Utilization of real-world data in assessing treatment effectiveness for diffuse large B-cell lymphoma

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.

Automated summary

Comparative effectiveness studies using real-world data can provide complementary evidence on treatment effectiveness. By balancing baseline covariates using propensity score methods, closely matched patient-level cohorts can be generated for robust comparative analyses.