Cost-effectiveness of axicabtagene ciloleucel versus tisagenlecleucel for the treatment of 3L + relapsed/refractory large B-cell lymphoma in the United States: incorporating longer survival results

To provide an update on the cost-effectiveness of the chimeric antigen receptor (CAR) T-cell therapies axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) for the treatment of relapsed/refractory (r/r) large B-cell lymphoma (LBCL) among patients who have previously received ≥2 lines of systemic therapy using more mature clinical trial data cuts (60 months for axi-cel overall survival [OS] and 45 months for tisa-cel OS and progression-free survival [PFS]). A partitioned survival model consisting of three health states (pre-progression, post-progression and death) was used to estimate quality-adjusted life years (QALYs) and costs associated with axi-cel and tisa-cel over a lifetime horizon. PFS and OS inputs for axi-cel and tisa-cel were based on a previously published matching-adjusted indirect treatment comparison (MAIC). Long-term OS and PFS were extrapolated using parametric survival mixture cure models (PS-MCMs). Costs of CAR-T cell therapy drug acquisition and administration, conditioning chemotherapy, apheresis, CAR T-specific monitoring, stem cell transplant, hospitalization, adverse events, routine care, and terminal care were sourced from US cost databases. Health state utilities were derived from previous publications. Model inputs were varied using a range of sensitivity and scenario analyses. Compared with tisa-cel, axi-cel resulted in 2.51 additional QALYs and $50,185 additional costs (an incremental cost-effectiveness ratio [ICER] of$19,994 per QALY gained). In probabilistic sensitivity analysis (PSA), the ICER for axi-cel versus tisa-cel was ≤$50,000/QALY in 99.4$33,500 in 99% of simulations. Axi-cel remained cost-effective versus tisa-cel (assuming a willingness-to-pay threshold of $150,000 per QALY) across a range of scenarios. With longer-term survival data, axi-cel continues to represent a cost-effective option versus tisa-cel for treatment of r/r LBCL among patients who have previously received ≥2 lines of systemic therapy, from a US payer perspective.</p

Betting on the fastest horse: Using computer simulation to design a combination HIV intervention for future projects in Maharashtra, India

<div><p>Objective</p><p>To inform the design of a combination intervention strategy targeting HIV-infected unhealthy alcohol users in Maharashtra, India, that could be tested in future randomized control trials.</p><p>Methods</p><p>Using probabilistic compartmental simulation modeling we compared intervention strategies targeting HIV-infected unhealthy alcohol users on antiretroviral therapy (ART) in Maharashtra, India. We tested interventions targeting four behaviors (unhealthy alcohol consumption, risky sexual behavior, depression and antiretroviral adherence), in three formats (individual, group based, community) and two durations (shorter versus longer). A total of 5,386 possible intervention combinations were tested across the population for a 20-year time horizon and intervention bundles were narrowed down based on incremental cost-effectiveness analysis using a two-step probabilistic uncertainty analysis approach.</p><p>Results</p><p>Taking into account uncertainty in transmission variables and intervention cost and effectiveness values, we were able to reduce the number of possible intervention combinations to be used in a randomized control trial from over 5,000 to less than 5. The most robust intervention bundle identified was a combination of three interventions: long individual alcohol counseling; weekly Short Message Service (SMS) adherence counseling; and brief sex risk group counseling.</p><p>Conclusions</p><p>In addition to guiding policy design, simulation modeling of HIV transmission can be used as a preparatory step to trial design, offering a method for intervention pre-selection at a reduced cost.</p></div

Clinical trial interventions and associated costs and effects considered in HIV transmission simulation model.

<p>Uniform distributions were used for all costs and lognormal distribution for all effects in probabilistic analyses. Intervention costs were derived from India-specific sources.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref035" target="_blank">35</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref049" target="_blank">49</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref051" target="_blank">51</a>] Cost in 2012 USD.</p

Analyses methodology.

<p>Pipeline workflow for intervention bundle prioritization. a, Creation of efficient frontier for all combinations of 15 interventions and filtering out 8 interventions that were never found on the frontier. b, For the remaining 7 interventions, completion of 100 probabilistic runs varying intervention costs and effects and filtering out intervention bundles that were never found on the frontier. c, Completion of a full probabilistic analyses (run N = 1000) varying intervention cost and effect as well as 96 input variables. All analysis was run for a 20-year simulation.</p

Efficient frontier for HIV interventions during a 20-year simulation of HIV epidemic in Maharashtra, India.

<p>a, Graphical representation efficient frontier for all permutations of 12 interventions (4096 total combinations). Blue circles represent packages of interventions on the frontier, red represent packages off the frontier. b, focused graphical representation of efficient frontier for the lower end of discounted cost (0.888–0.898 Billion USD). c, Interventions contained within each efficient frontier package.</p

India transmission model inputs.

<p>India transmission model inputs.</p

Final probabilistic analysis of top 32 intervention bundles.

<p>a, Percentage of runs in which each bundle was identified on the efficient frontier across 1000 probabilistic runs using the 32 bundles identified in the previous analysis step. b, bundle ranking comparison between intervention-only probabilistic and the full probabilistic analysis. c, intervention bundle details corresponding to panel a. *Bundle 0 represents runs with no intervention.</p