Dataset Subdivision

Due to the aforementioned high complexity and diversity of ophthalmology data, it is discouraged to funnel all records from the OE SDTM dataset into a single ADOE dataset, as this will result in an overly complicated program/dataset pair. {admiralophtha} instead suggests the following partition:

• ADOE for general miscellaneous ophthalmology tests not used for efficacy programming.
• ADBCVA for BCVA data only - these will almost always constitute a primary or secondary endpoint and so will require endpoint-related programming such as criterion flags.
• Any other endpoints for which specific efficacy programming may be required should have their own ADaM dataset (e.g. for intraocular pressure data, ADIOP).
• ADVFQ for Visual Functioning Questionnaire data.

Subdividing the ADaM datasets in a study as above will ensure that wherever custom efficacy programming is required, this will automatically be limited to the data of interest. For instance, if a study has various endpoints of the form Gain of between x and y letters relative to baseline (or similar) then each will likely require a criterion variable/flag pair (see the Criterion Flag section for more detail). If the BCVA data were stored in ADOE, then these criterion variable/flag pairs would be blank and irrelevant for most of the data in the dataset, save for the BCVA records. Conversely, collecting the BCVA data in ADBCVA ensures the criterion variable/flag pairs are more relevant, and the resulting dataset is more readable.

Criterion Flags

{admiralophtha} suggests the use of criterion variable/flag pairs CRITx/CRITxFL where possible for endpoint programming. If implemented correctly, this is a very transparent approach as the condition for CRITxFL can be clearly encoded in CRITx, without having to view any documentation. When appropriate, the condition in CRITx should be represented programmatically rather than in words to reduce possibility of confusion. For instance, for an endpoint such as Gain of between x and y letters relative to baseline, one would set CRIT1 = "x <= CHG <= y".

Note: Though allowable according to ADaM standards, it is generally discouraged to use the same criterion flag/variable pair for more than one criterion across multiple parameters in an ADaM dataset, as this renders the dataset confusing to scrutinise.

For BCVA change endpoints, provides the function derive_var_bcvacritxfl to add them en masse. Additionally, the function automatically populates CRITx in the programmatic manner described above.

Affected Eye Derivation

{admiralophtha} function derive_var_afeye follows the standard derivation: Set to “BOTH EYES” when Study Eye Selection [ADSL.STUDYEYE] is not missing, and Laterality [xxLAT] is equal to “BILATERAL”. Else set to “STUDY EYE” when Study Eye Selection [ADSL.STUDYEYE] is either “RIGHT” or “LEFT”, and matches Laterality [xxLAT] for the observation record. Else set to “STUDY EYE” when Study Eye Selection [ADSL.STUDYEYE] is “BILATERAL”, and Laterality [xxLAT] is not missing for the observation record. Else set to “FELLOW EYE” when Study Eye Selection [ADSL.STUDYEYE] is either “RIGHT” or “LEFT”, and [xxLAT] is not missing and does not match Laterality [xxLAT] for the observation record. Else set to null.

If the standard values of Location [xxLOC] = “EYE” and Laterality [xxLAT] = “LEFT”, “RIGHT”, “BILATERAL” are not the same for your study this can be updated as inputs in the function, otherwise this is also expected for AFEYE to be derived, and a warning will be returned if any other values are found.