REVIEW AND RECOMMENDATIONS OF ENDPOINTS FOR REAL-WORLD STUDIES IN ONCOLOGY

Author(s)

Julian GS, Duva A, Santana P, Cavalcanti HE, Ballalai AF
IQVIA, São Paulo, Brazil

Objective: Real-world studies have larger external validity when compared with randomized clinical trials (RCTs). In oncology, although real-world studies are crucial to evaluate drugs´ effectiveness, most real-world studies use clinical endpoints that reflect RCTs reality. Therefore, the aim of this study is to review and recommend the best endpoint approaches in oncology for different real-world data sources. Methods: We have reviewed publications, evaluations and recommendations of real-world endpoints in oncology in MEDLINE and grey literature from regulatory agencies. After the identification of the publications, we compared them with RECIST and WHO criteria. For comparison purposes, we compared most commonly used surrogate endpoints: response rate (RR), progression-free survival (PFS) and time-to-treatment failure (TTF). We did not include overall survival in the comparison, once it is the final objective of an oncologic evaluation. Finally, we performed a critical evaluation of which endpoint would be more appropriate for each types of source. Results:Definitions in real-world setting are less strict than those defined by RECIST or WHO. In RR evaluation, real-world recommendation is based on clinician´s overall assessment, while in RECIST and WHO criteria those are predefined. Specifically in PFS evaluation, the most adequate endpoint in real-world is TTF, once this reflects the real drug usage – specially in some tumors that disease progression does not reflect treatment discontinuation. Additionally, TTF reflects better information regarding health economics modeling, in which this information is required. Conclusion:Utilization of surrogate endpoints, such as RR, PFS and TTF are important in cases with immature OS data, or with excessive incompleteness data – classical problem in real world setting. In data sources with clinical information (e.g. EHR, chart review), the use of RR with complementary OS is the ideal scenario, while in claim data TTF may reflect better the reality of the patient, obviously, with complementary OS, if available.

Conference/Value in Health Info

2018-11, ISPOR Europe 2018, Barcelona, Spain

Value in Health, Vol. 21, S3 (October 2018)

Code

PCP55

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference

Disease

Oncology

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