RWD Express — a SAS package designed to accelerate your Real World Data journey. It helps you prepare your SAS environment for efficient RWD processing. Provides practical tools to handle, clean, and organize large, complex datasets.

%index_single_key is a macro that creates an index for datasets in a library all at once. The index key should be a single variable, such as patientid, that exists in all target datasets.

• inlib : Library reference containing the original datasets.
• outlib : Library reference where output datasets with index data to be stored
• indexkey: index key variable for all datasets.e.g: patientid
• in_ds(optional) : datasets to be extracted. e.g: AE CM DM
• ex_ds(optional) : datasets to be excluded. e.g: XX XY XS
• ds_select_cond(optional) : Condition to extract datasets. Note that condition to extract the datasets from output of proc contents. e.g: index(memname,"D_")

create single index for variable patid on all datasets in rwd library and store them in rwdx

%index_single_key(inlib=rwd, outlib=rwdx, indexkey=patid);

You can specify the target datasets using the optional parameter in_ds or ex_ds.

%index_single_key(inlib=rwd, outlib=rwdx, indexkey=patid, in_ds = DISEASE DRUG);
%index_single_key(inlib=rwd, outlib=rwdx, indexkey=patid, ex_ds = MASTER_DRUG);

You can specify the target datasets using condition using the optional parameterds_select_cond.

%index_single_key(inlib=rwd, outlib=rwdx, indexkey=patid, ds_select_cond = index(memname,"MASTER_")=0);

This macro creates a single index key to all datasets, supporting streamlined patient data extraction. Remember, only one key variable should be specified, typically the patient ID.

%small_world is a macro to extract data with subjects in subject_level_ds using WHERE expression. Optionally, the number of subjects can be specify to extract smaller number of subjects from large size datasets.

• inlib : libname where original datasets are located. dataset with index is preferable
• outlib: libname output datasets extracted with specific subjects to be stored
• subject_level_ds: subject level dataset e.g: work.DM, inds.ADSL
• subject_id_var: variable with subject / patient id. e.g: usubjid patientid
• no_sub : Number of subjects you would like to extract. If this parameter is blank, all subjects in subject_level_ds is extracted
• ds_select_cond(optional): Condition to extract datasets. Note that condition to extract the datasets from output of proc contents. e.g: index(memname,"D_")

Extract subjects in PATIENT from datasets in library rwd and store them in library out

%small_world(inlib=rwd, outlib=out, 
	     subject_level_ds=PATIENT, 
	     subject_id_var=patid);

Use this macro with %index_single_key to shorten execution time for extraction. Extract first 1000 subjects in rwdx.PATIENT from datasets in rwdx then store them in swd.

%index_single_key(inlib=rwd, outlib=rwdx, indexkey=patid);
%small_world(inlib=rwdx, outlib=swd, 
	     subject_level_ds=rwdx.PATIENT, 
	     subject_id_var=patid, no_sub=1000);

If the original datasets have index of subject_id_var, the macro can extract dataset extremely fast.

%split_world is a macro which allow user to split the large dataset in to small piecies. so that user can process one by one.

• inlib : libname where target dataset are located.
• indata : target dataset e.g: act
• outlib(default to work) : libname split datasets (e.g: act001,act002...) to be stored
• nperBlock : the number of records split dataset will have in one dataset
• blockstart(default to 1) : the start of block. It starts from the first block if not specified.
• blockend : the end of block, if not specified, it ends with the last block if not specified.

Split disease dataset in rwd into small datasets with 100000 observations.

%split_world(inlib=rwd, indata=disease, nperBlock=100000);

Split disease dataset in rwd into small datasets with 100000 observations. You can specify which datasets you want and store them in the library split.

* from 3rd dataset to 5th datasets;
%split_world(inlib=rwd, indata=d02_actdata, outlib=split, nperBlock=100000, blockstart=3, blockend=5);
* from 6th to the end;
%split_world(inlib=rwd, indata=d02_actdata, outlib=split, nperBlock=100000, blockstart=6);

0.1.1(22Sep2025) : Bug fixed for %small_world.
0.1.0(05Sep2025) : Two new macros(%small_world, %split_world) released.
0.0.1(16Jun2025) : Initial version

The package is built on top of SAS Packages Framework(SPF) developed by Bartosz Jablonski.

For more information about the framework, see SAS Packages Framework.

You can also find more SAS Packages (SASPacs) in the SAS Packages Archive(SASPAC).

First, create a directory for your packages and assign a packages fileref to it.

filename packages "\path\to\your\packages";

Secondly, enable the SAS Packages Framework. (If you don't have SAS Packages Framework installed, follow the instruction in SPF documentation to install SAS Packages Framework.)

%include packages(SPFinit.sas)

Install SAS package you want to use with the SPF's %installPackage() macro.

• For packages located in SAS Packages Archive(SASPAC) run:

  %installPackage(packageName)

• For packages located in PharmaForest run:

  %installPackage(packageName, mirror=PharmaForest)

• For packages located at some network location run:

  %installPackage(packageName, sourcePath=https://some/internet/location/for/packages)

  (e.g. %installPackage(ABC, sourcePath=https://github.com/SomeRepo/ABC/raw/main/))

Load SAS package you want to use with the SPF's %loadPackage() macro.

%loadPackage(packageName)