knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(dmtools)
For checking the dataset from EDC in clinical trials. Notice, your dataset should have a postfix( _V1 ) or a prefix( V1_ ) in the names of variables. Column names should be unique.
For laboratory check, you need to create the excel table like in the example.
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*column names without prefix or postfix
library(knitr) library(dmtools) library(dplyr) refs <- system.file("labs_refer.xlsx", package = "dmtools") refers <- readxl::read_xlsx(refs) kable(refers, caption = "lab reference ranges")
ID <- c("01", "02", "03") AGE <- c("19", "20", "22") SEX <- c("f", "m", "m") V1_GLUC <- c("5.5", "4.1", "9.7") V1_GLUC_IND <- c("norm", NA, "norm") V2_AST <- c("30", "48", "31") V2_AST_IND <- c("norm", "norm", "norm") df <- data.frame( ID, AGE, SEX, V1_GLUC, V1_GLUC_IND, V2_AST, V2_AST_IND, stringsAsFactors = F ) kable(df, caption = "dataset")
# "norm" and "no" it is an example, necessary variable for the estimate, get from the dataset # parameter is_post has value FALSE because a dataset has a prefix( V1_ ) in the names of variables refs <- system.file("labs_refer.xlsx", package = "dmtools") obj_lab <- lab(refs, ID, AGE, SEX, "norm", "no", is_post = FALSE) obj_lab <- obj_lab %>% check(df) # ok - analysis, which has a correct estimate of the result obj_lab %>% choose_test("ok") # mis - analysis, which has an incorrect estimate of the result obj_lab %>% choose_test("mis") # skip - analysis, which has an empty value of the estimate obj_lab %>% choose_test("skip") # all analyzes obj_lab %>% get_result()
For dates check, you need to create the excel table like in the example.
contains(num_visit)
dates <- system.file("dates.xlsx", package = "dmtools") timeline <- readxl::read_xlsx(dates) kable(timeline, caption = "timeline")
id <- c("01", "02", "03") screen_date_E1 <- c("1991-03-13", "1991-03-07", "1991-03-08") rand_date_E2 <- c("1991-03-15", "1991-03-11", "1991-03-10") ph_date_E3 <- c("1991-03-21", "1991-03-16", "1991-03-16") bio_date_E3 <- c("1991-03-23", "1991-03-16", "1991-03-16") df <- data.frame( id, screen_date_E1, rand_date_E2, ph_date_E3, bio_date_E3, stringsAsFactors = F ) kable(df, caption = "dataset")
# use parameter str_date for search columns with dates, default:"DAT" dates <- system.file("dates.xlsx", package = "dmtools") obj_date <- date(dates, id, dplyr::contains, dplyr::matches) obj_date <- obj_date %>% check(df) # out - dates, which are out of the protocol's timeline obj_date %>% choose_test("out") # uneq - dates, which are unequal obj_date %>% choose_test("uneq") # ok - correct dates obj_date %>% choose_test("ok") # all dates obj_date %>% get_result()
dplyr::contains
- A function, which select necessary visit or event e.g. dplyr::start_with, dplyr::contains. It works like df %>% select(contains("E1"))
. You also can use dplyr::start_with
, works like df %>% select(start_with("V1"))
dplyr::matches
- A function, which select dates from necessary visit e.g. dplyr::matches, dplyr::contains. It works like visit_one %>% select(contains("DAT"))
, default: dplyr::contains()
Function to rename the dataset, using crfs.
rename_dataset("./crfs", "old_name", "new_name", 2)
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