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Survival Time Point Analysis

Source: R/survival_timepoint.R
survival_timepoint.Rd

[Stable]

Summarize patient's survival rate and difference of survival rates between groups at a time point.

Usage

s_surv_timepoint(
  df,
  .var,
  time_point,
  is_event,
  control = control_surv_timepoint()
)

a_surv_timepoint(
  df,
  .var,
  time_point,
  is_event,
  control = control_surv_timepoint()
)

s_surv_timepoint_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  time_point,
  control = control_surv_timepoint(),
  ...
)

a_surv_timepoint_diff(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  time_point,
  control = control_surv_timepoint(),
  ...
)

surv_timepoint(
  lyt,
  vars,
  ...,
  table_names_suffix = "",
  var_labels = "Time",
  show_labels = "visible",
  method = c("surv", "surv_diff", "both"),
  .stats = c("pt_at_risk", "event_free_rate", "rate_ci", "rate_diff", "rate_diff_ci",
    "ztest_pval"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

df

(data frame)
data set containing all analysis variables.

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

time_point

(number)
survival time point of interest.

is_event

(logical)
TRUE if event, FALSE if time to event is censored.

control

a (list) of parameters for comparison details, specified by using
the helper function control_surv_timepoint. Some possible parameter options are:

  • conf_level: (proportion)
    confidence level of the interval for survival rate.

  • conf_type: (string)
    "plain" (default), "log", "log-log" for confidence interval type,
    see more in survival::survfit(). Note that the option "none" is no longer supported.

  • time_point: (number)
    survival time point of interest.

.ref_group

(data frame or vector)
the data corresponding to the reference group.

.in_ref_col

(logical)
TRUE when working with the reference level, FALSE otherwise.

...

additional arguments for the lower level functions.

lyt

(layout)
input layout where analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

table_names_suffix

(string)
optional suffix for the table_names used for the rtables to avoid warnings from duplicate table names.

var_labels

character for label.

show_labels

label visibility: one of "default", "visible" and "hidden".

method

(string)
either surv (survival estimations), surv_diff (difference in survival with the control) or both.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels.

Value

The statistics are:

  • pt_at_risk : patients remaining at risk.

  • event_free_rate : event free rate (%).

  • rate_se : standard error of event free rate.

  • rate_ci : confidence interval for event free rate.

The statistics are:

  • rate_diff : event free rate difference between two groups.

  • rate_diff_ci : confidence interval for the difference.

  • ztest_pval : p-value to test the difference is 0.

Functions

  • s_surv_timepoint(): Statistics Function which analyzes survival rate.

  • a_surv_timepoint(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

  • s_surv_timepoint_diff(): Statistics Function which analyzes difference between two survival rates.

  • a_surv_timepoint_diff(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

  • surv_timepoint(): Analyze Function which adds the survival rate analysis to the input layout. Note that additional formatting arguments can be used here.

Examples

library(scda)
library(dplyr)

ADTTE <- synthetic_cdisc_data("latest")$adtte
ADTTE_f <- ADTTE %>%
  filter(PARAMCD == "OS") %>%
  mutate(
    AVAL = day2month(AVAL),
    is_event = CNSR == 0
  )
df <- ADTTE_f %>%
  filter(ARMCD == "ARM A")

# Internal function - s_surv_timepoint
if (FALSE) {
s_surv_timepoint(df, .var = "AVAL", time_point = 7, is_event = "is_event")
}

# Internal function - a_surv_timepoint
if (FALSE) {
a_surv_timepoint(df, .var = "AVAL", time_point = 7, is_event = "is_event")
}

df_ref_group <- ADTTE_f %>%
  filter(ARMCD == "ARM B")

# Internal function - s_surv_timepoint_diff
if (FALSE) {
s_surv_timepoint_diff(df, df_ref_group, .in_ref_col = TRUE, .var = "AVAL", is_event = "is_event")
s_surv_timepoint_diff(
  df,
  df_ref_group,
  .in_ref_col = FALSE,
  .var = "AVAL",
  time_point = 7,
  is_event = "is_event"
)
}

# Internal function - a_surv_timepoint_diff
if (FALSE) {
a_surv_timepoint_diff(
  df,
  df_ref_group,
  .in_ref_col = FALSE,
  .var = "AVAL",
  time_point = 7,
  is_event = "is_event"
)
}


# Survival at given time points.
basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  add_colcounts() %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    time_point = 7
  ) %>%
  build_table(df = ADTTE_f)
#>                                    ARM A            ARM B            ARM C     
#>                                   (N=134)          (N=134)          (N=132)    
#> ———————————————————————————————————————————————————————————————————————————————
#> 7 Months                                                                       
#>   Patients remaining at risk        103              102               83      
#>   Event Free Rate (%)              83.83            82.73            66.98     
#>   95% CI                       (77.49, 90.17)   (76.15, 89.30)   (58.79, 75.17)

# Difference in survival at given time points.
basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  add_colcounts() %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    time_point = 9,
    method = "surv_diff",
    .indent_mods = c("rate_diff" = 0L, "rate_diff_ci" = 2L, "ztest_pval" = 2L)
  ) %>%
  build_table(df = ADTTE_f)
#>                                    ARM A        ARM B             ARM C     
#>                                   (N=134)      (N=134)           (N=132)    
#> ————————————————————————————————————————————————————————————————————————————
#> 9 Months                                                                    
#>   Difference in Event Free Rate                 -4.34            -20.05     
#>       95% CI                                (-14.48, 5.79)   (-31.02, -9.09)
#>       p-value (Z-test)                          0.4012           0.0003     

# Survival and difference in survival at given time points.
basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  add_colcounts() %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    time_point = 9,
    method = "both"
  ) %>%
  build_table(df = ADTTE_f)
#>                                     ARM A            ARM B             ARM C     
#>                                    (N=134)          (N=134)           (N=132)    
#> —————————————————————————————————————————————————————————————————————————————————
#> 9 Months                                                                         
#>   Patients remaining at risk          97               93               73       
#>   Event Free Rate (%)               80.52            76.18             60.47     
#>   95% CI                        (73.66, 87.39)   (68.73, 83.64)   (51.92, 69.02) 
#> Difference in Event Free Rate                        -4.34            -20.05     
#> 95% CI                                           (-14.48, 5.79)   (-31.02, -9.09)
#> p-value (Z-test)                                     0.4012           0.0003