A Macro to carry values through observations forwards or backwards over missing or 'null' values within a BY group or a SAS® data set.

This paper explains the structure of a macro and its usefulness for imputing missing or defined 'null' values with a prior or following quantity of a numeric or character column in a SAS® data set or BY group. The macro uses DICTIONARY.COLUMNS to determine the data type, carries values over multiple successive observations with missing or 'null' amounts. When the specified data set column is numeric, parameters can be passed to return the differences between each quantity and the first or last values of the by group or the successive differences between non-missing values. The 'null' value defaults to missing but can be user specified. Included in this paper are examples of practical applications, such as imputing missing dates and times of events since the first or most recent dose in PK data.

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Generating Homeomorphically Irreducible Trees (Solving the Blackboard Problem in the Movie “Good Will Hunting”)

In the movie, Good Will Hunting (1997), a mathematics professor challenges his students to draw all Homeomorphically Irreducible Trees of Order Ten, that is, a collection of trees each having ten dots connected by lines. The well-known blackboard problem in the movie poses a formidable challenge, especially for larger trees having twenty or thirty nodes. It would require an extremely large blackboard to draw all the trees, as well as to erase those deemed redundant or incorrect. The paper explains a SAS® solution generating Homeomorphically Irreducible Trees of order N.

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A Critique of Implementing the Submission Data Tabulation Model (SDTM) for Drugs and Medical Devices

The Clinical Data Interchange Standards Consortium (CDISC) encompasses a variety of standards for medical research. Amongst the several standards developed by the CDISC organization are standards for data collection (Clinical Data Acquisition Standard Harmonization - CDASH), data submission (Study Data Tabulation Model - SDTM) and data analysis (Analysis Data Model - ADaM).  Adoption of SDTM (in general and the seven Device domains) by the medical device industry has been slow. Reasons for this slow adoption and suggestions for solutions adoption will be discussed.

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Multiple Imputation: A Statistical Programming Story.

Multiple imputation (MI) is a technique for handling missing data. MI is becoming an increasingly popular method for sensitivity analyses in order to assess the impact of missing data. The statistical theory behind MI is a very intense and evolving field of research for statisticians. It is important, as statistical programmers, to understand the technique in order to collaborate with statisticians on the recommended MI method.

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CREATING VIABLE SAS® DATA SETS FROM SURVEY MONKEY® TRANSPORT FILES. 

Survey Monkey is an application that provides a means for creating online surveys. Unfortunately, the transport (Excel) file from this application requires a complete overhaul in order to do any serious data analysis. (2016)

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A Statistical Analyses Across Overlapping Time Intervals Based on Person-Years.

This paper explains an intuitive method for augmenting the analysis data set so that the overlapping time intervals are represented, accordingly. PharmaSUG 2015 – Paper HA01.

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Recruiting for Retention.

Many companies struggle with hiring and retention. DataCeutics follows a successful process for hiring and retaining highly skilled SAS and Statistical programmers. Western Users of SAS® Software 2014.

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Implementing a Heuristic Solution.

Finding a Knight's sequence of moves on a chess board to visit each square only once.  An exercise using macros and data reports. PharmaSUG 2015 - Paper MS01.

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DataCeutics CR Toolkit™ Now Available Under SAS Version 9.1.3

Software that produces reports for regulatory submissions.

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A paper written by clinical Programmers on Effective communication needed for successful projects.

It's not that you know it, it's how you show it. PharmaSUG 2015 - Paper MS01. 

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