dmtools

Installation

install.packages("dmtools")

# dev-version
devtools::install_github("chachabooms/dmtools")

library(dmtools)

Overview

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.

• date() - create object date, for check dates in the dataset
• lab() - create object lab, for check lab results
• short() - create object short to transform the dataset(different events in one column)
• check() - check objects
• get_result() - get the final result of object
• choose_test() - filter the final result of check()
• check_sites() - check objects of different sites
• test_sites() - filter the final result of check_sites()
• rename_dataset() - rename dataset

Usage

For example, you want to check laboratory values, you need to create the excel table like in the example.

• AGELOW - number, >= number
• AGEHIGH - if none, type Inf, <= number
• SEX - for both sex, use |
• LBTEST - What was the lab test name?
• LBORRES - What was the result of the result lab test?
• LBNDIND - How [did/do] the reported values compare within the [reference/normal/expected] range?
• LBORNRLO - What was the lower limit of the reference range for this lab test, >=
• LBORNRHI - What was the high limit of the reference range for this lab test, <=

# "norm" and "no" it is an example, necessary variable for the estimate, get from the dataset
refs <- system.file("labs_refer.xlsx", package = "dmtools")
obj_lab <- lab(refs, id, age, sex, "norm", "no")
obj_lab <- obj_lab %>% check(df)

# ok - analysis, which has a correct estimate of the result
obj_lab %>% choose_test("ok")
#>   id age sex LBTEST LBTESCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 01  19   f   gluc    gluc   _v1      3.9      5.9     5.5    norm
#> 2 01  19   f    ast     ast   _v2      0.0     39.0      30    norm
#> 3 03  22   m    ast     ast   _v2      0.0     42.0      31    norm
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          5.5         norm
#> 2         30.0         norm
#> 3         31.0         norm

# mis - analysis, which has an incorrect estimate of the result
obj_lab %>% choose_test("mis")
#>   id age sex LBTEST LBTESCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02  20   m    ast     ast   _v2      0.0     42.0      48    norm
#> 2 03  22   m   gluc    gluc   _v1      3.9      5.9     9.7    norm
#>   RES_TYPE_NUM IND_EXPECTED
#> 1         48.0           no
#> 2          9.7           no

# skip - analysis, which has an empty value of the estimate
obj_lab %>% choose_test("skip")
#>   id age sex LBTEST LBTESCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02  20   m   gluc    gluc   _v1      3.9      5.9     4.1    <NA>
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          4.1         <NA>

# dmtools can work with the dataset as strange_df
obj_lab <- obj_lab %>% check(strange_df)

# dmtools can understand the value with a comma like 6,6 
obj_lab %>% choose_test("ok")
#>   id age sex LBTEST LBTESCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 01  19   f   gluc    gluc   _v1      3.9      5.9     5,5    norm
#> 2 03  22   m    ast     ast   _v2      0.0     42.0      31    norm
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          5.5         norm
#> 2         31.0         norm

# Notice, if dmtools can't understand the value of lab_vals e.g. < 5, it puts Inf in the vals_to_dbl
obj_lab %>% choose_test("mis")
#>   id age sex LBTEST LBTESCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 01  19   f    ast     ast   _v2      0.0     39.0     < 5    norm
#> 2 02  20   m    ast     ast   _v2      0.0     42.0      48    norm
#> 3 03  22   m   gluc    gluc   _v1      3.9      5.9     9,7    norm
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          Inf           no
#> 2         48.0           no
#> 3          9.7           no

obj_lab %>% choose_test("skip")
#>   id age sex LBTEST LBTESCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02  20   m   gluc    gluc   _v1      3.9      5.9     4,1    <NA>
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          4.1         <NA>