Page History

...

At present, there is no standard method for representing the randomization strata factors and values in SDTM-based datasets. Depending on the randomization process, it might be unnecessary to represent variables and values specific to stratification in SDTM-based datasets if the information can be found within an appropriate domain. For example, if age and sex were used as stratification factors, then the Demographics (DM) variables AGE and SEX should appropriately reflect values used for randomization. However, more sophisticated randomizations or more complicated derivations of prognostic factors, such as whether a subject had ever used a particular concomitant medication for a given length of time, may be harder to identify or document in SDTM-based datasets. If using an interactive voice response system (IVRS), the values used for randomization would be captured by the system and would correspond to the values that are represented on the randomization schedule. As-verified values are typically derived by comparing the values used for randomization against the data that is in the SDTM dataset, whether it be a simple match with a single data point such as sex or the reprogramming of more complex factors such as previous treatmentsproducts.

The following table provides a set of variables to allow maximum flexibility in representing the description of the prognostic factors. To illustrate the interrelationships of the variables, the examples for every variable in the CDISC Notes column use the combination of 3 stratification factors: age group (“<50” or “ >=50”), prior product status (“Product naïve”, “Product experienced”), and hypertension (“Y” or “N”).

...