Understanding EC vs. EX Domains in SDTM: When to Use Each

-
Understanding EC vs. EX Domains in SDTM: When to Use Each

Understanding EC vs. EX Domains in SDTM: When to Use Each

In SDTM, the EC (Exposure as Collected) and EX (Exposure) domains are both used to capture data related to drug or therapy exposure, but they serve different purposes depending on how the exposure data is collected and whether the study is blinded or unblinded.

Key Updates from PharmaSUG Papers:

  • PharmaSUG 2017 Paper DS08 introduces the historical context of the EC domain, which was established in SDTMIG v3.2 to support the EX domain by providing detailed traceability for exposure data. EC helps capture deviations, titrations, and other variations from planned dosing, especially when the collected data doesn't match protocol-specified dosing.
  • PharmaSUG 2022 Paper DS121 emphasizes the importance of capturing dose modifications using the EC domain, which often occurs in oncology trials. By utilizing EC, sponsors can accurately document variations such as dose holds, eliminations, and reductions, which later assist in deriving the EX domain.
  • PharmaSUG 2018 Paper DS16 discusses the challenges in blinded studies, highlighting that the EC domain can be used to store blinded data until the study is unblinded, after which the EX domain can be derived. This paper also details the use of EC to capture missed doses that cannot be represented in EX.
  • PharmaSUG China 2021 Paper DS083 provides a detailed discussion of how to present exposure data in a reviewer-friendly manner. It also introduces two new domains from SDTMIG v3.3 — AG (Procedure Agents) and ML (Meals) — which, though not directly related to EC/EX, offer additional context for studies that involve substances administered during procedures or nutritional intake.
---

When to Use the EC and EX Domains

EC (Exposure as Collected) Domain:

  • Use EC when dose modifications such as elimination, hold, delay, reduction, or mid-cycle adjustments are expected due to treatment-related factors (e.g., toxicities in oncology trials).
  • EC is suitable for blinded studies to store collected exposure information until unblinding.
  • EC captures exact details such as missed doses, variations from protocol-specified units, and planned or scheduled exposure using the `ECMOOD` variable.

EX (Exposure) Domain:

  • Use EX to represent planned exposure that aligns with the study protocol. This includes the administration of investigational products in protocol-specified units.
  • EX captures the actual administered dose after unblinding and can also reflect doses of placebos in clinical trials.
---

Key Takeaways for EC and EX Domain Usage

  • Blinded Studies: EC can capture blinded doses, and once unblinded, the EX domain should reflect the actual doses administered.
  • Dose Modifications: EC captures any variations from planned dosing, including dose holds, eliminations, and adjustments.
  • Missed Doses: Use EC to document missed doses and the reasons for those missed doses using `ECOCCUR` and `SUPPEC` for reasons like adverse events.
  • Protocol-Specified Units: EC can capture doses in collected units (e.g., volume of a dosing solution), while EX converts them into protocol-specified units (e.g., mg/kg).
---

Introduction to EC and EX Domains

The EC domain captures the exact exposure data as it is collected in the study. This is especially useful when exposure data varies between subjects, such as in cases of dose titrations, interruptions, or other adjustments. The key feature of the EC domain is its ability to reflect actual data, making it indispensable in complex trials where the administration schedule doesn’t always follow the protocol exactly.

For instance, if subjects are receiving doses that are adjusted based on their responses or lab results, or if subjects experience dose interruptions, the EC domain should be used to capture this variability. It provides an accurate picture of what really happened, even if the data does not align with the protocol-specified dose.

Example: Titration or Adjusted Dosing Scenario

In a trial where Drug B’s dose is titrated based on a subject's response, one subject might start at 25 mg and increase to 50 mg after 10 days. Another subject could remain at 25 mg due to adverse events, and a third subject might increase to 75 mg. These variations should be captured in the EC domain.

STUDYID USUBJID ECDOSE ECDOSU ECDOSFRM ECSTDTC ECENDTC ECREASND
ABC123 001 25 mg Tablet 2024-01-01 2024-01-10 Titration
ABC123 001 50 mg Tablet 2024-01-11 2024-01-14
ABC123 002 25 mg Tablet 2024-01-01 2024-01-15 Adverse Event

When to Use the EX Domain

The EX domain captures the planned exposure based on the study protocol. It is used when the actual exposure follows the protocol as intended. The EX domain should be used for trials where the dosing regimen is straightforward and subjects receive the planned doses at scheduled times.

For example, if a trial protocol specifies that subjects receive 50 mg of Drug A daily for 30 days, and all subjects follow this schedule without any variations, the EX domain can capture this data.

Example: Simple Dosing Scenario

In a study where Drug A is administered in a fixed dose of 50 mg daily, the EX domain captures the planned exposure:

STUDYID USUBJID EXTRT EXDOSE EXDOSU EXROUTE EXSTDTC
XYZ456 001 Drug A 50 mg Oral 2024-02-01
XYZ456 002 Drug A 50 mg Oral 2024-02-01

Using Both EC and EX Domains Together

In some cases, both domains can be used together to represent the planned vs. actual exposure. For instance, the EX domain captures the protocol-specified dose (e.g., 50 mg daily), while the EC domain captures deviations, such as dose interruptions or adjustments. This approach provides a complete picture of the exposure.

Example: Combined Use of EC and EX Domains

In a study where Drug D is administered as 50 mg daily but a subject misses doses due to personal reasons, the EX domain would capture the planned regimen, while the EC domain would record the missed doses.

EX Domain (Planned Dose):
STUDYID USUBJID EXTRT EXDOSE EXDOSU EXROUTE EXSTDTC
DEF789 001 Drug D 50 mg Oral 2024-03-01
EC Domain (Actual Doses with Missed Doses):
STUDYID USUBJID ECDOSE ECDOSU ECDOSFRM ECSTDTC ECENDTC ECREASND
DEF789 001 50 mg Tablet 2024-03-01 2024-03-05
DEF789 001 50 mg Tablet 2024-03-07 2024-03-30 Missed Dose

Additional Considerations for Submission

  • Do not duplicate EC and EX data unless necessary.
  • Use SUPPEC to provide additional reasons for missed or not-given doses in the EC domain.
  • Ensure proper representation of blinded and unblinded data in EC and EX to comply with regulatory expectations.
---

Conclusion

By leveraging the **EC** and **EX** domains appropriately, sponsors can ensure clear traceability of exposure data and provide regulatory reviewers with a complete and accurate story of how subjects were exposed to the study treatment. These domains, when used in tandem, help differentiate between collected and derived data, making it easier for reviewers to assess and understand study results.

Popular posts from this blog

SAS Interview Questions and Answers: CDISC, SDTM and ADAM etc

-
1) What do you know about CDISC and its standards? CDISC stands for Clinical Data Interchange Standards Consortium and it is developed keeping in mind to bring great deal of efficiency in the entire drug development process. CDISC brings efficiency to the entire drug development process by improving the data quality and speed-up the whole drug development process and to do that CDISC developed a series of standards, which include Operation data Model (ODM), Study data Tabulation Model (SDTM) and the Analysis Data Model ADaM ). 2) Why people these days are more talking about CDSIC and what advantages it brings to the Pharmaceutical Industry? A) Generally speaking, Only about 30% of programming time is used to generate statistical results with SAS®, and the rest of programming time is used to familiarize data structure, check data accuracy, and tabulate/list raw data and statistical results into certain formats. This non-statistical programming time will be significantly reduced...
Read more

Comparing Two Methods for Removing Formats and Informats in SAS: DATA Step vs. PROC DATASETS

-
Comparing Two Approaches to Removing Formats and Informats in SAS Comparing Two Approaches to Removing Formats and Informats in SAS When working with SAS datasets, there are times when you need to remove formats and informats that have been previously assigned to variables. Two primary approaches can be used for this task: Using the DATA Step Using the PROC DATASETS Procedure This article compares and contrasts these two approaches to help you determine which method is most appropriate for your needs. Approach 1: Using the DATA Step The DATA step is a versatile and commonly used method for removing formats and informats. By assigning variables to a null format or informat, you can effectively remove these attributes from your dataset. Example: data mydata_clean; set mydata; format _all_; informat _all_; run; In this example, the mydata dataset is processed in the DATA step, and...
Read more

Studyday calculation ( --DY Variable in SDTM)

-
USE OF THE “STUDY DAY” VARIABLES The permissible Study Day variables (--DY, --STDY, and --ENDY) describe the relative day of the observation starting with the reference date as Day 1. They are determined by comparing the date portion of the respective date/time variables (--DTC, --STDTC, and --ENDTC) to the date portion of the Subject Reference Start Date (RFSTDTC from the Demographics domain). The Subject Reference Start Date (RFSTDTC) is designated as Study Day 1. The Study Day value is incremented by 1 for each date following RFSTDTC. Dates prior to RFSTDTC are decremented by 1, with the date preceding RFSTDTC designated as Study Day -1 (there is no Study Day 0). This algorithm for determining Study Day is consistent with how people typically describe sequential days relative to a fixed reference point, but creates problems if used for mathematical calculations because it does not allow for a Day 0. As such, Study Day is not suited for use in subsequent numerical computations...
Read more