You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 7 Next »

This section addresses how to identify the records of an ADaM dataset that are used for analysis. The four specific issues addressed include: (1) identification of the records used in an LOCF analysis; (2) identification of the record containing the baseline value; (3) identification of post-baseline conceptual timepoint records, such as endpoint, minimum, maximum, or average; and (4) identification of specific records used in an analysis.

Added points here:


NumIdentificationImplementation
1

Records Used in a Timepoint Imputation Analysis

  • This section considers the issue of how to identify records used in a timepoint-related imputation analysis as well as how to represent data imputed for missing timepoints in an ADaM dataset. LOCF (Last Observation Carried Forward) is one of the most commonly used timepoint-related imputation analyses and is therefore specifically mentioned. However, the methodology is general and is not restricted to LOCF analysis. WOCF (Worst Observation Carried Forward) analysis is also mentioned to emphasize the generalizability.
  • When an analysis timepoint is missing, the ADaM methodology is to create a new record in the ADaM dataset to represent the missing timepoint and identify these imputed records by populating the derivation type variable DTYPE.
  • For example, when an LOCF/WOCF analysis is being performed, create LOCF/WOCF records when the LOCF/WOCF analysis timepoints are missing, and identify these imputed records by populating the derivation type variable DTYPE with values LOCF or WOCF. All of the original records would have null values in DTYPE. It would be very simple to select the appropriate records for analysis by selecting DTYPE = null for Data as Observed (DAO) analysis, DTYPE = null or LOCF for LOCF analysis, and DTYPE = null or WOCF for WOCF analysis. This approach would require understanding and communicating that if the DTYPE flag were not referenced correctly, the analysis would default to using all records, including the DAO records, plus the records derived by LOCF and WOCF. To perform a correct DAO analysis, one would need to explicitly select DTYPE = null.

Identification of Baseline Records
  • Many statistical analyses require the identification of a baseline value. This section describes how a record used as a baseline is identified.
  • The ADaM methodology is to create a baseline flag column to indicate the record used as baseline (the record whose value of AVAL is used to populate the BASE variable). This method does not require duplication of records in the event that the baseline record is not derived.
  • Although a baseline record flag variable ABLFL is created and used to identify the record that is the baseline record, this does not prohibit also providing a record with a unique value of AVISIT (e.g., "Baseline"), designating the baseline record used for analysis, even if redundant with another record. For more complicated baseline definitions (functions of multiple records), a derived baseline record would have to be created as described in ADD LINK Section 4.2.1.3, Rule 3. This methodology requires that clear metadata be provided for the baseline record variable so that the value can be reproduced accurately.



  • No labels