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Utilization of real‐world data in assessing treatment effectiveness for diffuse large B‐cell lymphoma
Author(s) -
Nowakowski Grzegorz,
Maurer Matthew J.,
Cerhan James R.,
Dey Debarshi,
Sehn Laurie H.
Publication year - 2023
Publication title -
american journal of hematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.456
H-Index - 105
eISSN - 1096-8652
pISSN - 0361-8609
DOI - 10.1002/ajh.26767
Subject(s) - observational study , medicine , covariate , clinical trial , randomized controlled trial , oncology , propensity score matching , diffuse large b cell lymphoma , sample size determination , comparative effectiveness research , clinical study design , lymphoma , computer science , statistics , machine learning , pathology , alternative medicine , mathematics
Direct comparisons of the effectiveness of the numerous novel therapies in the diffuse large B‐cell lymphoma (DLBCL) treatment landscape in a range of head‐to‐head randomized phase 3 trials would be time‐consuming and costly. Comparative effectiveness studies using real‐world data (RWD) represent a complementary approach. Recently, several studies of relapsed/refractory (R/R) DLBCL have used RWD to create observational cohorts to compare patient outcomes with cohorts derived from single‐arm phase 2 trials. Using propensity score methods to balance clinically and prognostically relevant baseline covariates, closely matched patient‐level cohorts can be generated. By incorporating appropriate measures to assess covariate balance and address potential bias in comparative effectiveness study designs, robust comparative analyses can be performed. Results from such studies have been used to supplement regulatory approval of therapies assessed in single‐arm trials. While RWD studies have a greater susceptibility to bias compared to randomized controlled trials, well‐designed and appropriately analyzed studies can provide complementary real‐world evidence on treatment effectiveness.