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Hadfield, Matthew J.

https://orcid.org/0000-0002-8768-0082

Mayo Clinic

Brown University

Citations 3

Characterizing the authorship of phase 3 randomized clinical trials in oncology, 1960-2023.
Bakow, Brianna R. , Fan, Elaine, Ganta, Teja , Glover, Michael , Hadfield, Matthew J. , Kothari, Shalin , Sethi, Tarsheen , Singhi, Eric K. , Rubinstein, Sam , Warner, Jeremy L. , Brown, Alaina J. , Choe, Jennifer H , Fan, Elaine Y. L., Hilton, Cameron Beau , Khaki, Ali Raza , Lythgoe, Mark, Nagpal, Seema , Nguyen, Ryan H. , Noyd, David H. , Riaz, Irbaz Bin , Rooney, Michael Kevin

Journal of Clinical Oncology | June 3, 2024 | Volume: 42 | Issue: 16_suppl | Pages: 11079-11079

10.1200/jco.2024.42.16_suppl.11079 Get It

11079 Background: Phase 3 randomized clinical trials (RCTs) inform the standard of care in cancer treatment yet have a mixed track record of success. In this study, we sought to 1) determine the overall success rate of phase 3 RCTs; 2) assess whether authorship patterns differ between successful (positive) and unsuccessful (negative) trials; and 3) explore if individual authors have distinct track records of success. Methods: The HemOnc knowledgebase (KB) was queried for initial publications of phase 3 RCTs of systemic anticancer therapy; those without primary outcome described were excluded. RCTs were labeled positive if the primary endpoint was met per HemOnc standard criteria (P value ≤ 0.05 or hazard ratio upper bound ≤ 1 for any experimental arm[s]) and negative if they did not meet these criteria or if the experimental arm was statistically inferior. Trial metadata and author information were extracted from the HemOnc KB. Author positive publication rate (PPR) was defined as % of publications reporting a positive result divided by total number of eligible publications. Prolific authors were defined as those with 6+ eligible publications. Author count for manuscripts reporting positive vs negative results was evaluated with the Mann-Whitney U test; other statistics were descriptive. Results: 3328 studies met criteria as of February 5, 2024; these were associated with 3305 initial publications between 1960-2023, involving 25,412 unique authors. 1474 (44.3%) of the studies were positive. Publications reporting positive trial results had, on average, 3 more authors, median (interquartile range) of 18 (12-23) vs 15 (11-20), p<0.0001. The mean (±SD) PPR for n=1967 prolific authors was 47.7% (±22.3%). The PPR was not related to individual author output; mean PPR for non-prolific authors was 48.0% (SD not reported). Differences in PPR were apparent by the disease under study and the years of publication (Table). Among the 46 prolific authors who had a PPR 2+ SD above the mean, 26 (55%) were from China followed by Italy with 3 (6%). Conclusions: Despite known positive publication bias, the success rate of published phase 3 RCTs remains <50%. Publications reporting positive results had significantly more authors; this finding requires further study including adjustment for trial sample size. Significant variation in PPR amongst fields could be due to watershed therapeutic advances, differential selection of primary endpoints, and/or selective reporting of results. Finally, a signal for geographic association of outlier PPRs suggests the need for further investigation. [Table: see text]
Mind the gap: Estimating the opportunity lost due to the gap between FDA and EMA cancer drug approvals.
Gage, Samantha, Eteshola, Elias , Hadfield, Matthew J. , Hamze, Dany, Masse, Gabrielle, Jain, Sandeep Kumar , Rubinstein, Sam , Khaki, Ali Raza , Mishra, Sanjay , Rooney, Michael Kevin , Hilal, Talal , Lythgoe, Mark, Warner, Jeremy L.

Journal of Clinical Oncology | June 3, 2024 | Volume: 42 | Issue: 16_suppl | Pages: 11030-11030

10.1200/jco.2024.42.16_suppl.11030 Get It

11030 Background: Drug regulators operate on their own timelines to assess the safety and efficacy of new drugs and/or extensions to existing indications, such that there are delays between regulators. During such a gap, patients in the “early” domain have access to the approved drug, while those in the “late” domain do not, outside of compassionate use. Our study aimed to quantify the “opportunity lost” for new therapies approved by FDA and EMA, assuming patients in the late domain receive standard-of-care (SOC) therapy – which is presumably inferior to the new drug – during the gap. Methods: Anticancer drugs approved by both FDA and EMA were identified through the HemOnc knowledgebase. Inclusion criteria included: 1) drugs approved in both domains based on the same randomized clinical trial (RCTs) for the same indication; 2) a primary time-to-event endpoint; and 3) a quantitated median time-to-event for the primary endpoint. For each included trial, the approval gap between FDA and EMA in days and the ratio of approval gap to median control arm time-to-event duration was calculated. A ratio ≥2 implies that, on average, no late domain patients starting treatment in the first half of the gap would be expected to have access to the new drug prior to an event. Results: Of 60 eligible RCTs, 59 had calculable median event durations. 25 (42%) had a primary overall survival endpoint; the most common surrogate endpoint was progression-free survival (PFS; 29/59; 49%). The median (interquartile range [IQR]) approval gap was 186 (124-271) days, with FDA being the first approver in 56/59 (95%) cases. The median (IQR) ratio of approval gap to median control arm event duration was 0.63 (0.35-1.11). Drugs with ratio ≥2 (Table) were targeted or immunotherapies; 7/8 (88%) were approved with PFS endpoint; 5/8 (62.5%) were approved for second line or later. Conclusions: Our study shows that FDA approves most drugs prior to EMA and a nontrivial number of patients may experience progression or death events while awaiting access to anticancer therapies already approved in another domain. The analysis does not take into consideration scenarios in which the new therapy is inferior to SOC or delays in patient access (due to slow uptake; regulatory barriers [in Europe/UK]; or patient affordability [in US]). Further regulatory collaboration (e.g., Project Orbis) between FDA and EMA could help to reduce and potentially ameliorate this differential in access to new cancer drugs in the future. [Table: see text]
Evaluation of the study of control arms in randomized clinical trials of cancer.
Jain, Sandeep Kumar , Sethi, Tarsheen , Rubinstein, Sam , Madireddy, Sathwik , Kulkarni, Amit , Kothari, Shalin , Hadfield, Matthew J. , Ganta, Teja , Zauderer, Marjorie G. , Schoen, Martin W. , Nguyen, Ryan H. , Nagpal, Seema , Mohan, Sanjay , Lythgoe, Mark, Liang, Wayne H. , Hilal, Talal , Goyal, Gaurav , Dholaria, Bhagirathbhai , Brown, Alaina J. , Warner, Jeremy L.

Journal of Clinical Oncology | June 3, 2024 | Volume: 42 | Issue: 16_suppl | Pages: 11023-11023

10.1200/jco.2024.42.16_suppl.11023 Get It

11023 Background: Randomized clinical trials (RCTs) in cancer typically compare experimental to control arm regimens. The choice of an appropriate control (ctrl) arm can have a major impact on the expected success of an RCT. The frequency with which cancer RCTs use control arms based on established dosing and scheduling protocols has not been described. Methods: The HemOnc knowledgebase (KB) was used to identify systemic anti-cancer therapy (SACT) regimen variants, defined as regimens with identical components that differ in dosing, scheduling, and/or route. Non-cancer, nonrandomized, and non-SACT studies were excluded. Study publication year was used to define standard variants as those evaluated in the experimental arm of a positive phase 3 RCT >1 year prior to publication as a control arm, regardless of cancer type or context of treatment. Study-variant dyads were evaluated to determine whether the variant was standard. Success rates of RCTs with standard vs non-standard control arms were evaluated with Fisher’s exact test. Results: 5221 studies were associated with ≥1 named variant in the HemOnc KB, as of 2024-02-06. After exclusions, there were 3511 study-variant dyads (2386 studies; 1714 variants). The 9 most common regimens are shown in the Table. The median (IQR) number of variants per regimen was 2 (1-3); carboplatin & paclitaxel (CP) had the most variants (n=33). Across all control arm study-variant dyads, 2228/3492 (64%) utilized non-standard variants. For example, 60/97 studies (62%) of docetaxel as control used the 75 mg/m2 q3wk variant after it was established in 2000, whereas n=27 others used 19 non-standard variants. Trials that used a standard control arm had a numerically higher success rate, 45% vs 42% (OR 1.10, 95% CI 0.92-1.32). Conclusions: Non-standard regimen variants are frequently used in cancer RCTs. Reasons for this could include toxicity, patient convenience, or emerging data from smaller studies that establish comparable efficacy or improved toxicity to standard variants. However, the a priori efficacy of non-standard control arms is less rigorously established, and trials with standard control arms might be more successful. Future directions include quantifying the granular differences between standard and non-standard variants and investigating whether certain types of non-standard variation (e.g. fewer cycles, lower doses) associate with trial success rates. [Table: see text]