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dmtools

Installation

install.packages("dmtools")

# dev-version
devtools::install_github("KonstantinRyabov/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 to check dates in the dataset
lab() - create object lab to check lab reference range
short() - create object short to reshape the dataset in a tidy view.
check() - check objects
get_result() - get the final result of object
choose_test() - filter the final result of check()
rename_dataset() - rename the 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? (can be any convenient name for you)
LBORRES* - What was the result of the lab test?
LBNRIND* - 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, <=

*column names without prefix or postfix

lab reference ranges
AGELOW	AGEHIGH	SEX	LBTEST	LBORRES	LBNRIND	LBORNRLO	LBORNRHI
18	45	f\|m	Glucose	GLUC	GLUC_IND	3.9	5.9
18	45	m	Aspartate transaminase	AST	AST_IND	0	42
18	45	f	Aspartate transaminase	AST	AST_IND	0	39

dataset
ID	AGE	SEX	GLUC_V1	GLUC_IND_V1	AST_V2	AST_IND_V2
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                 LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 01  19   f                Glucose     GLUC   _V1      3.9      5.9     5.5
#> 2 01  19   f Aspartate transaminase      AST   _V2      0.0     39.0      30
#> 3 03  22   m Aspartate transaminase      AST   _V2      0.0     42.0      31
#>   LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1    norm          5.5         norm
#> 2    norm         30.0         norm
#> 3    norm         31.0         norm

# mis - analysis, which has an incorrect estimate of the result
obj_lab %>% choose_test("mis")
#>   ID AGE SEX                 LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 02  20   m Aspartate transaminase      AST   _V2      0.0     42.0      48
#> 2 03  22   m                Glucose     GLUC   _V1      3.9      5.9     9.7
#>   LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1    norm         48.0           no
#> 2    norm          9.7           no

# skip - analysis, which has an empty value of the estimate
obj_lab %>% choose_test("skip")
#>   ID AGE SEX  LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02  20   m Glucose     GLUC   _V1      3.9      5.9     4.1    <NA>
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          4.1         norm

strange_dataset
ID	AGE	SEX	V1_GLUC	V1_GLUC_IND	V2_AST	V2_AST_IND
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
# parameter is_post has value FALSE because a dataset has a prefix( V1_ ) in the names of variables
obj_lab <- lab(refs, ID, AGE, SEX, "norm", "no", is_post = F)
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 LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 01  19   f                Glucose     GLUC   V1_      3.9      5.9     5,5
#> 2 03  22   m Aspartate transaminase      AST   V2_      0.0     42.0      31
#>   LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1    norm          5.5         norm
#> 2    norm         31.0         norm

# Notice, if dmtools can't understand the value of lab_vals e.g. < 5, it puts Inf in the RES_TYPE_NUM
obj_lab %>% choose_test("mis")
#>   ID AGE SEX                 LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES
#> 1 01  19   f Aspartate transaminase      AST   V2_      0.0     39.0     < 5
#> 2 02  20   m Aspartate transaminase      AST   V2_      0.0     42.0      48
#> 3 03  22   m                Glucose     GLUC   V1_      3.9      5.9     9,7
#>   LBNRIND RES_TYPE_NUM IND_EXPECTED
#> 1    norm          Inf           no
#> 2    norm         48.0           no
#> 3    norm          9.7           no

obj_lab %>% choose_test("skip")
#>   ID AGE SEX  LBTEST LBTESTCD VISIT LBORNRLO LBORNRHI LBORRES LBNRIND
#> 1 02  20   m Glucose     GLUC   V1_      3.9      5.9     4,1    <NA>
#>   RES_TYPE_NUM IND_EXPECTED
#> 1          4.1         norm

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.