Indirect comparison of tisagenlecleucel and blinatumomab in pediatric relapsed/refractory acute lymphoblastic leukemia

Abstract

In the absence of head-to-head trials, an indirect-treatment comparison can estimate the treatment effect of tisagenlecleucel in comparison with blinatumomab on rates of complete remission (CR) and overall survival (OS) in patients with relapsed or primary refractory (R/R) acute lymphoblastic leukemia (ALL). Patient-level data from two pivotal trials, ELIANA (tisagenlecleucel; n = 79) and MT103-205 (blinatumomab; n = 70), were used in comparisons of CR and OS, controlling for cross-trial difference in available patient characteristics. Five different adjustment approaches were implemented: stabilized inverse probability of treatment weight (sIPTW); trimmed sIPTW; stratification by propensity score quintiles; adjustment for prognostic factors; and adjustment for both prognostic factors and propensity score. Comparative analyses indicate that treatment with tisagenlecleucel was associated with a statistically significant higher likelihood of achieving CR and lower hazard of death than treatment with blinatumomab. The tisagenlecleucel group exhibited a higher likelihood of CR than the blinatumomab group in every analysis regardless of adjustment approach (odds ratios: 6.71-9.76). Tisagenlecleucel was also associated with a lower hazard of death than blinatumomab in every analysis, ranging from 68% to 74% lower hazard of death than with blinatumomab, determined using multiple adjustment approaches (hazard ratios: 0.26-0.32). These findings support the growing body of clinical trial and real-world evidence demonstrating that tisagenlecleucel is an important treatment option for children and young adults with R/R ALL.

The “One-to-Many” Survival Analysis to Evaluate a New Treatment in Comparison With Therapeutic Alternatives Based on Reconstructed Patient Data: Enfortumab Vedotin Versus Standard of Care in Advanced or Metastatic Urothelial Carcinoma

“…Furthermore, one drawback of the present analysis is the assumption, not adequately proven, that the previous study by Rivano et al [17] was representative of the main treatments available because a formal literature search was not conducted. The same problem might occur with other analyses belonging to the large group of Shiny studies previously published [3][4][5][6][7][8][9][10][11][12][13][14][15]. Finally, there could be potential issues regarding the ethical requirements of investigations conducted according to the Shiny method and therefore based, like the present one, on "reconstructed" patients.…”

“…Methods that reconstruct individual patient data have been extensively used both for estimating the RMST [15] and for performing indirect head-to-head comparisons [3][4][5][6][7][8][9][10][11][12][13][14]. Overall, nearly 30 topics have already been investigated since 2020, thus providing dozens of multi-trial datasets that summarize the current state of the art in terms of effectiveness.…”

“…While these assessments are quick, their limitations must not be overlooked. The drawbacks of indirect comparisons have been extensively discussed in the previous literature [3][4][5][6][7][8][9][10][11][12][13][14], to which reference can be made. In brief, indirect comparisons lose the balancing effects due to randomization, and so a new treatment can perform better simply because the enrolled population had more favorable prognostic characteristics.…”

“…The characteristics of the Shiny method have previously been described in detail [1,2]. It should be stressed that the reconstruction of patient-level data can have two different purposes: first, reconstructed individual patient data are estimated to determine the restricted mean survival time (RMST) [15]; second, reconstructed individual patient data are estimated to perform indirect comparisons between treatments and to generate a multi-treatment KM graph [3][4][5][6][7][8][9][10][11][12][13][14]. This second application of the Shiny method is increasingly considered the most important one.…”

“…The main prerequisite for performing the Shiny analysis is that the same time-to-event endpoint (e.g., OS or PFS) has been employed in all the trials included in the comparison. Numerous experiences with the Shiny method have already been reported [3][4][5][6][7][8][9][10][11][12][13][14]; anti-cancer treatments are the most suitable area for this analysis [3][4][5][6][7][8][9][10][11][12][13].…”