PMID- 37651087
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231217
IS  - 2509-4254 (Electronic)
IS  - 2509-4262 (Print)
IS  - 2509-4262 (Linking)
VI  - 7
IP  - 6
DP  - 2023 Nov
TI  - Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in 
      Large B-Cell Lymphoma.
PG  - 941-950
LID - 10.1007/s41669-023-00435-w [doi]
AB  - BACKGROUND: Durable remission has been observed in patients with relapsed or 
      refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen 
      receptor (CAR) T-cell therapy. Consequently, hazard functions for overall 
      survival (OS) are often complex, requiring the use of flexible methods for 
      extrapolations. OBJECTIVES: We aimed to retrospectively compare the predictive 
      accuracy of different survival extrapolation methods and evaluate the validity of 
      goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in 
      R/R LBCL. METHODS: OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 
      001. Standard parametric, mixture cure, cubic spline, and mixture models were fit 
      to multiple database locks (DBLs), with varying follow-up durations. GOF was 
      assessed using the Akaike information criterion and Bayesian information 
      criterion. Predictive accuracy was calculated as the mean absolute error (MAE) 
      relative to OS observed in the most mature DBL. RESULTS: For all studies, mixture 
      cure and cubic spline models provided the best predictive accuracy for the least 
      mature DBL (MAE 0.013‒0.085 and 0.014‒0.128, respectively). The predictive 
      accuracy of the standard parametric and mixture models showed larger variation 
      (MAE 0.024‒0.162 and 0.013‒0.176, respectively). With increasing data maturity, 
      the predictive accuracy of standard parametric models remained poor. Correlation 
      between GOF criteria and predictive accuracy was low, particularly for the least 
      mature DBL. CONCLUSIONS: Our analyses demonstrated that mixture cure and cubic 
      spline models provide the most accurate survival extrapolations of CAR T-cell 
      therapies in LBCL. Furthermore, GOF should not be the only criteria used when 
      selecting the optimal survival model.
CI  - (c) 2023. The Author(s).
FAU - Peterse, Elisabeth F P
AU  - Peterse EFP
AD  - OPEN Health, Rotterdam, The Netherlands.
FAU - Verburg-Baltussen, Elisabeth J M
AU  - Verburg-Baltussen EJM
AD  - OPEN Health, Rotterdam, The Netherlands.
FAU - Stewart, Alexa
AU  - Stewart A
AD  - OPEN Health, Oxford, UK.
FAU - Liu, Fei Fei
AU  - Liu FF
AD  - Bristol Myers Squibb, Princeton, NJ, USA.
FAU - Parker, Christopher
AU  - Parker C
AD  - Bristol Myers Squibb, Uxbridge, UK.
FAU - Treur, Maarten
AU  - Treur M
AD  - OPEN Health, Rotterdam, The Netherlands.
FAU - Malcolm, Bill
AU  - Malcolm B
AD  - Bristol Myers Squibb, Uxbridge, UK.
FAU - Klijn, Sven L
AU  - Klijn SL
AD  - Bristol Myers Squibb, Princeton, NJ, USA. sven.klijn@bms.com.
LA  - eng
PT  - Journal Article
DEP - 20230831
PL  - Switzerland
TA  - Pharmacoecon Open
JT  - PharmacoEconomics - open
JID - 101700780
PMC - PMC10721757
COIS- Elisabeth F.P. Peterse, Elisabeth J.M. Verburg-Baltussen and Alexa Stewart were 
      employees of OPEN Health at the time of this research. Maarten Treur is an 
      employee of OPEN Health. OPEN Health received payment from Bristol Myers Squibb 
      to conduct this study. Fei Fei Liu, Bill Malcolm, and Sven L. Klijn are employees 
      and shareholders of Bristol Myers Squibb. Christopher Parker was an employee of 
      and held stock in Bristol Myers Squibb at the time of this research. This study 
      was sponsored by Bristol Myers Squibb.
EDAT- 2023/08/31 12:43
MHDA- 2023/08/31 12:44
PMCR- 2023/08/31
CRDT- 2023/08/31 11:17
PHST- 2023/08/06 00:00 [accepted]
PHST- 2023/08/31 12:44 [medline]
PHST- 2023/08/31 12:43 [pubmed]
PHST- 2023/08/31 11:17 [entrez]
PHST- 2023/08/31 00:00 [pmc-release]
AID - 10.1007/s41669-023-00435-w [pii]
AID - 435 [pii]
AID - 10.1007/s41669-023-00435-w [doi]
PST - ppublish
SO  - Pharmacoecon Open. 2023 Nov;7(6):941-950. doi: 10.1007/s41669-023-00435-w. Epub 
      2023 Aug 31.