Modeling Oncology Study Designs: The Use of APHASE and APERIOD in Analysis Data Model (ADaM)
Introduction
Oncology study designs often involve complex scenarios where study treatments are administered at various intervals and additional therapies may be allowed in response to disease progression. In such cases, determining changes in study treatment and selecting the appropriate ADaM timing variables, such as APHASE and APERIOD, becomes crucial for accurate data analysis. This article will discuss a specific oncology study design, its schematic representation, and the rationale behind using APHASE or APERIOD to model the study segments.
Oncology Study Design
In the given oncology study, subjects were randomized to one of two possible treatment arms and remained on that arm for the duration of the study. The study treatment was administered via injection at various intervals. If a subject experienced disease progression, they were allowed to receive additional anticancer therapy, which was not considered as study treatment for analysis purposes. If they subsequently experienced a second disease progression, they were discontinued from the study. The efficacy analysis summarized responses from the beginning of the study until the start of the first additional anticancer therapy, and then from there until the end of the study.
The schematic of the study design is as follows:
APHASE: Screening → Before Progression → After Progression
Choosing APHASE or APERIOD
In this oncology study, the study treatment was not changed after the start of the first anticancer therapy. Therefore, APHASE was chosen instead of APERIOD to model the study segments. APHASE represents the analysis phase associated with the record and can be used to represent different study time divisions that may or may not involve the administration of an investigational product. In this case, APHASE was used to denote the study segments as “Screening,” “Before Progression,” and “After Progression.”
TRT01P, defined in the ADSL (Analysis Dataset Subject Level) dataset, was used to populate the TRTP (Treatment) variable on the BDS (Basic Data Structure) dataset records. This allowed for the proper representation of the treatment assignments in the analysis datasets.
Conclusion
The selection of the appropriate ADaM timing variables, such as APHASE and APERIOD, plays a critical role in accurately modeling and analyzing data from complex oncology study designs. By carefully considering the study treatment changes and the unique requirements of the study design, researchers can make informed decisions on the use of APHASE or APERIOD to create consistent and interpretable datasets. This, in turn, contributes to the advancement of drug development and improved patient care in the field of oncology.
