# How to convert original results to standard results using conversion factors

This post is part of 'SDTM | General' series

While working on the creation of SDTM datasets, we often find that the results of tests are collected in a different unit than the standard unit.

In such cases, to populate the standard results we need the conversion factors, and these are often provided in the form of an excel file or a sas dataset.

We make use of the conversion factors and programmatically convert collected results into standard results.

Below is an example of vital signs data in which some of the results are collected in non-standard units while some others are collected in standard units.

 USUBJID VSTESTCD VSORRESU VSORRES VSSTRESU 1001 HEIGHT m 1.68 cm 1001 WEIGHT LB 138.9 kg 1002 HEIGHT cm 170 cm 1002 WEIGHT kg 60.5 kg

Description of the data

• VSTESTCD variable has the test information - we have HEIGHT and WEIGHT collected in this example
• VSORRESU variable has the original units in which the results are collected
• VSORRES variable has the actual collected result associated with the test
• VSSTRESU variable has the standard unit in which the results are to be presented as per the SDTM standard expectation

And, the conversion factors are provided in the form of a sas dataset with the structure as shown below.

 TESTCD ORRESU Conversion_multiplier HEIGHT m 100 HEIGHT cm 1 WEIGHT LB 0.4536 WEIGHT kg 1

Description of the data

• TESTCD variable has the test information
• ORRESU variable has the original result units
• CONVERSION_MULTIPLIER variable has the conversion factor that must be multiplied to the original result to obtain the results in standard unit

To achieve this, we need to fetch the conversion factors associated with each test and unit to get a structure like this.

 USUBJID VSTESTCD VSORRESU VSORRES VSSTRESU Conversion_multiplier 1002 HEIGHT cm 170 cm 1 1001 HEIGHT m 1.68 cm 100 1002 WEIGHT kg 60.5 kg 1 1001 WEIGHT LB 138.9 kg 0.454

Below is an example SAS code that we can use to achieve the result.

data rvs;

infile cards truncover;

input USUBJID\$ VSTESTCD\$    VSORRESU\$    VSORRES\$ VSSTRESU\$;

cards;

1001 HEIGHT   m   1.68 cm

1001 WEIGHT   LB  138.9    kg

1002 HEIGHT   cm  170 cm

1002 WEIGHT   kg  60.5 kg

;

run;

data cf;

infile cards truncover;

input TESTCD\$ ORRESU\$  Conversion_multiplier;

cards;

HEIGHT   m   100

HEIGHT   cm  1

WEIGHT   LB  0.4536

WEIGHT   kg  1

;

run;

proc sql;

create table rvs01 as

select a.*,b.conversion_multiplier

from rvs as a

left join

cf as b

on upcase(a.vstestcd)=upcase(b.testcd) and upcase(a.vsorresu)=upcase(b.orresu)

order by usubjid,vstestcd

;

quit;

data rvs02;

set rvs01;

vsorresn=input(vsorres,??best.);

if nmiss(vsorresn,conversion_multiplier)=0 then vsstresn=vsorresn*conversion_multiplier;

drop conversion_multiplier vsorresn;

run;

And, the final output (VSSTRESN) looks like below.

 USUBJID VSTESTCD VSORRESU VSORRES VSSTRESU vsstresn 1001 HEIGHT m 1.68 cm 168 1001 WEIGHT LB 138.9 kg 63.00504 1002 HEIGHT cm 170 cm 170 1002 WEIGHT kg 60.5 kg 60.5

Points to note:

• We may want to round the result to appropriate number of decimals based on the precision required for each test

Post categories

## List of other posts

#### SDTM

##### General
How to convert original results to standard results using conversion factors

#### SASnR

##### Introduction
What is R?
What is an R package?
What is tidyverse?
What are the core packages of tidyverse?
What is haven package of tidyverse?
How to install tidyverse?
How to load core tidyverse packages into the R session?

Import/Read SAS dataset into R

##### Creating sample data
How to create some sample data in SAS and R tidyverse

##### Subset variables (columns)
How to select only required variables/columns in SAS and R tidyverse?
How to drop unwanted variables/columns in SAS and R tidyverse?

##### Subset observations (rows)
How to select/subset required rows in SAS and R tidyverse

##### Sort (order) observations
Sort/order observations based on the values in a single variable in SAS and R tidyverse

##### Transpose/Restructure data
Restructure/transpose long data to wide data
Restructure/transpose wide data to long data

##### Obtain frequencies
Obtain frequencies/counts based on one variable - one-way frequencies in SAS and R tidyverse
Obtain frequencies/counts based on two variables - two-way frequencies in SAS and R tidyver

##### Descriptive statistics
Descriptive statistics for a numeric variable using SAS and R tidyverse