Kaplan Meier (KM) plot function for anchored and unanchored cases

It is wrapper function of basic_kmplot. The argument setting is similar to maic_anchored and maic_unanchored, and it is used in those two functions.

Usage

kmplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  ...
)

Arguments

weights_object: an object returned by estimate_weight
tte_ipd: a data frame of individual patient data (IPD) of internal trial, contain at least "USUBJID", "EVENT", "TIME" columns and a column indicating treatment assignment
tte_pseudo_ipd: a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least "EVENT", "TIME"
trt_ipd: a string, name of the interested investigation arm in internal trial dat_igd (real IPD)
trt_agd: a string, name of the interested investigation arm in external trial dat_pseudo (pseudo IPD)
trt_common: a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case
normalize_weights: logical, default is FALSE. If TRUE, scaled_weights (normalized weights) in weights_object$data will be used.
trt_var_ipd: a string, column name in tte_ipd that contains the treatment assignment
trt_var_agd: a string, column name in tte_pseudo_ipd that contains the treatment assignment
km_conf_type: a string, pass to conf.type of survfit
km_layout: a string, only applicable for unanchored case (trt_common = NULL), indicated the desired layout of output KM curve.
...: other arguments in basic_kmplot

Value

In unanchored case, a KM plot with risk set table. In anchored case, depending on km_layout,

if "by_trial", 2 by 1 plot, first all KM curves (incl. weighted) in IPD trial, and then KM curves in AgD trial, with risk set table.
if "by_arm", 2 by 1 plot, first KM curves of trt_agd and trt_ipd (with and without weights), and then KM curves of trt_common in AgD trial and IPD trial (with and without weights). Risk set table is appended.
if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.

Examples

# unanchored example using kmplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  km_conf_type = "log-log",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# anchored example using kmplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)


# plot by arm
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)


# plot all
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)