Analysis data sets

Transforming Data and Creating Analysis Data Sets... 

Key Concepts for Creating Analysis Data Sets 84 Defining Variables Once 84 Defining Study Populations 85 Defining Baseline Observations 85 Last Observation Carried Forward (LOCF) 86 Defining Study Day 89 Windowing Data 91 Transposing Data 94... 

Categorical Data and Why Zero and Missing Results Differ Greatly 102 Performing Many-to-Many Comparisons/Joins 106 Using Medical Dictionaries 108 Other Tricks and Traps in Data Manipulation 112 Common Analysis Data Sets 118 Critical Variables Data Set 118 Change-from-Baseline Data Set 118 Time-to-Event Data Set 121... 

In this section we discuss some of the key concepts to keep in mind when creating an analysis data set. The next section takes these key concepts and puts them together to show how the most common analysis data sets are created. 

One of the primary reasons for creating analysis data sets is to have variable derivations in a single place. If a variable is defined in a single analysis data set, then the following are true ... 

Which patients should be in which data set is something that should be considered before analysis data sets are created. For example, it is often decided that all analysis data sets should have a record for a subject if that subject was randomized to treatment and is considered an intent-to-treat subject. Whether this is true or not, the specifications for analysis data sets should make it clear who should be present in any analysis data set. Here is a list of common populations and their definitions ... 

Program 4.3 for creating study day variables for the SDTM data sets. However, the General Considerations document from the CDISC Analysis Data Set Modeling Team states that you should use the algorithm in Program 4.2 for analysis data sets. Whether you are deriving data based on the CDISC models or not, you should calculate study day variables in a consistent fashion across a clinical trial or set of trials for an application. 

Medical dictionaries often need to be referenced when creating various analysis data sets For instance, perhaps the raw adverse event database in your clinical data management system contains only the MedDRA code. The code is worth having, but you would need the adverse event body system and preferred medical term to provide a useful summary of events. 

In this section we take the aforementioned principles and guidelines for analysis data sets and apply them to creating the most common analysis data sets. The critical variables, change-from-baseline, and time-to-event data sets are presented. Although these are the most common analysis data sets that a statistical programmer will encounter, they are by no means all of the possible analysis data sets. When it comes to analysis data sets, there is no limit to the diversity of data that you may have to create. 

The purpose of using change-from-baseline analysis data sets is to measure what effect some therapeutic intervention had on some kind of diagnostic measure. A measure is taken before and after therapy, and a difference and sometimes a percentage difference are calculated for each post-baseline measure. These data sets are generally normalized... 

A time-to-event analysis data set captures the information about the time distance between therapeutic intervention and some other particular event. There are two time-to-event analysis variables that deserve special attention and definition. They are as follows ... 

Get data This step involves pulling the data to be used into SAS. It often requires merging treatment or study population data with analysis data sets or some other data to be summarized/listed. 

Manipulate data On occasion the data being pulled into SAS for summarization and presentation are not ready for that purpose. In such cases, you may need to manipulate or create additional variables within the SAS program. Keep in mind that it is almost always better to create derived variables prior to this step in analysis data sets programming. 

This section demonstrates aspects of the application of a nonlinear mixed effects modehng approach to the analysis of count data using the premature neonate apnea data described in Section 27.2. The objective is to draw attention to key features the pharmacometrician should be aware of and provide methods for model diagnostics and general considerations. Selected results presented here are excerpted and adapted from the complete analysis (3). A subset of the analysis data set is provided in the appendix. 

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