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Oracle DMW 3.1.1 Public views

Oracle DMW public views are listed in the API guide which you can find online at Oracle homepage here: Public Views (oracle.com)

The public views are useful for listing study meta data including data models, tables, columns, transformations, validation etc.

If your company has created study templates in Oracle DMW is it essential that you can check and verify using a database query for any changes between versions. If your company is maintaining study templates in a Meta Data Repository (MDR) or MS Excel, you will need a solution to verify that the MDR or Excel contains current and updated information.

Blinding (masking) of data with random codelist values in Oracle DMW

In DMW you can blind data in your study using different levels of blinding types (table, column, row, cell), and you can specify how the blinded data should be masked, e.g. using hardcoded ‘999999’ for numeric values, or ‘XXXXXX’ for character values. But more advanced blinding can also be accomplished, e.g. blinding of data using random date within FPFV and LPLV, random values from a codelist applied for field validation, e.g. simple Yes/No, or AE relation to trial product: LIKELY, UNLIKEY, POSSIBLE, PROBABLE.

You can use the Oracle database package DBMS_RANDOM and its function to generate a random value, e.g. random integer from 0..6, and then use the DECODE to set a value depending on the random value generated.

Example: Random value generated from codelist for Action taken to Trial Product in SDTM-AE (Adverse Events):

Random valueCodelist Value assignment
0DRUG INTERUPTED
1DRUG WITHDRAWN
2DOSE REDUCED
3DOSE INCREASED
4DOSE NOT CHANGED
5UNKNOWN
6N/A
Random codelist value assignment for use with Bliding and masking criteria in Oracle DMW.

Note: For random date you will need to write a function to lookup and return a valid date from within FPFV to LPLV.

If you want to know more or need help to implement more advanced blinding solutions in Oracle DMW, please contact us.

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