A Professional ADaM Conversions With Business Logics

Adam-Convertions-and-extra-variables-and-business-logics

The ADaMIG specifies ADaM standard dataset structures and variables, including naming
conventions. It also specifies standard solutions to implementation issues.

The Analysis Data Model supports efficient generation, replication, and review of analysis results.

The ADaM syllabus includes  ADaM standard data structures:

  1. Subject-Level Analysis Dataset (ADSL)

The ADSL dataset contains one record per subject. It contains variables such as subject-level population flags, planned and actual treatment variables for each period, demographic information, stratification and subgrouping variables, important dates, etc.

  1. Occurrence Data Structure (OCCDS)

ADaM OCCDS  includes several examples with displays, data, and associated metadata applied to adverse events, concomitant medications, and medical history.

  1. Basic Data Structure (BDS).

A BDS dataset contains one or more records per subject, per analysis parameter, per analysis timepoint. Analysis timepoint is not required; it is dependent on the analysis. In situations where there is no analysis timepoint, the structure is one or more records per subject per analysis parameter. This structure contains a central set of variables that represent the actual data being analyzed. The BDS supports parametric and nonparametric analyses such as analysis of variance (ANOVA), analysis of covariance (ANCOVA), categorical analysis, logistic regression, Cochran-Mantel-Haenszel, Wilcoxon rank-sum, time-to-event analysis, etc.

Fundamentals of the ADaM Standard

Standard ADaM Variables

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• ADaM Variable Conventions General Variable Conventions
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• Timing Variable Conventions
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• Date and Time Imputation Flag Variables
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• Flag Variable Conventions
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• Variable Naming Fragments

The ADaM Subject-Level Analysis Dataset (ADSL)

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1. ADSL Identifier Variables
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2. Subject Demographics Variables
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3. ADSL Population Indicator Variables
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4. ADSL Treatment Variables
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5. ADSL Dose Variables
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6. Treatment Timing Variables
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7. Subject-Level Period, Subperiod, and Phase Timing Variables
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8. ADSL Subject-Level Trial Experience Variables
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9. More concepts on Extra variables .

The ADaM (OCCDS)

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1. Working with ADAE Conversions with additional variables
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2. Working with ADDS Conversions with additional variables
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3. Working with ADCM Conversions with additional variables
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4. Working with ADEX Conversions with additional variables
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5. Working with ADDS Conversions with additional variables

The ADaM Basic Data Structure (BDS)

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• 1.Identifier Variables for BDS Datasets
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• 2. Record-Level Treatment and Dose Variables for BDS Datasets
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• Record-Level Dose Variables for BDS Datasets
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• Timing Variables for BDS Datasets
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• Period, Subperiod, and Phase Start and End Timing Variables
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• Suffixes for User-Defined Timing Variables in BDS Datasets
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• Analysis Parameter Variables for BDS Datasets
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• PARAM, AVAL, and AVALC
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• Analysis Parameter Criteria Variables for BDS Datasets
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• Analysis Descriptor Variables for BDS Datasets
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• Analysis Visit Windowing Variables for BDS Datasets
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• Time-to-Event Variables for BDS Datasets
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• Toxicity and Range Variables for BDS Datasets
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• Flag Variables for BDS Datasets
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• BDS Population Indicators
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• Datapoint Traceability Variables
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• SDTM and ADaM Population and Baseline Flags difference
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• Creation of Derived Columns versus Creation of Derived Rows
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• Rules for the Creation of Rows and Columns

Working with BDS Conversion specs

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1. Working with ADVS Conversion with additional Variables
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2. Working with ADLB Conversion with additional variables
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3. Working with ADTTE Conversion with additional Variables

Business Logics:

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1. A parameter-invariant function of AVAL and BASE on the same row that does not involve a transform of BASE should be added as a new column.
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2. Creation of a New Parameter to Handle a Transformation
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3. Creation of a New Parameter to Handle a Second System of Units
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4. Creation of a New Row to Handle a Derived Analysis Timepoint
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5. Creation of New Rows to Handle a Derived Analysis Timepoint When There is Value-Level Population Flagging
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6. Creation of New Rows to Handle Imputation of Missing Values by Last Observation Carried Forward and Worst Observation Carried Forward
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7. Creation of New Rows to Handle Imputation of Missing Values by Baseline Observation Carried Forward and Last Observation Carried Forward
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8. Creation of Endpoint Rows to Facilitate Analysis of a Crossover Design
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9. Creation of a New Parameter to Handle a Function of More Than One Row of a Parameter
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10. Creation of New Parameter to Handle a Function of More Than One Parameter
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11. Creation of New Rows to Handle Multiple Baseline Definitions – Supporting Comparisons to Any Prior Baseline
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12. Creation of New Rows to Handle Multiple Baseline Definitions – Supporting Comparison to Most Recent Baseline
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13. ADaM Methodology and Examples
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14. ADaM Dataset with Identification of Rows Used in a LOCF Analysis
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15. ADaM Dataset with Identification of Rows Used in Both LOCF and WOCF Analyses
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16. ADaM Dataset with Identification of Baseline Rows When Imputation is Used
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17. ADaM Dataset with Identification of Baseline Rows When Baseline is an Average
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18. ADaM Dataset with Identification of Baseline Rows, Including Description in Analysis Timepoint Variable
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19. Identification of Post-Baseline Conceptual Timepoint Rows
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20. ADaM Dataset with Identification of Endpoint Rows
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21. ADaM Dataset with Identification of Endpoint and Post-Baseline Minimum, Maximum, and Average Rows
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22. ADaM Dataset with Identification of Post-Baseline Minimum and Maximum Rows
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23. Identification of Rows Used for Analysis – General Case
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24. ADaM Dataset with Identification of Rows Used for Analysis When Multiple Visits Fall Within a Visit Window
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25. ADaM Dataset with Identification of Rows Used for Analysis When Visit Falls Outside of a Target Window
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26. ADaM Dataset with a Value that is Carried Forward but Not Included in the Analysis
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27. Identification of Population-Specific Analyzed Rows
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28. ADaM Dataset with Subject-Level and Row-Level Indicator Variables
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29. ADaM Dataset with CRITy Populated Only When Criterion Met
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30. ADaM Dataset with CRITy Populated on All Rows within a Parameter
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31. ADaM Dataset with Both Compound and Non-compound Criteria

ADaM Methodology and Examples When the Criterion Has Multiple Responses

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1. ADaM Dataset with a Criterion that Has Multiple Responses
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Enrolled: 49 students
Duration: 30 Days
Lectures: 73
Level: Advanced

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