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663 Correlation between early endpoints and overall survival in non-small-cell lung cancer: a trial-level meta-analysis
  1. Shameer Khader1,
  2. Youyi Zhang1,
  3. Daniel Jackson1,
  4. Kirsty Rhodes1,
  5. Imran Khan Anwer Neelufer1,
  6. Sreenath Nampally1,
  7. Andrzej Prokop2,
  8. Emmette Hutchison1,
  9. Jiabu Ye1,
  10. Feng Liu1,
  11. Antony Sabin1,
  12. James Weatherall1,
  13. Cristina Duran1,
  14. Renee Iacona1,
  15. Faisal Khan1 and
  16. Pralay Mukhopadhyay1
  1. 1AstraZeneca, Gaithersburg, MD, USA
  2. 2AstraZeneca Pharma Poland, Warsaw, MD, Poland


Background In clinical trials that assess novel therapeutic agents in patients with non-small-cell lung cancer (NSCLC), early endpoints (e.g. progression-free survival [PFS] and objective response rate) are often evaluated as indicators of biological drug activity, and are used as surrogate endpoints for overall survival (OS). Compiling trial-level data could help to develop a predictive framework to ascertain correlation trends between treatment effects for early (e.g. odds ratio [OR] for PFS at 6 months) and late endpoints (e.g. hazard ratio [HR] OS).

Methods A dataset was compiled, which included 81 randomized, controlled trials (RCTs; Phase II–IV) of NSCLC (Stages I–IV), with 35 drugs and 156 observations. The dataset was collected from multiple source databases, including Citeline, TrialTrove,, and PubMed. We applied random-effects meta-analysis to correlate a variety of treatment effects for early endpoints with HR OS. We performed meta-regression analyses across different data-strata, stratified by the mechanism of action (MoA) of the investigational product (programmed death protein-1/programmed death-ligand 1 [PD-1/PD-L1], epidermal growth factor receptor [EGFR], vascular endothelial growth factor receptor, and DNA damage response).

Results Low (Spearman’s rho 0.3–<0.5) to moderate (rho 0.5–<0.7) correlations were observed between HR OS and (1) HR PFS, (2) OR PFS 4 months, and (3) OR PFS 6 months for PD-1/PD-L1 trials, EGFR trials, and all trials combined (Random-effects meta-regression; P<0.05). Similar correlations were observed between each of the early endpoint treatment effects and HR OS. For example, the moderate correlation observed between OR PFS 4 months and HR OS (rho-0.579; 95% confidence interval [CI]-0.800,-0.274; meta-regression R2= 72.5%) was similar to that between OR PFS 6 months and HR OS (rho-0.633; 95% CI-0.802, -0.383; R2=86.1%) for PD-1/PD-L1 trials. Note, the reported rho values are negative as a HR<1, and an OR>1, indicate benefit with the investigational product.

Conclusions Using a comprehensive summary data set in the NSCLC space, we observed low-to-moderate correlations between treatment effects for early endpoints and HR OS across RCTs of agents with different MoAs, including trials of PD-1/PD-L1 checkpoint inhibitors. Exploration of additional endpoints, beyond RECIST, is required to identify other early indicators of efficacy that might predict HR OS. By incorporating additional trial-level parameters and building composite biomarkers using machine intelligence methods, in collaboration with innovative trial design efforts, we envisage to improve the prediction of HR OS from early endpoints.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:

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