tests/testthat/test_check_inputs_adjustedsurv.r
In RobinDenz1/adjustedCurves: Confounder-Adjusted Survival Curves and Cumulative Incidence Functions
library(survival)
set.seed(42)
sim_dat <- readRDS(system.file("testdata",
"d_sim_surv_n_20.Rds",
package="adjustedCurves"))
sim_dat$group <- as.factor(sim_dat$group)
test_that("variable not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="grouup",
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"grouup is not a valid column name in 'data'.")
})
test_that("variable has wrong type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="x1",
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("The column in 'data' specified by 'variable' needs to be",
" a factor variable."))
})
test_that("variable has wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable=c("x1", "x2"),
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'variable' must be a character string of length 1",
" specifying the grouping variable in 'data'."))
})
test_that("non-standard evaluation in variable", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable=group,
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'variable' must be a character string specifying ",
"a variable in 'data'."))
})
test_that("variable is really wrong", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable=1,
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Arguments 'variable', 'ev_time' and 'event' ",
"must be character strings, specifying variables ",
"in 'data'."))
})
test_that("ev_time not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time2",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"time2 is not a valid column name in 'data'.")
})
test_that("ev_time wrong format", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="group",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"The column in 'data' specified by 'ev_time' must be numeric.")
})
test_that("ev_time wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time=c("group", "group"),
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'ev_time' must be a character string of length 1 ",
"specifying the time until the event or ",
"censoring occured."))
})
sim_dat$time2 <- sim_dat$time
sim_dat$time2[1] <- -1
test_that("ev_time contains negative values", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time2",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"All values in the 'ev_time' variable must be >= 0.")
})
test_that("non-standard evaluation in ev_time", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time=time3,
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'ev_time' must be a character string specifying a ",
"variable in 'data'."))
})
test_that("event not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event2",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"event2 is not a valid column name in 'data'.")
})
test_that("event wrong format", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="group",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"The column in 'data' specified by 'event' must be numeric or logical.")
})
test_that("event wrong type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event=2,
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Arguments 'variable', 'ev_time' and 'event'",
" must be ", "character strings, specifying ",
"variables in 'data'."))
})
sim_dat$event2 <- sim_dat$event
sim_dat$event2[1] <- 2
test_that("event contains more than two event types", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event2",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("The 'event' variable has to be a ",
"binary event indicator.", " For data with competing ",
"risks, see the 'adjustedcif' function."))
})
test_that("event wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event=c("group", "group"),
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'event' must be a character string of length 1",
" specifying the binary event indicator."))
})
test_that("non-standard evaluation in event", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event=status,
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'event' must be a character string specifying ",
"a variable in 'data'."))
})
test_that("method undefined", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km3",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Method 'km3' is undefined. See documentation",
" for details on available methods."))
})
test_that("method wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method=c("km", "km"),
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'method' must be a single character string. ",
"Using multiple methods in one call is ",
"currently not supported."))
})
test_that("wrong conf_int type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=1,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_int' must be either TRUE or FALSE.")
})
test_that("wrong conf_int length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=c(TRUE, TRUE),
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_int' must be either TRUE or FALSE, not a vector.")
})
test_that("wrong conf_level type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=-0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_level' must be a number < 1 and > 0.")
})
test_that("wrong conf_level format", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level="-0.95",
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_level' must be a number < 1 and > 0.")
})
test_that("wrong conf_level length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=c(0.95, 0.8),
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_level' must be a number < 1 and > 0, not a vector.")
})
test_that("wrong bootstrap type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=1,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'bootstrap' must be either TRUE or FALSE.")
})
test_that("wrong bootstrap length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=c(TRUE, TRUE),
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'bootstrap' must be either TRUE or FALSE, not a vector.")
})
test_that("wrong n_boot type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=-2,
na.action="na.omit",
clean_data=TRUE),
"'n_boot' must be a positive integer > 2.")
})
test_that("wrong n_boot length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=c(10, 10),
na.action="na.omit",
clean_data=TRUE),
"'n_boot' must be a positive integer > 2, not a vector.")
})
test_that("wrong times", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times="string",
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'times' must be a numeric vector or NULL.")
})
test_that("wrong na.action type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action=1,
clean_data=TRUE),
"'na.action' must be a function or a single character string. See details.")
})
test_that("wrong na.action length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action=c("1", "1"),
clean_data=TRUE),
paste0("'na.action' must be a function or a character string,",
" not a vector. See documentation."))
})
test_that("wrong clean_data type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=1),
"'clean_data' must be either TRUE or FALSE.")
})
test_that("wrong clean_data length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=c(TRUE, TRUE)),
"'clean_data' must be either TRUE or FALSE, not a vector.")
})
test_that("no extrapolation", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=30,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Values in 'times' must be smaller than ",
"max(data[,ev_time]). No extrapolation allowed."),
fixed=TRUE)
})
test_that("bootstrap only with treatment_model that can be updated", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=runif(n=20)),
paste0("'treatment_model' needs to be a model that can be ",
"refit or a formula object when using bootstrap=TRUE."))
})
test_that("no outcome_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'outcome_vars' must be specified when using ",
"method='direct_pseudo'. See documentation."))
})
test_that("wrong outcome_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=2),
paste0("'outcome_vars' should be a character vector of ",
"column names in 'data', used to model ",
"the outcome mechanism."))
})
test_that("ps_scores > 1 or < 0", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
treatment_model=rep(2, nrow(sim_dat))),
paste0("Propensity Scores supplied using the 'treatment_model' ",
"argument must be smaller than 1 and bigger than 0."))
})
test_that("ps_scores wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
treatment_model=c(0.1, 0.2)),
paste0("The vector of propensity score supplied in the ",
"'treatment_model' argument must be of ",
"length nrow(data)."), fixed=TRUE)
})
test_that("wrong type_time argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
type_time="aha"),
"'type_time' should be either 'factor', 'bs' or 'ns'.")
})
test_that("too many spline_df", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=c(0.01, 0.02),
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
type_time="bs",
spline_df=10),
"'spline_df' > len(times) might lead to problems.",
fixed=TRUE)
})
test_that("too many spline_df with default", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=c(0.01, 0.02),
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
type_time="bs"),
paste0("'spline_df' > length(times) might lead to ",
"problems when type_time!='factor'."),
fixed=TRUE)
})
test_that("not enough time points", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=0.2,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1")),
paste0("'geese' models require at least two distinct ",
"time points. Add more points in time to 'times' ",
"and try again."))
})
test_that("missing treatment_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_vars' has to be specified when ",
"using method='emp_lik'."))
})
test_that("wrong treatment_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=1),
paste0("'treatment_vars' should be a character vector of ",
"column names in 'data', used to model the ",
"outcome mechanism."))
})
test_that("treatment_vars not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x7", "x8")),
paste0("'treatment_vars' should be a character vector of ",
"column names in 'data', used to model the ",
"outcome mechanism."))
})
test_that("wrong standardize argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1"),
standardize=2),
"Argument 'standardize' must be either TRUE or FALSE.")
})
test_that("wrong moment argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1"),
moment=1),
"Argument 'moment' must be either 'first' or 'second'.")
})
test_that("0/1 variables in method='emp_lik'", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1")),
paste0("Dichotomous variables coded with 0 and 1 found in",
" 'treatment_vars'. Consider recoding to -1 and 1 ",
"to avoid estimation problems."))
})
test_that("weights in matching", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=FALSE,
conf_level=0.95,
times=0.2,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=runif(20, min=10, max=20)),
paste0("Propensity Scores > 1 or < 0 not allowed. ",
"Perhaps you supplied weights on accident?"))
})
test_that("no treatment_model with matching", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=FALSE,
conf_level=0.95,
times=0.2,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_model' must be specified when ",
"using method='matching'."))
})
test_that("no treatment_model with method='aiptw'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"Argument 'treatment_model' must be specified when using method='aiptw'.")
})
treat_mod <- list()
class(treat_mod) <- "glm"
test_that("no outcome_model with method='aiptw'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod),
"Argument 'outcome_model' must be specified when using method='aiptw'.")
})
test_that("no outcome_model with method='direct'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"Argument 'outcome_model' must be specified when using method='direct'.")
})
outcome_model <- list()
class(outcome_model) <- "pecRpart"
test_that("bootstrapping with pecRpart", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
paste0("Bootstrapping is currently not supported with ",
"method='direct' and an 'outcome_model' of ",
"class 'pecRpart'."))
})
class(outcome_model) <- "glm"
test_that("glm with censoring", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
paste0("'outcome_model' of class c('glm', 'ols', ",
"'randomForest') are only allowed when there ",
"is no censoring."), fixed=TRUE)
})
outcome_model <- survival::coxph(Surv(time, event) ~ 1, data=sim_dat)
test_that("coxph not including 'variable'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
"'variable' has to be included in the cox-regression model.")
})
sim_dat$x7 <- stats::runif(n=nrow(sim_dat))
test_that("strat_cupples with continuous confounder", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_cupples",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x7"),
paste0("Variables in 'adjust_vars' have to be integer, ",
"factor or character variables. Continuous variables ",
"are not allowed when using method='strat_cupples'."))
})
test_that("adjust_vars not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x8"),
"'adjust_vars' must specify valid column names in 'data'.")
})
test_that("invalid reference data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x1",
reference=sim_dat[, c("x2", "x3")]),
paste0("If a 'reference' data.frame is supplied, it ",
"needs to contain all variables listed ",
"in 'adjust_vars'."))
})
sim_dat$.ALL <- 1
test_that("strat_cupples with '.ALL' column", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_cupples",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x1"),
paste0("The column name '.ALL' cannot be used with ",
"method='strat_cupples'. Please rename that ",
"variable and rerun the function."))
})
test_that("'adjust_vars' not specified with strat_ method", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"Argument 'adjust_vars' needs to be specified when using method='strat_amato'.")
})
sim_dat$.COVARS <- 1
test_that("strat_amato with '.COVARS' column", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x1"),
paste0("The column name '.COVARS' cannot be used with ",
"method='strat_amato'. Please rename that variable ",
"and rerun the function."))
})
test_that("no 'treatment_model' with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_model' must be defined when ",
"using method='iptw_km'. See documentation."))
})
test_that("no 'treatment_model' with iptw_pseudo / aiptw_pseudo", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_model' must be specified when ",
"using method='iptw_pseudo'. See documentation."))
})
treat_mod <- list()
class(treat_mod) <- "gam"
test_that("wrong 'treatment_model' with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod),
paste0("'treatment_model' must be a glm or multinom object, ",
"a formula or a numeric vector of weights. ",
"See documentation."))
})
test_that("wrong 'treatment_model' with aiptw_pseudo", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod,
outcome_vars=c("x1", "x2")),
paste0("Argument 'treatment_model' must be one of: 'glm', ",
"'multinom', 'mira' or a numeric vector of ",
"propensity scores."))
})
fake_survfit <- list()
class(fake_survfit) <- "survfit"
mod <- list(fit=fake_survfit)
class(mod) <- "selectCox"
test_that("wrong selectCox mod", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=mod),
paste0("The final 'fit' object in the 'selectCox' model ",
"must be a coxph object, not survfit."))
})
test_that("wrong weights length with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=c(1, 2)),
paste0("If weights are supplied directly in the ",
"'treatment_model' argument, they must be of ",
"length nrow(data)."), fixed=TRUE)
})
test_that("negative weights with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=rep(-1, nrow(sim_dat))),
paste0("Weights supplied directly to the 'treatment_model' ",
"argument must be positive."))
})
sim_dat3 <- sim_dat
sim_dat3$group <- 1
test_that("only one treatment", {
expect_error(check_inputs_adjustedsurv(data=sim_dat3,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=group ~ x1),
"There have to be at least two groups in 'variable'.")
})
sim_dat3$group <- c(rep(0, 10), rep(1, 5), rep(2, 5))
sim_dat3$group <- as.factor(sim_dat3$group)
test_that("categorical when not allowed", {
expect_error(check_inputs_adjustedsurv(data=sim_dat3,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Categorical treatments are currently not supported ",
"for method='matching'."))
})
test_that("conf_int when not allowed", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod),
paste0("Asymptotic or exact variance calculations are ",
"currently not available for method='matching'. ",
"Use bootstrap=TRUE to get bootstrap estimates."),
fixed=TRUE)
})
sim_dat3$group <- as.character(c(rep(0, 10), rep(1, 10)))
test_that("'variable' is not a factor", {
expect_error(check_inputs_adjustedsurv(data=sim_dat3,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1", "x3")),
paste0("The column in 'data' specified by 'variable' needs ",
"to be a factor variable."))
})
## multiple imputation stuff
sim_dat_na_time <- sim_dat_na_group <- sim_dat_na_event <- sim_dat_na_x1 <-
sim_dat
sim_dat_na_time$time[3] <- NA
sim_dat_na_group$group[3] <- NA
sim_dat_na_event$event[3] <- NA
sim_dat_na_x1$x1[3] <- NA
mids_na_time <- suppressWarnings(mice::mice(sim_dat_na_time, m=2,
printFlag=FALSE))
mids_na_group <- suppressWarnings(mice::mice(sim_dat_na_group, m=2,
printFlag=FALSE))
mids_na_event <- suppressWarnings(mice::mice(sim_dat_na_event, m=2,
printFlag=FALSE))
mids_na_x1 <- suppressWarnings(mice::mice(sim_dat_na_x1, m=2,
printFlag=FALSE))
outcome_model <- list()
class(outcome_model) <- "coxph"
treatment_model <- list()
class(treatment_model) <- "glm"
test_that("wrong outcome_model with mira", {
expect_error(check_inputs_adjustedsurv(data=mids_na_x1,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
paste0("When using multiple imputation, mira objects need ",
"to be supplied to 'outcome_model' instead of ",
"single models. See documentation."))
})
test_that("wrong treatment_model with mira", {
expect_error(check_inputs_adjustedsurv(data=mids_na_x1,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treatment_model),
paste0("When using multiple imputation, mira objects or a ",
"formula need to be supplied to 'treatment_model' ",
"instead of single models. See documentation."))
})
test_that("wrong censoring_model with mira", {
expect_error(check_inputs_adjustedsurv(data=mids_na_x1,
variable="group",
ev_time="time",
event="event",
method="aiptw",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
censoring_model=outcome_model),
paste0("When using multiple imputation, mira objects need to ",
"be supplied to 'censoring_model' instead of single ",
"models. See documentation."))
})
RobinDenz1/adjustedCurves documentation built on Sept. 27, 2024, 7:04 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(survival)
set.seed(42)
sim_dat <- readRDS(system.file("testdata",
"d_sim_surv_n_20.Rds",
package="adjustedCurves"))
sim_dat$group <- as.factor(sim_dat$group)
test_that("variable not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="grouup",
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"grouup is not a valid column name in 'data'.")
})
test_that("variable has wrong type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="x1",
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("The column in 'data' specified by 'variable' needs to be",
" a factor variable."))
})
test_that("variable has wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable=c("x1", "x2"),
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'variable' must be a character string of length 1",
" specifying the grouping variable in 'data'."))
})
test_that("non-standard evaluation in variable", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable=group,
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'variable' must be a character string specifying ",
"a variable in 'data'."))
})
test_that("variable is really wrong", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable=1,
ev_time="time",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Arguments 'variable', 'ev_time' and 'event' ",
"must be character strings, specifying variables ",
"in 'data'."))
})
test_that("ev_time not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time2",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"time2 is not a valid column name in 'data'.")
})
test_that("ev_time wrong format", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="group",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"The column in 'data' specified by 'ev_time' must be numeric.")
})
test_that("ev_time wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time=c("group", "group"),
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'ev_time' must be a character string of length 1 ",
"specifying the time until the event or ",
"censoring occured."))
})
sim_dat$time2 <- sim_dat$time
sim_dat$time2[1] <- -1
test_that("ev_time contains negative values", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time2",
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"All values in the 'ev_time' variable must be >= 0.")
})
test_that("non-standard evaluation in ev_time", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time=time3,
event="event",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'ev_time' must be a character string specifying a ",
"variable in 'data'."))
})
test_that("event not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event2",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"event2 is not a valid column name in 'data'.")
})
test_that("event wrong format", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="group",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"The column in 'data' specified by 'event' must be numeric or logical.")
})
test_that("event wrong type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event=2,
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Arguments 'variable', 'ev_time' and 'event'",
" must be ", "character strings, specifying ",
"variables in 'data'."))
})
sim_dat$event2 <- sim_dat$event
sim_dat$event2[1] <- 2
test_that("event contains more than two event types", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event2",
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("The 'event' variable has to be a ",
"binary event indicator.", " For data with competing ",
"risks, see the 'adjustedcif' function."))
})
test_that("event wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event=c("group", "group"),
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'event' must be a character string of length 1",
" specifying the binary event indicator."))
})
test_that("non-standard evaluation in event", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event=status,
method="km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'event' must be a character string specifying ",
"a variable in 'data'."))
})
test_that("method undefined", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km3",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Method 'km3' is undefined. See documentation",
" for details on available methods."))
})
test_that("method wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method=c("km", "km"),
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("'method' must be a single character string. ",
"Using multiple methods in one call is ",
"currently not supported."))
})
test_that("wrong conf_int type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=1,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_int' must be either TRUE or FALSE.")
})
test_that("wrong conf_int length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=c(TRUE, TRUE),
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_int' must be either TRUE or FALSE, not a vector.")
})
test_that("wrong conf_level type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=-0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_level' must be a number < 1 and > 0.")
})
test_that("wrong conf_level format", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level="-0.95",
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_level' must be a number < 1 and > 0.")
})
test_that("wrong conf_level length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=c(0.95, 0.8),
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'conf_level' must be a number < 1 and > 0, not a vector.")
})
test_that("wrong bootstrap type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=1,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'bootstrap' must be either TRUE or FALSE.")
})
test_that("wrong bootstrap length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=c(TRUE, TRUE),
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'bootstrap' must be either TRUE or FALSE, not a vector.")
})
test_that("wrong n_boot type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=-2,
na.action="na.omit",
clean_data=TRUE),
"'n_boot' must be a positive integer > 2.")
})
test_that("wrong n_boot length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=c(10, 10),
na.action="na.omit",
clean_data=TRUE),
"'n_boot' must be a positive integer > 2, not a vector.")
})
test_that("wrong times", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times="string",
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"'times' must be a numeric vector or NULL.")
})
test_that("wrong na.action type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action=1,
clean_data=TRUE),
"'na.action' must be a function or a single character string. See details.")
})
test_that("wrong na.action length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action=c("1", "1"),
clean_data=TRUE),
paste0("'na.action' must be a function or a character string,",
" not a vector. See documentation."))
})
test_that("wrong clean_data type", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=1),
"'clean_data' must be either TRUE or FALSE.")
})
test_that("wrong clean_data length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=c(TRUE, TRUE)),
"'clean_data' must be either TRUE or FALSE, not a vector.")
})
test_that("no extrapolation", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="km",
conf_int=TRUE,
conf_level=0.95,
times=30,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Values in 'times' must be smaller than ",
"max(data[,ev_time]). No extrapolation allowed."),
fixed=TRUE)
})
test_that("bootstrap only with treatment_model that can be updated", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=runif(n=20)),
paste0("'treatment_model' needs to be a model that can be ",
"refit or a formula object when using bootstrap=TRUE."))
})
test_that("no outcome_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'outcome_vars' must be specified when using ",
"method='direct_pseudo'. See documentation."))
})
test_that("wrong outcome_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=2),
paste0("'outcome_vars' should be a character vector of ",
"column names in 'data', used to model ",
"the outcome mechanism."))
})
test_that("ps_scores > 1 or < 0", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
treatment_model=rep(2, nrow(sim_dat))),
paste0("Propensity Scores supplied using the 'treatment_model' ",
"argument must be smaller than 1 and bigger than 0."))
})
test_that("ps_scores wrong length", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
treatment_model=c(0.1, 0.2)),
paste0("The vector of propensity score supplied in the ",
"'treatment_model' argument must be of ",
"length nrow(data)."), fixed=TRUE)
})
test_that("wrong type_time argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
type_time="aha"),
"'type_time' should be either 'factor', 'bs' or 'ns'.")
})
test_that("too many spline_df", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=c(0.01, 0.02),
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
type_time="bs",
spline_df=10),
"'spline_df' > len(times) might lead to problems.",
fixed=TRUE)
})
test_that("too many spline_df with default", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=c(0.01, 0.02),
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1"),
type_time="bs"),
paste0("'spline_df' > length(times) might lead to ",
"problems when type_time!='factor'."),
fixed=TRUE)
})
test_that("not enough time points", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=0.2,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_vars=c("x1")),
paste0("'geese' models require at least two distinct ",
"time points. Add more points in time to 'times' ",
"and try again."))
})
test_that("missing treatment_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_vars' has to be specified when ",
"using method='emp_lik'."))
})
test_that("wrong treatment_vars argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=1),
paste0("'treatment_vars' should be a character vector of ",
"column names in 'data', used to model the ",
"outcome mechanism."))
})
test_that("treatment_vars not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x7", "x8")),
paste0("'treatment_vars' should be a character vector of ",
"column names in 'data', used to model the ",
"outcome mechanism."))
})
test_that("wrong standardize argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1"),
standardize=2),
"Argument 'standardize' must be either TRUE or FALSE.")
})
test_that("wrong moment argument", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1"),
moment=1),
"Argument 'moment' must be either 'first' or 'second'.")
})
test_that("0/1 variables in method='emp_lik'", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1")),
paste0("Dichotomous variables coded with 0 and 1 found in",
" 'treatment_vars'. Consider recoding to -1 and 1 ",
"to avoid estimation problems."))
})
test_that("weights in matching", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=FALSE,
conf_level=0.95,
times=0.2,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=runif(20, min=10, max=20)),
paste0("Propensity Scores > 1 or < 0 not allowed. ",
"Perhaps you supplied weights on accident?"))
})
test_that("no treatment_model with matching", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=FALSE,
conf_level=0.95,
times=0.2,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_model' must be specified when ",
"using method='matching'."))
})
test_that("no treatment_model with method='aiptw'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"Argument 'treatment_model' must be specified when using method='aiptw'.")
})
treat_mod <- list()
class(treat_mod) <- "glm"
test_that("no outcome_model with method='aiptw'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod),
"Argument 'outcome_model' must be specified when using method='aiptw'.")
})
test_that("no outcome_model with method='direct'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"Argument 'outcome_model' must be specified when using method='direct'.")
})
outcome_model <- list()
class(outcome_model) <- "pecRpart"
test_that("bootstrapping with pecRpart", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
paste0("Bootstrapping is currently not supported with ",
"method='direct' and an 'outcome_model' of ",
"class 'pecRpart'."))
})
class(outcome_model) <- "glm"
test_that("glm with censoring", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
paste0("'outcome_model' of class c('glm', 'ols', ",
"'randomForest') are only allowed when there ",
"is no censoring."), fixed=TRUE)
})
outcome_model <- survival::coxph(Surv(time, event) ~ 1, data=sim_dat)
test_that("coxph not including 'variable'", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
"'variable' has to be included in the cox-regression model.")
})
sim_dat$x7 <- stats::runif(n=nrow(sim_dat))
test_that("strat_cupples with continuous confounder", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_cupples",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x7"),
paste0("Variables in 'adjust_vars' have to be integer, ",
"factor or character variables. Continuous variables ",
"are not allowed when using method='strat_cupples'."))
})
test_that("adjust_vars not in data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x8"),
"'adjust_vars' must specify valid column names in 'data'.")
})
test_that("invalid reference data", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x1",
reference=sim_dat[, c("x2", "x3")]),
paste0("If a 'reference' data.frame is supplied, it ",
"needs to contain all variables listed ",
"in 'adjust_vars'."))
})
sim_dat$.ALL <- 1
test_that("strat_cupples with '.ALL' column", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_cupples",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x1"),
paste0("The column name '.ALL' cannot be used with ",
"method='strat_cupples'. Please rename that ",
"variable and rerun the function."))
})
test_that("'adjust_vars' not specified with strat_ method", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
"Argument 'adjust_vars' needs to be specified when using method='strat_amato'.")
})
sim_dat$.COVARS <- 1
test_that("strat_amato with '.COVARS' column", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="strat_amato",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
adjust_vars="x1"),
paste0("The column name '.COVARS' cannot be used with ",
"method='strat_amato'. Please rename that variable ",
"and rerun the function."))
})
test_that("no 'treatment_model' with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_model' must be defined when ",
"using method='iptw_km'. See documentation."))
})
test_that("no 'treatment_model' with iptw_pseudo / aiptw_pseudo", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Argument 'treatment_model' must be specified when ",
"using method='iptw_pseudo'. See documentation."))
})
treat_mod <- list()
class(treat_mod) <- "gam"
test_that("wrong 'treatment_model' with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod),
paste0("'treatment_model' must be a glm or multinom object, ",
"a formula or a numeric vector of weights. ",
"See documentation."))
})
test_that("wrong 'treatment_model' with aiptw_pseudo", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw_pseudo",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod,
outcome_vars=c("x1", "x2")),
paste0("Argument 'treatment_model' must be one of: 'glm', ",
"'multinom', 'mira' or a numeric vector of ",
"propensity scores."))
})
fake_survfit <- list()
class(fake_survfit) <- "survfit"
mod <- list(fit=fake_survfit)
class(mod) <- "selectCox"
test_that("wrong selectCox mod", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=mod),
paste0("The final 'fit' object in the 'selectCox' model ",
"must be a coxph object, not survfit."))
})
test_that("wrong weights length with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=c(1, 2)),
paste0("If weights are supplied directly in the ",
"'treatment_model' argument, they must be of ",
"length nrow(data)."), fixed=TRUE)
})
test_that("negative weights with iptw_km / iptw_cox", {
expect_error(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=rep(-1, nrow(sim_dat))),
paste0("Weights supplied directly to the 'treatment_model' ",
"argument must be positive."))
})
sim_dat3 <- sim_dat
sim_dat3$group <- 1
test_that("only one treatment", {
expect_error(check_inputs_adjustedsurv(data=sim_dat3,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=group ~ x1),
"There have to be at least two groups in 'variable'.")
})
sim_dat3$group <- c(rep(0, 10), rep(1, 5), rep(2, 5))
sim_dat3$group <- as.factor(sim_dat3$group)
test_that("categorical when not allowed", {
expect_error(check_inputs_adjustedsurv(data=sim_dat3,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE),
paste0("Categorical treatments are currently not supported ",
"for method='matching'."))
})
test_that("conf_int when not allowed", {
expect_warning(check_inputs_adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="matching",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treat_mod),
paste0("Asymptotic or exact variance calculations are ",
"currently not available for method='matching'. ",
"Use bootstrap=TRUE to get bootstrap estimates."),
fixed=TRUE)
})
sim_dat3$group <- as.character(c(rep(0, 10), rep(1, 10)))
test_that("'variable' is not a factor", {
expect_error(check_inputs_adjustedsurv(data=sim_dat3,
variable="group",
ev_time="time",
event="event",
method="emp_lik",
conf_int=FALSE,
conf_level=0.95,
times=NULL,
bootstrap=TRUE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_vars=c("x1", "x3")),
paste0("The column in 'data' specified by 'variable' needs ",
"to be a factor variable."))
})
## multiple imputation stuff
sim_dat_na_time <- sim_dat_na_group <- sim_dat_na_event <- sim_dat_na_x1 <-
sim_dat
sim_dat_na_time$time[3] <- NA
sim_dat_na_group$group[3] <- NA
sim_dat_na_event$event[3] <- NA
sim_dat_na_x1$x1[3] <- NA
mids_na_time <- suppressWarnings(mice::mice(sim_dat_na_time, m=2,
printFlag=FALSE))
mids_na_group <- suppressWarnings(mice::mice(sim_dat_na_group, m=2,
printFlag=FALSE))
mids_na_event <- suppressWarnings(mice::mice(sim_dat_na_event, m=2,
printFlag=FALSE))
mids_na_x1 <- suppressWarnings(mice::mice(sim_dat_na_x1, m=2,
printFlag=FALSE))
outcome_model <- list()
class(outcome_model) <- "coxph"
treatment_model <- list()
class(treatment_model) <- "glm"
test_that("wrong outcome_model with mira", {
expect_error(check_inputs_adjustedsurv(data=mids_na_x1,
variable="group",
ev_time="time",
event="event",
method="direct",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
outcome_model=outcome_model),
paste0("When using multiple imputation, mira objects need ",
"to be supplied to 'outcome_model' instead of ",
"single models. See documentation."))
})
test_that("wrong treatment_model with mira", {
expect_error(check_inputs_adjustedsurv(data=mids_na_x1,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
treatment_model=treatment_model),
paste0("When using multiple imputation, mira objects or a ",
"formula need to be supplied to 'treatment_model' ",
"instead of single models. See documentation."))
})
test_that("wrong censoring_model with mira", {
expect_error(check_inputs_adjustedsurv(data=mids_na_x1,
variable="group",
ev_time="time",
event="event",
method="aiptw",
conf_int=TRUE,
conf_level=0.95,
times=NULL,
bootstrap=FALSE,
n_boot=2,
na.action="na.omit",
clean_data=TRUE,
censoring_model=outcome_model),
paste0("When using multiple imputation, mira objects need to ",
"be supplied to 'censoring_model' instead of single ",
"models. See documentation."))
})
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