Abstract

Metadata is now an integral part of the clinical trial process. CDISC provides the framework for this metadata and Define.xml provides a way to share the final product. There is, however, no standard way to efficiently collect, manage and collaborate in the process of creating metadata.

Clinical Metadata provides an end-to-end metadata management solution, including CDASH, SDTM, ADaM and output metadata. It generates SAS code fragments and submission deliverables, automating repetitive programming tasks, accelerating and simplifying the development process. Clinical Metadata is built as a web-based single page application, using the latest technologies and practices, and is securely hosted in the Amazon Web Services cloud. Its visualisation tools present the user with a high-level metadata overview and allows them to drill down into individual metadata fragments. In-built standards management, traceability and impact analysis tools help users understand the relationships between each metadata element as well as encourage Agile and collaborative ways of working.

PhUse Conference

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Join them to learn how Clinical Metadata can help the pharmaceutical industry manage its clinical trial metadata and data standards. The tool also helps users obtain CDISC compliance and accelerates the reporting process by generating SAS code and submission deliverables.

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PROC COMPARE is often seen as the gold standard for validating datasets within clinical trials. However the output it produces can often be extensive and manually interpreting this can be timely and error prone. This paper discusses an automated approach for examining the results of PROC COMPARE, by interpreting the return code that the procedure creates. This approach allows the user to quickly create single page summaries displaying the comparison results of multiple (i.e. more than 30) comparisons in an easy to interpret one observation per comparison format.

We have previously seen several papers that have explained how to utilise PROC COMPARE return codes, however what differentiates this paper is its clever use of the SAS bAND function to interpret the return codes.

The paper was presented at the SAS  Global Forum, 2012. Download the paper here.

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The paper examines problems which can result from processing and storing large SDTM  datasets and how to resolve them. Although this paper was written in 2010, we feel that the issues and techniques discussed in this paper are particularly important in the light of the guidance provided by CDISC  (SDTM Implementation Guide 3.2 – section 4.1.2.9) that:

Very large transport files have become an issue for FDA to process. One of the main contributors to the large file sizes has been sponsors using the maximum length of 200 for character variables. To help rectify this situation:
• The maximum SAS Version 5 character variable length of 200 characters should not be used unless necessary.

The paper presents several methods for handling large datasets more efficiently and suggests ways in which the size of datasets can be reduced. We particularly liked the tables which show real time, CPU time and memory costs associated with different approaches, which provide a tangible illustration of the benefits of efficiency savings.

The paper was presented at the SESUG conference, Savannah, GA, 2010. Download the paper here.

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Sometimes it is necessary to force additional observations into your datasets, inserting new observations into a dataset with the Data Step usually involves either creating ta new dataset containing the new observations using a  DATALINES statement and then appending this dataset with the original, or alternatively  using multiple OUTPUT statements  within a Data Step.

 DATALINES method

DATA class_in;
  ATTRIB name LENGTH = $8 sex LENGTH = $1;
  INFILE DATALINES;
  INPUT name sex age height weight;
  DATALINES;
Andy M 30 190 100
Ben M 60 180 110
Paul M 26 170 120
;
RUN;
PROC APPEND BASE = class DATA = class_in;
RUN;

Multiple OUTPUT statement method

DATA class;
  SET class;
  OUTPUT;
  name = "Andy";
  sex  = "M";
  age = 30;
  height = 190;
  weight = 100;
  OUTPUT; 
  name = "Ben";
  sex  = "M";
  age = 60;
  height = 180;
  weight = 110;
  OUTPUT; 
  name = "Paul";
  sex  = "M";
  age = 26 ;
  height = 170 ;
  weight = 120;
  OUTPUT; 
RUN;

PROC SQL INSERT INTO method

The DATALINES method is not ideal as it involves creating an additional dataset and ensuring that the variable lengths in the new dataset  are consistent with the length in the original dataset, otherwise truncation can occur. Whereas the Multiple OUTPUT statement method can be time consuming and error prone because it required one assignment statement to be manually typed out for each variable in each observation. The PROC SQL INSERT INTO approach provides a concise alternative to these methods. This approach inserts lines using the VALUES statement in combination with a comma separated list, as shown below:

PROC SQL NOPRINT ; 
  INSERT INTO class
  VALUES ("Andy", "M", 30,190,100)
  VALUES ("Ben", "M", 60,180,110)
  VALUES ("PAUL", "M", 26,170,120); 
QUIT;

If using this approach, care should be taken to ensure that the order of the values in the comma separated list is consistent with the order of the associated variable in the dataset. To minimise the possibility of making a mistake in the order, or if you want to populate certain columns only, the columns which you wish to update, and the order of the variables in your comma separated list can be specified in brackets after the name of the dataset, as shown below:

PROC SQL; 
  INSERT INTO class (name,weight)
  VALUES ("Andy",100)
  VALUES ("Ben",110)
  VALUES ("PAUL",.); 
QUIT;

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“ERROR: Variable X not found” 

What causes the message?

This message occurs when a PROC Step  attempts to use a variable which is not present in the input dataset. This usually happens if, the variable was dropped or not kept in a preceding step, or if the code to create it has been accidentally omitted, or code has been copied from one place to another without making the necessary alterations.

What happens when this occurs?

When SAS cannot find a variable within a PROC Step, SAS will:

• outputs a message to a log stating the name of the variable(s) it cannot find, e.g. “ERROR: Variable X not found” implying that the variable X  is not present in the input dataset.
• stop the execution of the current PROC Step and not produce any output.

You will notice that SAS’ behavior here is different to uninitialized variables in DATA Steps as in that instance it continues to create the output dataset.

How to resolve it?

To resolve this issue, ensure that the not found variable is available in the input dataset to the PROC Step, or udpate the PROC Step so that it does not require the not found variable in order to execute.

Example

The following code attempts to sort the SASHELP.CLASS dataset by the variables AGE, BSA and BMI variables.

PROC SORT DATA = sashelp.class OUT=class;
  BY age bsa bmi;
RUN;

This code results in the following messages being printed to the log.:

 
 43         PROC SORT DATA = sashelp.class OUT=class;
 44           BY age bsa bmi;
 ERROR: Variable BSA not found.
 ERROR: Variable BMI not found.
 45         RUN;

 NOTE: The SAS System stopped processing this step because of errors.
 WARNING: The data set WORK.CLASS may be incomplete.  When this step was stopped there were 0 observations and 0 variables.

The lines:

ERROR: Variable BSA not found.
ERROR: Variable BMI not found.

Tell us that the BSA and BMI variables respectively were not present in the dataset SASHELP.CLASS.

While the line:

 
WARNING: The data set WORK.CLASS may be incomplete.  When this step was stopped there were 0 observations and 0 variables.

Tells us that as a result of the error, the output dataset was not created.
To resolve this issue, either the BSA and BMI  variables should be present in the input dataset or else they should be removed from the PROC Step.

For example, here we use a DATA Step to first derive the BSA variable and then remove the BMI variable from the PROC SORT:

DATA class;
  SET sashelp.class;
  bsa = ((height *weight )/ 3131 )**1/2;
RUN;

PROC SORT DATA = class OUT=class2;
  BY age bsa ;
RUN;

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