RBMIT01

Tables for RBMI

Creating Intercurrent Event Data
Define Key Variables

Set the imputation strategy to "MAR" for each patient with at least one missing observation.

Code

data_ice <- data_full %>%
  dplyr::arrange(dplyr::across(.cols = c("TMP_ID", vars$visit))) %>%
  dplyr::filter(is.na(.data[[missing_var]])) %>%
  dplyr::group_by(TMP_ID) %>%
  dplyr::slice(1) %>%
  dplyr::ungroup() %>%
  dplyr::select(all_of(c("TMP_ID", vars$visit))) %>%
  dplyr::mutate(strategy = "MAR")

Draws

The rbmi::draws() function fits the imputation models and stores the corresponding parameter estimates or Bayesian posterior parameter draws. The three main inputs to the rbmi::draws() function are:

data - The primary longitudinal data.frame containing the outcome variable and all covariates.
data_ice - A data.frame which specifies the first visit affected by an intercurrent event (ICE) and the imputation strategy for handling missing outcome data after the ICE. At most one ICE which is to be imputed by a non-MAR strategy is allowed per subject.
method - The statistical method used to fit the imputation models and to create imputed datasets.

Define the names of key variables in our dataset and the covariates included in the imputation model using rbmi::set_vars(). Note that the covariates argument can also include interaction terms.

Code

debug_mode <- FALSE

draws_vars <- rbmi::set_vars(
  outcome = missing_var,
  visit = vars$visit,
  group = vars$group,
  covariates = covariates$draws
)
draws_vars$subjid <- "TMP_ID"

Define which imputation method to use, then create samples for the imputation parameters by running the draws() function.

Code

set.seed(123)
draws_method <- method_bayes()

draws_obj <- rbmi::draws(
  data = data_full,
  data_ice = data_ice,
  vars = draws_vars,
  method = draws_method
)

Compiling Stan model please wait...


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Impute

The next step is to use the parameters from the imputation model to generate the imputed datasets. This is done via the rbmi::impute() function. The function only has two key inputs: the imputation model output from rbmi::draws() and the reference groups relevant to reference-based imputation methods. It’s usage is thus:

Code

impute_references <- c("DRUG" = "PLACEBO", "PLACEBO" = "PLACEBO")

impute_obj <- rbmi::impute(
  draws_obj,
  references = impute_references
)

Analyze

The next step is to run the analysis model on each imputed dataset. This is done by defining an analysis function and then calling rbmi::analyse() to apply this function to each imputed dataset.

Code

# Define analysis model
analyse_fun <- ancova

ref_levels <- levels(impute_obj$data$group[[1]])
names(ref_levels) <- c("ref", "alt")

analyse_obj <- rbmi::analyse(
  imputations = impute_obj,
  fun = analyse_fun,
  vars = rbmi::set_vars(
    subjid = "TMP_ID",
    outcome = missing_var,
    visit = vars$visit,
    group = vars$group,
    covariates = covariates$analyse
  )
)

Pool

The rbmi::pool() function can be used to summarize the analysis results across multiple imputed datasets to provide an overall statistic with a standard error, confidence intervals and a p-value for the hypothesis test of the null hypothesis that the effect is equal to 0. Using the broom::tidy() function the rbmi final results are reshaped.

Code

library(broom)

pool_obj <- rbmi::pool(
  results = analyse_obj,
  conf.level = 0.95,
  alternative = c("two.sided", "less", "greater"),
  type = c("percentile", "normal")
)

df <- tidy(pool_obj)
df

  group       est    se_est lower_cl_est upper_cl_est   est_contr  se_contr
1   ref -1.615820 0.4862316    -2.575771   -0.6558685          NA        NA
2   alt -1.707626 0.4749573    -2.645319   -0.7699335 -0.09180645 0.6826279
3   ref -4.218952 0.6505048    -5.503615   -2.9342892          NA        NA
4   alt -2.815275 0.6364376    -4.072204   -1.5583461  1.40367691 0.9149382
5   ref -6.330717 0.7020406    -7.717703   -4.9437297          NA        NA
6   alt -4.177543 0.6863985    -5.533665   -2.8214218  2.15317319 0.9854002
7   ref -7.540923 0.8133804    -9.152471   -5.9293739          NA        NA
8   alt -4.824323 0.7521449    -6.310829   -3.3378160  2.71660019 1.1225222
  lower_cl_contr upper_cl_contr    p_value relative_reduc visit conf_level
1             NA             NA         NA             NA     4       0.95
2     -1.4394968       1.255884 0.89317724     0.05681725     4       0.95
3             NA             NA         NA             NA     5       0.95
4     -0.4032862       3.210640 0.12696838    -0.33270747     5       0.95
5             NA             NA         NA             NA     6       0.95
6      0.2063790       4.099967 0.03040925    -0.34011524     6       0.95
7             NA             NA         NA             NA     7       0.95
8      0.4945431       4.938657 0.01698541    -0.36024772     7       0.95

Create Output

Finally, use functions from the rtables and tern packages to generate a nicely formatted rtable object.

Code

result <- basic_table() %>%
  split_cols_by("group", ref_group = levels(df$group)[1]) %>%
  split_rows_by("visit", split_label = "Visit", label_pos = "topleft") %>%
  summarize_rbmi() %>%
  build_table(df)
result

Visit                                       ref                alt       
—————————————————————————————————————————————————————————————————————————
4                                                                        
  Adjusted Mean (SE)                   -1.616 (0.486)     -1.708 (0.475) 
    95% CI                            (-2.576, -0.656)   (-2.645, -0.770)
  Difference in Adjusted Means (SE)                       -0.092 (0.683) 
    95% CI                                               (-1.439, 1.256) 
    Relative Reduction (%)                                     5.7%      
    p-value (RBMI)                                            0.8932     
5                                                                        
  Adjusted Mean (SE)                   -4.219 (0.651)     -2.815 (0.636) 
    95% CI                            (-5.504, -2.934)   (-4.072, -1.558)
  Difference in Adjusted Means (SE)                       1.404 (0.915)  
    95% CI                                               (-0.403, 3.211) 
    Relative Reduction (%)                                    -33.3%     
    p-value (RBMI)                                            0.1270     
6                                                                        
  Adjusted Mean (SE)                   -6.331 (0.702)     -4.178 (0.686) 
    95% CI                            (-7.718, -4.944)   (-5.534, -2.821)
  Difference in Adjusted Means (SE)                       2.153 (0.985)  
    95% CI                                                (0.206, 4.100) 
    Relative Reduction (%)                                    -34.0%     
    p-value (RBMI)                                            0.0304     
7                                                                        
  Adjusted Mean (SE)                   -7.541 (0.813)     -4.824 (0.752) 
    95% CI                            (-9.152, -5.929)   (-6.311, -3.338)
  Difference in Adjusted Means (SE)                       2.717 (1.123)  
    95% CI                                                (0.495, 4.939) 
    Relative Reduction (%)                                    -36.0%     
    p-value (RBMI)                                            0.0170

Data Setup

We use a publicly available example dataset from an antidepressant clinical trial of an active drug versus placebo from the rbmi package. The relevant endpoint is the Hamilton 17-item depression rating scale (HAMD17) which was assessed at baseline and at weeks 1, 2, 4, and 6. Study drug discontinuation occurred in 24% of subjects from the active drug and 26% of subjects from placebo. All data after study drug discontinuation are missing and there is a single additional intermittent missing observation.

Code

library(tern.rbmi)
library(dplyr)
set.seed(123)

data <- antidepressant_data
levels(data$THERAPY) <- c("PLACEBO", "DRUG") # This is important! The order defines the computation order later

missing_var <- "CHANGE"
vars <- list(
  id = "PATIENT",
  visit = "VISIT",
  expand_vars = c("BASVAL", "THERAPY"),
  group = "THERAPY"
)
covariates <- list(
  draws = c("BASVAL*VISIT", "THERAPY*VISIT"),
  analyse = c("BASVAL")
)

data <- data %>%
  dplyr::select(PATIENT, THERAPY, VISIT, BASVAL, THERAPY, CHANGE) %>%
  dplyr::mutate(dplyr::across(.cols = vars$id, ~ as.factor(.x))) %>%
  dplyr::arrange(dplyr::across(.cols = c(vars$id, vars$visit)))

# Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset
data_full <- do.call(
  expand_locf,
  args = list(
    data = data,
    vars = c(vars$expand_vars, vars$group),
    group = vars$id,
    order = c(vars$id, vars$visit)
  ) %>%
    append(lapply(data[c(vars$id, vars$visit)], levels))
)

data_full <- data_full %>%
  dplyr::group_by(dplyr::across(vars$id)) %>%
  dplyr::mutate(!!vars$group := Filter(Negate(is.na), .data[[vars$group]])[1])

# there are duplicates - use first value
data_full <- data_full %>%
  dplyr::group_by(dplyr::across(c(vars$id, vars$group, vars$visit))) %>%
  dplyr::slice(1) %>%
  dplyr::ungroup()
# need to have a single ID column
data_full <- data_full %>%
  tidyr::unite("TMP_ID", dplyr::all_of(vars$id), sep = "_#_", remove = FALSE) %>%
  dplyr::mutate(TMP_ID = as.factor(TMP_ID))

Reproducibility

Timestamp

[1] "2024-11-30 17:39:24 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-30
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

Download

Draws

Impute

Analyze

Pool

Create Output

Data Setup

Reproducibility

Timestamp

Session Info

.lock file

`.lock` file