step 5: Summarize `ana_locf`, i.e., calculate the pairwise comparison by ANCOVA model, and then format it into an RTF table.
- Step 6: Rowly bind the output in Step 4 and Step 5.