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Updating Data Objects

Source: R/Data-methods.R
update-Data-method.Rd

[Stable]

A method that updates existing Data object with new data.

Usage 

# S4 method for class 'Data'
update(
  object,
  x,
  y,
  response = rep(NA_integer_, length(y)),
  ID = length(object@ID) + seq_along(y),
  new_cohort = TRUE,
  check = TRUE,
  backfill = FALSE,
  cohort = NULL,
  ...
)

Arguments

object

(Data)
object you want to update.

x

(number)
the dose level (one level only!).

y

(integer)
the DLT vector (0/1 vector) for all patients in this cohort. You can also supply numeric vectors, but these will then be converted to integer internally.

response

(integer)
the efficacy response vector (0/1 vector). May contain NA.

ID

(integer)
the patient IDs. You can also supply numeric vectors, but these will then be converted to integer internally.

new_cohort

(flag)
if TRUE (default) the new data are assigned to a new cohort.

check

(flag)
whether the validation of the updated object should be conducted. See details below.

backfill

(flag)
whether the new patients being added are from a backfill cohort.

cohort

(int)
if provided, the new patients will be assigned to this cohort index. If NULL (default), the cohort index will be determined based on the new_cohort parameter.

...

not used.

Value

The new, updated Data object.

Details

The current implementation of this update method allows for updating the Data class object by adding a single dose level x only. However, there might be some use cases where the new cohort to be added contains a placebo and active dose. Hence, such update would need to be performed iteratively by calling the update method twice. For example, in the first call a user can add a placebo, and then in the second call, an active dose. Since having a cohort with placebo only is not allowed, the update method would normally throw the error when attempting to add a placebo in the first call. To allow for such updates, the check parameter should be then set to FALSE for that first call.

Examples 

# Create some data of class 'Data'.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
#> Used default patient IDs!
#> Used best guess cohort indices!

# Update the data with a new cohort.
my_data1 <- update(my_data, x = 20, y = c(0L, 1L, 1L))
my_data1
#> An object of class "Data"
#> Slot "x":
#>  [1]  0.1  0.5  1.5  3.0  6.0 10.0 10.0 10.0 20.0 20.0 20.0
#> 
#> Slot "y":
#>  [1] 0 0 0 0 0 0 1 0 0 1 1
#> 
#> Slot "doseGrid":
#>  [1]  0.1  0.5  1.5  3.0  6.0 10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0 26.0 28.0
#> [16] 30.0 32.0 34.0 36.0 38.0 40.0 42.0 44.0 46.0 48.0 50.0 52.0 54.0 56.0 58.0
#> [31] 60.0 62.0 64.0 66.0 68.0 70.0 72.0 74.0 76.0 78.0 80.0
#> 
#> Slot "nGrid":
#> [1] 41
#> 
#> Slot "xLevel":
#>  [1]  1  2  3  4  5  6  6  6 11 11 11
#> 
#> Slot "placebo":
#> [1] FALSE
#> 
#> Slot "backfilled":
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 
#> Slot "response":
#>  [1] NA NA NA NA NA NA NA NA NA NA NA
#> 
#> Slot "ID":
#>  [1]  1  2  3  4  5  6  7  8  9 10 11
#> 
#> Slot "cohort":
#>  [1] 1 2 3 4 5 6 6 6 7 7 7
#> 
#> Slot "nObs":
#> [1] 11
#>