clinicalfd (Clinical Fake Data)

The goal of clinicalfd is to create Fake SDTM/ADaM data for generating TFL.This data is available at (https://github.com/phuse-org/phuse-scripts) for free to download for public use see the licence here (https://github.com/phuse-org/phuse-scripts/blob/master/LICENSE.md)

Installation

You can install the released version of clinicalfd (clinical fake data) from [Github] (https://github.com/sas2r/clinical_fd)

General Instructions:

Make sure you have lateset version of R-language , the way to check is type in
session() in R console .

If it <= 0.4.7 then the Package may not be loaded, you need to have >= 0.4.7.

To update the R-version

If R studio is on your Personnel drive then do Install.packages (“rlang”) , then library (rlang) , check sessionInfo ()

NOTE: If you are using Rstudio on cloud this would not work , On R-cloud simply go to right most corner of the window and change the R version to either 4.0 or 4.1.

Once you have updated the R-languge then

NOTE: devtools and clinical_fd would install couple of standard packages and would take some time

install.packages(“devtools”) wait for the dev tools to be installed then call the library(devtools). Once the devtools is installed then you can call install_github(“sas2r/clinical_fd”) to install the package clinical_fd (all lower case , with the underscore in it ) then call library(clincalfd) (without the underscore)

During the process of Installation ( May take around 10 min depending on the System) you will see couple of packages either been downloaded or updated.

NOTE: Make sure you type in exactly as shown below without # for install packages part

#install.packages("devtools") 

library(devtools)
#> Loading required package: usethis

#install_github("sas2r/clinical_fd")

library(clinicalfd)
## basic example code

(Please see dataguide in man folder for more information ) Where to Find Key Data

Demographics and Populations

The ADSL (Subject Level Analysis Data) dataset contains all subject-level variables for demographics, subject characteristics, and population flags. Safety Key safety data are found in the datasets ADAE (Adverse Events Analysis Data), ADLBC (Laboratory Results Chemistry Analysis Data), ADLBH (Laboratory Results Hematology Analysis Data), ADLBHY (Laboratory Results Hy’s Rule Analysis Data), and ADVS (Vital Signs Analysis Dataset). In addition, ADTTE is specifically for safety analyses of the time to the first dermatologic AE. Dermatologic AEs are considered an adverse event of special interest.

Efficacy Study CDISCPilot01 has two primary endpoints, the Alzheimer’s Disease Assessment Scale - Cognitive Subscale, total of 11 items [ADAS-Cog (11)] at Week 24 and the Video-referenced Clinician’s Interview-based Impression of Change (CIBIC+) at Week 24. All ADAS-Cog data, including the first primary endpoint, can be found in the dataset ADQSADAS. All CIBIC+ data, including the second primary endpoint, can be found in the dataset ADQSCIBC. Mean Revised Neuropsychiatric Inventory (NPI-X) data are considered secondary. These data can be found in the dataset ADQSNPIX.

List of ADaM/SDTM Data sets available in ‘clinicalfd’ package are:

ADaM: adadas
adae
adcibc
adlbc
adlbcpv
adlbh
adlbhpv
adlbhy
adnpix
adsl
adtte
advs

SDTM: ae
cm
dm
ds
ex
lbch
lbhe
mh
qsco
qsda
qsgi
qshi
relrec
sc
se
suppae
suppdm
suppds
supplbch
supplbur
sv
ta
te
ti
ts
tv
vs

Example

This is a basic example which shows you how to solve a common problem:

#from SDTM
summary(dplyr::select(dm, age, sex , race, ethnic))
#>       age        sex                                   race    
#>  Min.   :50.00   F:179   AMERICAN INDIAN OR ALASKA NATIVE:  2  
#>  1st Qu.:70.25   M:127   ASIAN                           :  2  
#>  Median :77.00           BLACK OR AFRICAN AMERICAN       : 29  
#>  Mean   :75.09           WHITE                           :273  
#>  3rd Qu.:81.00                                                 
#>  Max.   :89.00                                                 
#>                     ethnic   
#>  HISPANIC OR LATINO    : 17  
#>  NOT HISPANIC OR LATINO:289  
#>                              
#>                              
#>                              
#> 
#from ADaM
summary(dplyr::select(adsl, age, sex , race, ethnic))
#>       age        sex                                   race    
#>  Min.   :51.00   F:143   AMERICAN INDIAN OR ALASKA NATIVE:  1  
#>  1st Qu.:70.00   M:111   BLACK OR AFRICAN AMERICAN       : 23  
#>  Median :77.00           WHITE                           :230  
#>  Mean   :75.09                                                 
#>  3rd Qu.:81.00                                                 
#>  Max.   :89.00                                                 
#>                     ethnic   
#>  HISPANIC OR LATINO    : 12  
#>  NOT HISPANIC OR LATINO:242  
#>                              
#>                              
#>                              
#> 

You can also embed plots, for example:

plot(dplyr::select(dm , sex, age) )

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date.

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub!