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#' Calculate follow-up time per case until end of follow-up depending on pat_status - tidyverse version
#'
#' @param wide_df dataframe in wide format
#' @param futime_var_new Name of the newly calculated variable for follow-up time. Default is p_futimeyrs.
#' @param fu_end end of follow-up in time format YYYY-MM-DD.
#' @param dattype can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
#' @param check Check newly calculated variable p_status by printing frequency table. Default is TRUE.
#' @param time_unit Unit of follow-up time (can be "days", "weeks", "months", "years"). Default is "years".
#' @param status_var Name of the patient status variable that was previously created. Default is p_status.
#' @param lifedat_var Name of variable containing Date of Death. Will override dattype preset.
#' @param fcdat_var Name of variable containing Date of Primary Cancer diagnosis. Will override dattype preset.
#' @param spcdat_var Name of variable containing Date of SPC diagnosis Will override dattype preset.
#' @return wide_df
#' @export
#' @examples
#' #load sample data
#' data("us_second_cancer")
#'
#' #prep step - make wide data as this is the required format
#' usdata_wide <- us_second_cancer %>%
#' msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
#' time_id_var = "SEQ_NUM", timevar_max = 10)
#'
#' #prep step - calculate p_spc variable
#' usdata_wide <- usdata_wide %>%
#' dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
#' !is.na(t_site_icd.2) ~ "SPC developed",
#' TRUE ~ NA_character_)) %>%
#' dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
#' TRUE ~ 0))
#'
#' #prep step - create patient status variable
#' usdata_wide <- usdata_wide %>%
#' msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
#' status_var = "p_status", life_var = "p_alive.1",
#' birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#'
#' #now we can run the function
#' msSPChelpR::calc_futime(usdata_wide,
#' futime_var_new = "p_futimeyrs",
#' fu_end = "2017-12-31",
#' dattype = "seer",
#' time_unit = "years",
#' status_var = "p_status",
#' lifedat_var = "datedeath.1",
#' fcdat_var = "t_datediag.1",
#' spcdat_var = "t_datediag.2")
#'
calc_futime <- function(wide_df,
futime_var_new = "p_futimeyrs",
fu_end,
dattype = NULL,
check = TRUE,
time_unit = "years",
status_var = "p_status",
lifedat_var = NULL,
fcdat_var = NULL,
spcdat_var = NULL){
#---- Checks start
#check if wide_df is data.frame
if(!is.data.frame(wide_df)){
rlang::inform("You are using a dplyr based function. Data has been converted to a data.frame to let this function run more efficiently.")
wide_df <- as.data.frame(wide_df)
}
#fetch variable names provided in function call
futime_var_new <- rlang::ensym(futime_var_new)
status_var <- rlang::ensym(status_var)
time_unit <- rlang::enquo(time_unit)
if(!is.null(dattype)){
#setting default var names and values for SEER data
if (dattype == "seer"){
if(is.null(lifedat_var)){
lifedat_var <- rlang::quo("p_datedeath.1")
} else{
lifedat_var <- rlang::enquo(lifedat_var)
}
if(is.null(fcdat_var)){
fcdat_var <- rlang::quo("t_datediag.1")
} else{
fcdat_var <- rlang::enquo(fcdat_var)
}
if(is.null(spcdat_var)){
spcdat_var <- rlang::quo("t_datediag.2")
} else{
spcdat_var <- rlang::enquo(spcdat_var)
}
}
#setting default var names and values for ZfKD data
if (dattype == "zfkd"){
if(is.null(lifedat_var)){
lifedat_var <- rlang::quo("SDIMP.1")
} else{
lifedat_var <- rlang::enquo(lifedat_var)
}
if(is.null(fcdat_var)){
fcdat_var <- rlang::quo("DDIMP.1")
} else{
fcdat_var <- rlang::enquo(fcdat_var)
}
if(is.null(spcdat_var)){
spcdat_var <- rlang::quo("DDIMP.2")
} else{
spcdat_var <- rlang::enquo(spcdat_var)
}
}
} else{
# ensym if no dattype is given
lifedat_var <- rlang::enquo(lifedat_var)
fcdat_var <- rlang::enquo(fcdat_var)
spcdat_var <- rlang::enquo(spcdat_var)
}
#---- Checks start
#check whether p_dodmin information can be used
if("p_dobmin" %in% names(wide_df)) {
dmin <- TRUE
} else{
dmin <- FALSE
}
#check whether all required variables are defined and present in dataset
defined_vars <- c(rlang::as_name(status_var), rlang::as_name(lifedat_var), rlang::as_name(fcdat_var), rlang::as_name(spcdat_var))
not_found <- defined_vars[!(defined_vars %in% colnames(wide_df))]
if(length(not_found) > 0) {
rlang::abort(paste0("The following variables defined are not found in the provided dataframe: ", not_found, ". Please run pat_status function beforehand."))
}
#get used FU date from function parameter and from label of status_var
fu_end_param <- as.Date(rlang::as_name(fu_end), date.format = "%y-%m-%d")
fu_end_status <- attr(wide_df[[rlang::as_name(status_var)]], "label", exact = T) %>% stringr::str_sub(-10) %>% as.Date(., date.format = "%y-%m-%d")
#check whether date was provided in correct format
if(!lubridate::is.Date(fu_end_param)) {
rlang::abort("You have not provided a correct Follow-up date in the format YYYY-MM-DD")
}
#check whether time_unit is provided in correct format
if((rlang::as_name(time_unit) %in% c("years", "months", "days")) == FALSE) {
rlang::abort("You can only use 'years', 'months' or 'days' as time_unit")
}
#check whether FU date provided was the same as for pat_status function
if(fu_end_status != fu_end_param) {
rlang::abort(paste0("You are using a different date of FU for calculating FU time (this function) than you have for calculating patient status (pat_status function).",
"\nEnd of Follow-up provide: ", fu_end_param,
"\nEnd of FU used in p_status is: ", fu_end_status))
}
#check whether FU date provided might be too late
if(fu_end_param > max(wide_df[[rlang::as_name(fcdat_var)]], na.rm = TRUE) & fu_end_param > max(wide_df[[rlang::as_name(spcdat_var)]], na.rm = TRUE)) {
rlang::abort(paste0("You have provided an end of Follow-up date that might be out of range of the collected data.",
"Thus events such as SPCs or deaths might not have been recorded and FU-time is overestimated.",
"\nEnd of Follow-up provided: ", fu_end_param,
"\nLatest recorded First Cancer: ", max(wide_df[[rlang::as_name(fcdat_var)]], na.rm = TRUE),
"\nLatest recorded Second Cancer: ", max(wide_df[[rlang::as_name(spcdat_var)]], na.rm = TRUE)
))
}
#check if new and old futime_var are the same --> message that id was overwritten
if(rlang::as_name(futime_var_new) %in% names(wide_df)){
rlang::warn(paste0(rlang::as_name(futime_var_new)," is already present in dataset. Variable has been overwritten with new values."))
}
#---- Calculate
#revert status_var to numeric if previously labelled
if(is.factor(wide_df[[rlang::as_name(status_var)]])){
changed_status_var <- TRUE
wide_df <- wide_df %>%
dplyr::mutate(
#copy old status var
status_var_orig = .data[[!!status_var]],
#make status_var numeric
!!status_var := sjlabelled::as_numeric(.data[[!!status_var]],
keep.labels=FALSE, use.labels = TRUE))
} else{
changed_status_var <- FALSE
}
#new variable label
futime_var_new_label <- paste0("Follow-up time of patient from diagnosis of first cancer until SPC or Death or End of FU [years]. End of FU is ", fu_end_param)
#calculate new follow_up time p_futimeyrs
wide_df <- wide_df %>%
dplyr::mutate(!!futime_var_new := dplyr::case_when(
#patient alive, after FC
.data[[!!status_var]] == 1 ~ lubridate::time_length(difftime(fu_end_param, .data[[!!fcdat_var]]), !!time_unit),
#patient alive, after SPC
.data[[!!status_var]] == 2 ~ lubridate::time_length(difftime(.data[[!!spcdat_var]], .data[[!!fcdat_var]]), !!time_unit),
#patient dead, after FC
.data[[!!status_var]] == 3 ~ lubridate::time_length(difftime(.data[[!!lifedat_var]], .data[[!!fcdat_var]]), !!time_unit),
#patient dead, after SPC
.data[[!!status_var]] == 4 ~ lubridate::time_length(difftime(.data[[!!spcdat_var]], .data[[!!fcdat_var]]), !!time_unit),
# NA 97 - not born
.data[[!!status_var]] == 97 ~ NA_real_,
# NA 98 - no FC
.data[[!!status_var]] == 98 ~ NA_real_,
# NA 99 - DOD missing
.data[[!!status_var]] == 99 ~ NA_real_,
TRUE ~NA_real_)) %>%
#label new variable
sjlabelled::var_labels(!!futime_var_new := !!futime_var_new_label)
#if status_var was changed from factor to numeric, revert
if(changed_status_var == TRUE){
wide_df <- wide_df %>%
dplyr::mutate(
#replace temporary lifedat_var values with values from old lifedat_var
!!status_var := .data$status_var_orig
) %>%
#remove status_var_orig
dplyr::select(-status_var_orig)
}
#---- Checks end
#conduct check on new variable
if(check == TRUE){
check_tab <- wide_df %>%
dplyr::group_by(.data[[!!status_var]])%>%
dplyr::summarise(mean_futime = mean(.data[[!!futime_var_new]], na.rm = TRUE),
min_futime = min(.data[[!!futime_var_new]], na.rm = TRUE),
max_futime = max(.data[[!!futime_var_new]], na.rm = TRUE),
median_futime = stats::median(.data[[!!futime_var_new]], na.rm = TRUE))
print(check_tab)
}
return(wide_df)
}
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