Exploring the Analysis Data Model – ADaM Datasets

Today, I stumbled upon a blog which is interesting and resourceful.  I liked the article so much so want to share with all my friends here.

Here is the direct link for the post to download or to review:
Actual Article:

The Analysis Data Model (ADaM) is a standard released by the Clinical Data Interchange Standards Consortium (CDISC) and has quickly become widely used in the submission of clinical trial information. ADaM has very close ties to another of CDISCs released standards, Study Data Tabulation Model (SDTM).

The main difference between these two CDISC standards is the way in which the data is displayed. SDTM provides a standard for the creation and mapping of collected data from Raw sources, where as ADAM provides a standard for the creation of analysis-ready data, often using SDTM data as the source.

The purpose of the analysis-ready ADaM data is to provide the programmer with a means to create tables, listings and figures with minimal time and effort whilst ensuring a clear level of traceability in the derived values. This is a key factor of ADaM data as there is a need for a clear and unambiguous flow from the study tabulation data to the analysis data which supports the statistical analyses performed in a clinical study.

CDISC state the following key principles for Analysis Datasets:

• facilitate clear and unambiguous communication and provide a level of traceability 
• be useable by currently available tools 
• be linked to machine-readable metadata 
• be analysis-ready

To perform statistical analysis on a study, data maybe required from many domains, such as labs, adverse events, demographics and subject characteristics. Bringing this data into ADaM datasets and performing any complex derivations required for display endpoints means that no further data manipulation is required to produce statistical outputs.

When creating the ADaM datasets the requirements of the analyses must be taken into consideration. This will ensure the desired numbers of datasets are produced – at the very least; a subject level dataset is required. Some of the data will be duplicated between domains, for example Age and Gender data. This is acceptable as this will aid the output creation or data review.

The naming convention for the datasets will follow “ADxxxx”, where the “xxxx” part will be sponsor-defined - AE for adverse events, LB for Laboratory results for example. The subject-level dataset, which will be discussed later, will be named “ADSL”. For the ADaM variables, the naming conventions should follow the standardized variable names defined in the ADaM Implementation Guide (ADaMIG). Any variables from the SDTM which are used directly in the ADaM dataset should keep the same variable name to avoid confusion. Sponsor-defined variable names will be given to any other analysis variables. Following these conventions will provide clarity for the reviewer.

As previously mentioned, a key requirement for ADaM data is a subject-level analysis dataset. This dataset and its documentation are always required – even if no other data is submitted. The subject-level dataset, or “ADSL” as it is named within ADaM conventions, contains a record for each subject with variables which display key information for subject disposition, demographic, and baseline characteristics. 

Other variables within ADSL will contain planned or actual treatment group information as well key dates and times of the subjects study participation on the study. Not all variables within ADSL may be used directly for analysis but could be used in conjunction with other datasets for display or grouping purposes or possibly included simply as variables of interest for review.

To conclude, the CDISC summary of ADSL is as follows: “The critical variables in ADSL will include those that are either descriptive, known to affect the subject’s response to drug (in terms of either efficacy or safety), used as strata for randomization, or identify the subject or event as belonging to specific subgroups (e.g. population flags). For example, subjects may be randomized after being stratified by age group because it is believed that younger subjects respond differently to the study drug. In this situation, a subject’s age category would be considered a critical variable for a study and included in ADSL.”

I hope you guys liked it.

Sarath

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How to use MISSING(), NMISS() and the CMISS() functions

SAS provides several functions to test for missing values but in this post we will focus on MISSING(), CMISS() and NMISS() functions. The NMISS() function is reserved for numeric variables. The MISSING() and CMISS() functions can be used with either character or numeric variables.  The CMISS() and NMISS() functions are designed by SAS to count the number of arguments with missing values whereas the MISSING function checks whether or not a variable is missing. The MISSING(), CMISS(), and  NMISS() functions provide a simple approach to check for missing values and these functions let you write few lines of code by avoiding large if-statements when you need to check for missing values in several values at the same time.

MISSING() function is very useful when you need to check any variable has a missing value or not, but not sure if it’s a character or numeric? MISSING function works for either character or numeric variables and it also checks for the special numeric missing values (.A, .B,.C ._ etc)as well. The MISSING() function produces a numeric result (0 or 1) if the data point is present or missing. MISSING(varname) is the same as MISSING(varname)=1.  MISSING(varname)=0 specifies when the data point is present.

The MISSING function is particularly useful if you use special missing values since 'if varname=.' will not identify all missing values in such cases.

NOTE: Missing value is not consistent in SAS as it changes between numeric and character variables. A single period (.) represents the Numeric missing value. A single blank enclosed in single or double quotes (' ' or “  ” ) represents the Character missing value. A single period followed by a single letter or an underscore (ex: .A, .B, .Z, ._) represents Special numeric missing values. Please note that these special missing values available for numeric variables only.

The NMISS() function will count the number of arguments with missing values in the specified list of numeric variables. NMISS() function is very useful if you want to make sure if at least one variable in the list is not missing.

The CMISS() is available with SAS 9.2 and SAS Enterprise Guide 4.3 and is similar to the NMISS() function. The only difference is that it counts the number arguments that are missing for both character and numeric variables.

The NMISS() function returns the number of argument variables which have missing values. NMISS works with multiple numeric values, whereas MISSING works with only one value that can be either numeric or character.

Examples:

* count the number of the variables A, B, and C which have missing values;

count=nmiss(A, B, C);

count=nmiss(of A B C);

* count the number of the variables from Var1 to Var10 which have missing values;

count=nmiss(of var1-var10);

Examples:

x1=nmiss(1,0,.,2,5,.);	2
x2=nmiss(1,0);	0
x3=nmiss(of x1-x2);	0

For more details refer to this page. (USING the CMISS, NMISS and MISSING FUNCTIONS)

For more details regarding the special missing values, please also refer to Special Missing Values in SAS (http://studysas.blogspot.com/2010/04/special-missing-values.html).

References:

1)     Missing values in SAS (http://www.pauldickman.com/teaching/sas/missing.php);

2)     MISSING! - Understanding and Making the Most of Missing Data: SUGI 31: Suzanne M. Humphreys, PRA International, Victoria, BC (Canada).

3)     Special Missing Values in SAS (http://studysas.blogspot.com/2010/04/special-missing-values.html)

4)     Usage Note 36480 KNOWLEDGE BASE / SAMPLES & SAS NOTES from support.sas.com

5)     SAS(R) 9.2 Language Reference: Dictionary, Fourth Edition.

6)     Carpenter's Guide to Innovative SAS Techniques, Art Carpenter (Page:99)

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