Oncology is a rapidly developing and expanding area of therapeutic development with more new drugs and supplemental indications obtaining FDA approval in the past five years compared to all previous years combined.[1] Amid this surge of new treatment options, sponsors and their partners must navigate several persistent challenges. This blog explores the key challenges facing modern oncology clinical trials and highlights some of the most common efficacy and safety endpoints used to evaluate treatment benefit and support regulatory decision-making.
The Current Landscape of Oncology Trials
One of the main challenges is recruitment and retention of patients. Recruitment can be hindered by various aspects including restrictive eligibility criteria, patients’ proximity to trial sites, willingness to participate and physician’s treatment preferences. Strategies have been proposed to mitigate these barriers, such as using virtual visits and leveraging real world evidence to identify patients with rare cancers. [2] The time-consuming nature and high patient burden of oncology trials may also restrict retention rates. Up to a quarter of cancer trials never complete patient accrual, and 18% are terminated with less than half of their target sample size after 3 or more years. [3]
Another difficulty in oncology trials lies in their complexity. Oncology is among the most complex therapeutic areas for trial design, driven by the incorporation of biomarkers and molecular profiling. [4] [5] While this complexity is crucial for providing novel treatments to patients, it also increases costs, extends timelines and demands more sophisticated statistical approaches. [5]
Careful Endpoint Selection is Key for Maximizing Chances of Success
Endpoint selection is a crucial step in the oncology trial design process, influencing sample size, study duration, and regulatory approval. As statisticians, our goal is to select endpoints that balance an understanding of treatment response and quality of life, without compromising operational feasibility. Overall survival is considered the most definitive endpoint to evaluate clinical benefit but can be constrained due to long follow-up periods and large sample sizes required for its evaluation. [6] As a result, surrogate endpoints based on tumor assessments are often favored, particularly in settings where accelerated approval is desired. The table below outlines the most common oncology endpoints and their definitions.
|
Endpoint |
Definition |
|---|---|
|
Overall survival (OS) |
Time from randomization until death from any cause. |
|
Progression-free survival (PFS) |
Time from randomization until first evidence of objective tumor progression or death. |
|
Progression-free survival 2 (PFS2) |
Time from randomization until objective tumor progression on next-line treatment or death. |
|
Time to progression (TTP) |
Time from randomization until first evidence of objective tumor progression. |
|
Time to treatment failure (TTF) |
Time from randomization until discontinuation of treatment for any reason, including disease progression, treatment toxicity, and death. |
|
Disease-free survival (DFS) |
Time from randomization until evidence of disease recurrence or death from any cause. |
|
Event-free survival (EFS) |
Time from randomization until an event including disease progression prior to surgery, local or distant recurrence, or death due to any cause. |
|
Complete response (CR) |
No detectable evidence of tumor. |
|
Pathological complete response (pCR) |
No detectable evidence of residual cancer in tissue samples removed during surgery or biopsy. |
|
Objective response rate (ORR) |
Proportion of patients with tumor size reduction of a predefined amount, and for a minimum time period. Typically includes patients with a complete or partial response. |
|
Disease control rate (DCR) |
Percentage of patients whose intervention has led to a complete response, partial response, or stable disease. |
|
Clinical benefit rate (CBR) |
Percentage of patients whose intervention has led to a complete response, partial response, or stable disease. |
|
Clinical benefit rate (CBR) |
Percentage of patients who achieve a complete response, partial response, or stable disease for a predefined duration, typically ≥ 6 months. |
|
Duration of response (DoR) |
Time from randomization to disease progression or death in patients who achieve complete or partial response. |
|
Time to next treatment (TTNT) |
Time from treatment start date to the beginning of the next line of therapy. |
|
Health-related quality of life (HRQoL) |
An evaluation of a patient’s quality of life with respect to health status over time. |
Endpoint definitions adapted from [6], [7] and [8].
Conclusion
As oncology research continues to evolve, clinical trials are becoming increasingly complex, with greater demands on recruitment, trial design and statistical methodology. Selecting the right endpoint is critical to balancing scientific rigor, operational feasibility and regulatory expectations. While overall survival remains the gold standard for demonstrating clinical benefit, a range of surrogate and patient-focused endpoints can provide valuable insights and support more efficient drug development.
Navigating endpoint strategy in oncology trials can be challenging. Phastar’s oncology biostatistics experts work with sponsors across study design, endpoint selection, estimand development, statistical analysis and regulatory interactions to help deliver robust and clinically meaningful evidence.
References
[1] Agrawal S, Pazdur R. Oncology Drug Development: Challenges of Increasingly Available Therapies. Journal of Clinical Oncology 2026. https://doi.org/10.1200/jco-25-02840.
[2] Chen J, Lu Y, Kummar S. Increasing Patient Participation in Oncology Clinical Trials. Cancer Medicine 2022, 12 (3), 2219–2226. https://doi.org/10.1002/cam4.5150.
[3] Idossa D, Patel S, Florez N. Enhancing Patient Retention in Clinical Trials—Strategies for Success. JAMA Oncol. 2023;9(8):1031–1033. doi:10.1001/jamaoncol.2023.1341
[4] Markey N, Howitt B, El-Mansouri I et al. Clinical trials are becoming more complex: a machine learning analysis of data from over 16,000 trials. Sci Rep 14, 3514 (2024). https://doi.org/10.1038/s41598-024-53211-z
[5] Law E, Chatfield K. The undue burdens of clinical trial participation: implications for equity, diversity, and inclusion. Trials. 2026;27(1):115. Published 2026 Feb 7. doi:10.1186/s13063-026-09540-7
[6] U.S. Department of Health and Human Services Food and Drug Administration. Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics Guidance for Industry, 2018. https://www.fda.gov/media/71195/download (accessed 2026-07-15).
[7] Delgado A, Guddati AK. Clinical endpoints in oncology – a primer. Am J Cancer Res. 2021;11(4):1121-1131. Published 2021 Apr 15.
[8] European Medicines Agency (EMA). Guideline on the evaluation of anticancer medicinal products. Committee for Medicinal Products for Human Use (CHMP), 2019. https://www.ema.europa.eu/en/evaluation-anticancer-medicinal-products-scientific-guideline#current-version-revision-7-8971 (accessed 2026-09-02)
