Binary endpoints
Last outcome
First we go through an example where the final outcome instead of being continuous, is a binary outcome. As an example, this could for example be weight loss above a certain threshold at the end of the study. The way the outcome should be included in the data is as a numeric vector with 1 for those patients that had the outcome and 0 for those that had not.
We do not have any example dataset with a final binary endpoint included in the package, so for this vignette we modify an existing dataset:
data("hce_scenario_a")
# Create data with binary version of continuous final endpoint
bin_data <- hce_scenario_a
# Index of all continuous outcome rows
idx_cont <- bin_data$GROUP == "Continuous outcome"
# Rename outcome
bin_data[idx_cont,"GROUP"] <- "Binary outcome"
# Binary version (>= 0/< 0)
bin_data[idx_cont,"AVAL0"] <- bin_data[idx_cont,"AVAL0"] >= 0
bin_data[idx_cont,"AVAL"] <- bin_data[idx_cont,"AVAL0"] +
bin_data[idx_cont,"GROUPN"]
head(bin_data)
## SUBJID GROUP GROUPN AVAL0 AVAL TRTP
## 1 1 Outcome I 0 120.4409 120.4409 Active
## 2 2 Binary outcome 40000 1.0000 40001.0000 Control
## 3 3 Binary outcome 40000 1.0000 40001.0000 Active
## 4 4 Outcome I 0 577.3114 577.3114 Control
## 5 5 Outcome II 10000 781.7581 10781.7581 Active
## 6 6 Outcome III 20000 985.0980 20985.0980 ControlIf we now want to create a maraca object for this data, we need to slightly update the default code. By default, maraca expects the last outcome to be continuous. In order for the function to know that the last outcome is binary, we have to update the parameter last_type. The parameter currently accepts the inputs "binary" and "continuous".
column_names <- c(
outcome = "GROUP",
arm = "TRTP",
value = "AVAL0"
)
step_outcomes <- c("Outcome I", "Outcome II",
"Outcome III", "Outcome IV")
last_outcome <- "Binary outcome"
arm_levels <- c(active = "Active",
control = "Control")
mar <- maraca(
bin_data, step_outcomes, last_outcome,
arm_levels, column_names,
fixed_followup_days = 3*365,
compute_win_odds = TRUE,
# Important change: Add information that last endpoint is
# not continuous (the default)
last_type = "binary"
)The maraca object can now be plotted. A binary endpoint is plotted as an ellipsis. The point in the middle of the ellipsis indicates the proportion that has met the binary endpoint. The width of the ellipsis (x-axis range) shows the confidence interval. Specifying vline_type = "mean" (the default) will add vertical lines indicating the proportions in each treatment group for easier readibility. Note that vline_type = "median" (the default for continuous endpoints) will result in an error. The same is true for setting density_plot_type to anything other than "default".
Step outcome
What if the binary outcome is not the last outcome of the hierarchical endpoint but rather one (or several) of the previous outcomes? So rather than solely having time-to-event endpoints within the step function part of the plot, we also have at least one binary (so not time depending) variable. To include a binary variable, we expect the data for this outcome to include only patients that had the outcome and they all have to have the original analysis value 1.
Let’s create a dataset with 2 binary outcomes.
data("hce_scenario_a")
# Create data with binary version of continuous final endpoint
bin_data2 <- hce_scenario_a
# Index of all continuous outcome rows
idx_bin <- bin_data2$GROUP %in% c("Outcome III", "Outcome IV")
# Binary version (>= 0/< 0), coded as 1
bin_data2[idx_bin,"AVAL0"] <- bin_data2[idx_bin,"AVAL0"] >= 500
bin_data2[idx_bin,"AVAL"] <- bin_data2[idx_bin,"AVAL0"] +
bin_data2[idx_bin,"GROUPN"]
# Remove 0 rows (only include patients that had the outcome)
bin_data2 <- bin_data2[bin_data2$AVAL0 != 0,]
head(bin_data2)
## SUBJID GROUP GROUPN AVAL0 AVAL TRTP
## 1 1 Outcome I 0 120.440921 120.4409 Active
## 2 2 Continuous outcome 40000 3.345229 40003.3452 Control
## 3 3 Continuous outcome 40000 22.802615 40022.8026 Active
## 4 4 Outcome I 0 577.311386 577.3114 Control
## 5 5 Outcome II 10000 781.758081 10781.7581 Active
## 6 6 Outcome III 20000 1.000000 20001.0000 ControlAgain we need to slightly update the default code if we want to create a maraca object for this data. By default, maraca expects all the step outcomes to be time-to-event endpoints. In order for the function to know that there are binary outcomes, we have to update the parameter step_types. The parameter currently accepts the inputs "binary" and "tte". If all the step outcomes are of the same type, the user can give the type as a string (such as by default step_types = "tte"). If there are different endpoint types, then the user has to provide a vector with one element for each step outcome. Similarly, the fixed-follow up time can be given as a single number or a vector. Note that the fixed-follow-up time is only needed for time-to-event endpoints, so if providing a vector then it should contain one value for each time-to-event endpoint.
column_names <- c(
outcome = "GROUP",
arm = "TRTP",
value = "AVAL0"
)
step_outcomes <- c("Outcome I", "Outcome II",
"Outcome III", "Outcome IV")
last_outcome <- "Continuous outcome"
arm_levels <- c(active = "Active",
control = "Control")
mar <- maraca(
bin_data2, step_outcomes, last_outcome,
arm_levels, column_names,
fixed_followup_days = 3*365,
compute_win_odds = TRUE,
# Important change: Add information that last endpoint is
# not continuous (the default)
step_types = c("tte","tte","binary","binary")
)Again, we can plot the final object.
As with all maraca objects, the different plotting parameters can be used.
Also, the win odds plots can be created as usual.
