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Create a Survival Outcome from CDISC Data

Source: R/Surv_CNSR.R
Surv_CNSR.Rd

The aim of Surv_CNSR() is to map the inconsistency in data convention between the survival package and CDISC ADaM ADTTE data model.

The function creates a survival object (e.g. survival::Surv()) that uses CDISC ADaM ADTTE coding conventions and converts the arguments to the status/event variable convention used in the survival package.

The AVAL and CNSR arguments are passed to survival::Surv(time = AVAL, event = 1 - CNSR, type = "right", origin = 0).

Usage

Surv_CNSR(AVAL, CNSR)

Arguments

AVAL

The follow-up time. The follow-up time is assumed to originate from zero. When no argument is passed, the default value is a column/vector named AVAL.

CNSR

The censoring indicator where 1=censored and 0=death/event. When no argument is passed, the default value is a column/vector named CNSR.

Value

Object of class 'Surv'

Details

The Surv_CNSR() function creates a survival object utilizing the expected data structure in the CDISC ADaM ADTTE data model, mapping the CDISC ADaM ADTTE coding conventions with the expected status/event variable convention used in the survival package---specifically, the coding convention used for the status/event indicator. The survival package expects the status/event indicator in the following format: 0=alive, 1=dead. Other accepted choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death). A final but risky option is to omit the indicator variable, in which case all subjects are assumed to have an event.

The CDISC ADaM ADTTE data model adopts a different coding convention for the event/status indicator. Using this convention, the event/status variable is named 'CNSR' and uses the following coding: censor = 1, status/event = 0.

See also

survival::Surv()

Examples

# Use the `Surv_CNSR()` function with ggsurvfit functions
survfit2(formula = Surv_CNSR() ~ STR01, data = adtte) %>%
  ggsurvfit() +
  add_confidence_interval()


# Use the `Surv_CNSR()` function with functions from other packages as well
survival::survfit(Surv_CNSR() ~ STR01, data = adtte)
#> Call: survfit(formula = Surv_CNSR() ~ STR01, data = adtte)
#> 
#>                   n events median 0.95LCL 0.95UCL
#> STR01=Negative  893    166   2.20    2.15    2.39
#> STR01=Positive 1306    589   3.41    3.19    3.63
survival::survreg(Surv_CNSR() ~ STR01 + AGE, data = adtte) %>%
  broom::tidy()
#> # A tibble: 4 × 5
#>   term          estimate std.error statistic  p.value
#>   <chr>            <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)    0.943     0.147        6.40 1.56e-10
#> 2 STR01Positive  0.213     0.0534       3.98 6.78e- 5
#> 3 AGE            0.00376   0.00221      1.70 8.97e- 2
#> 4 Log(scale)    -0.543     0.0306     -17.8  1.47e-70