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Published on: February 8, 2018
Model-based meta-analysis of progression-free survival in non-Hodgkin lymphoma patients
Mengyao Li1, Nimita Dave, Ahmed Hamed Salem
1AbbVie Inc., North Chicago, IL Current address: Merck and Co., Rahway, NJ Department of Clinical Pharmacy, Ain-Shams University, Cairo, Egypt.
Background:
Non-Hodgkin lymphoma (NHL) is a group of lymphoproliferative malignancies with varying treatment responses and progression-free survival (PFS) times. The objective of this study was to quantify the effect of treatment and patient-population characteristics on PFS in patients with NHL.
Methods:
A database was developed from 513 NHL clinical trials reported from 1993 to 2015. Summary-level PFS was obtained from 112 of these trials, which included 155 cohorts and 11,824 patients. Characteristics evaluated for their impact on PFS included cohort treatment, percentage of patients with each NHL subtype, percentage of patients with different numbers of prior treatments, percentage of subjects previously administered rituximab, performance status, disease stage, median age, and sex distribution.
Results:
Rituximab, bendamustine, CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone combination)/CHOP-like, and other nonchemotherapy drugs, aside from bortezomib, prolonged median PFS time 2 to 4-fold. Follicular lymphoma patients had 60% longer median PFS time than mantle cell lymphoma (MCL) patients, while diffuse large B-cell lymphoma patients had a median PFS time that was 25% of MCL patients. Patients who received ≤1 prior treatment had median PFS times > 10-fold longer than patients who received ≥2 prior treatments. The final model predicted the hazard ratio in 75% of the studies within 25% of the observed value and the observed median PFS time of 92% of the studies fell within the predicted 90% confidence intervals.
Conclusions:
The developed PFS model predicts the median PFS time and hazard ratio for specific populations and treatment combinations quantitatively and can potentially be extended to link short-term and long-term clinical outcomes.
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