dmtools

Installation

devtools::install_github("chachabooms/dmtools")
library(dmtools)

Overview

For checking the dataset from EDC in clinical trials. Notice, your dataset should have a postfix( _post ) or a prefix( pre_ ) 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
wbc() - create object wbc, for check WBCs count: (all * relative) / 100 = absolute
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.

age_min - whole number, >= number
age_max - if none, type Inf, <= number
sex - for both sex, use |
human_name - friendly name for analysis
name_lab_vals - analysis from the dataset, without postfix or prefix
name_is_norm - estimate from the dataset, without postfix or prefix
lab_vals_min - lower limit of normal, >=
lab_vals_max - upper limit of normal, <=

lab reference ranges
age_min	age_max	sex	human_name	name_lab_vals	name_is_norm	lab_vals_min	lab_vals_max
18	45	f\|m	gluc	gluc	gluc_res	3.9	5.9
18	45	m	ast	ast	ast_res	0	42
18	45	f	ast	ast	ast_res	0	39

dataset
id	age	sex	gluc_post	gluc_res_post	ast_post	ast_res_post
01	19	f	5.5	norm	30	norm
02	20	m	4.1	NA	48	norm
03	22	m	9.7	norm	31	norm

# "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 human_lab  name_lab      refs lab_vals is_norm vals_to_dbl
#> 1 01  19   f      gluc gluc_post 3.9 - 5.9      5.5    norm         5.5
#> 2 01  19   f       ast  ast_post    0 - 39       30    norm        30.0
#> 3 03  22   m       ast  ast_post    0 - 42       31    norm        31.0
#>   auto_norm
#> 1      norm
#> 2      norm
#> 3      norm

# mis - analysis, which has an incorrect estimate of the result
obj_lab %>% choose_test("mis")
#>   id age sex human_lab  name_lab      refs lab_vals is_norm vals_to_dbl
#> 1 02  20   m       ast  ast_post    0 - 42       48    norm        48.0
#> 2 03  22   m      gluc gluc_post 3.9 - 5.9      9.7    norm         9.7
#>   auto_norm
#> 1        no
#> 2        no

# skip - analysis, which has an empty value of the estimate
obj_lab %>% choose_test("skip")
#>   id age sex human_lab  name_lab      refs lab_vals is_norm vals_to_dbl
#> 1 02  20   m      gluc gluc_post 3.9 - 5.9      4.1    <NA>         4.1
#>   auto_norm
#> 1      <NA>

strange_dataset
id	age	sex	gluc_post	gluc_res_post	ast_post	ast_res_post
01	19	f	5,5	norm	< 5	norm
02	20	m	4,1	NA	48	norm
03	22	m	9,7	norm	31	norm

# 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 human_lab  name_lab      refs lab_vals is_norm vals_to_dbl
#> 1 01  19   f      gluc gluc_post 3.9 - 5.9      5,5    norm         5.5
#> 2 03  22   m       ast  ast_post    0 - 42       31    norm        31.0
#>   auto_norm
#> 1      norm
#> 2      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 human_lab  name_lab      refs lab_vals is_norm vals_to_dbl
#> 1 01  19   f       ast  ast_post    0 - 39      < 5    norm         Inf
#> 2 02  20   m       ast  ast_post    0 - 42       48    norm        48.0
#> 3 03  22   m      gluc gluc_post 3.9 - 5.9      9,7    norm         9.7
#>   auto_norm
#> 1        no
#> 2        no
#> 3        no

obj_lab %>% choose_test("skip")
#>   id age sex human_lab  name_lab      refs lab_vals is_norm vals_to_dbl
#> 1 02  20   m      gluc gluc_post 3.9 - 5.9      4,1    <NA>         4.1
#>   auto_norm
#> 1      <NA>