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Original Research

Cost-effectiveness of acalabrutinib regimens in treatment-naïve chronic lymphocytic leukemia in the United States

ORCID Icon, , , , &
Pages 579-589 | Received 10 Aug 2022, Accepted 24 Mar 2023, Published online: 10 Apr 2023

Figures & data

Figure 1. Diagram of the three-state semi-Markov model structure.

TP1 represents the probability of the patient transitioning from PF to PD. TP2 represents the probability of the patient transitioning from PF to Death. TP3 represents the probability of the patient transitioning from PD to Death. Transition probabilities are described within the Methods section (Clinical data). Not shown are tunnel states for each possible transition during each cycle to model time-dependent transitions. PD, progressed disease; PF, progression-free; TP, transition probability.
Figure 1. Diagram of the three-state semi-Markov model structure.

Figure 2. Parametric models overlaying the TTP Kaplan–Meier data for all comparators.

Parametric distributions were selected based on the lowest AIC/BIC values along with clinical plausibility. AIC, Akaike information criterion; BIC, Bayesian information criterion; KM, Kaplan–Meier; TTP, time to progression.
Figure 2. Parametric models overlaying the TTP Kaplan–Meier data for all comparators.

Figure 3. Parametric models overlaying the TTDeath Kaplan–Meier data for all comparators.

Parametric distributions were selected based on the lowest AIC/BIC values along with clinical plausibility. Note: AIC, Akaike information criterion; BIC, Bayesian information criterion; KM, Kaplan–Meier; TTDeath, time to death.
Figure 3. Parametric models overlaying the TTDeath Kaplan–Meier data for all comparators.

Table 1. Model parameters and goodness-of-fit indicators for the distributions of TTP, TTDeath, and PPS curves.

Table 2. Parameters implemented in the model base case analysis.

Table 3. Incremental cost-effectiveness results from the base case analysis.

Figure 4. Deterministic sensitivity analyses: Tornado diagrams showing results for acalabrutinib monotherapy (a) and acalabrutinib + obinutuzumab (b) vs chlorambucil + obinutuzumab.

Costs ($) represent 2021 US dollars. Low and high values for each parameter were based on their 95% confidence intervals or ±20% of their base case values when 95% confidence intervals were unavailable.
Figure 4. Deterministic sensitivity analyses: Tornado diagrams showing results for acalabrutinib monotherapy (a) and acalabrutinib + obinutuzumab (b) vs chlorambucil + obinutuzumab.

Figure 5. Probabilistic sensitivity analysis results for acalabrutinib monotherapy vs chlorambucil + obinutuzumab: Cost-effectiveness plane (a) and cost-effectiveness acceptability curves (b).

Costs ($) represent 2021 US dollars. QALY, quality-adjusted life-year.
Figure 5. Probabilistic sensitivity analysis results for acalabrutinib monotherapy vs chlorambucil + obinutuzumab: Cost-effectiveness plane (a) and cost-effectiveness acceptability curves (b).

Figure 6. Probabilistic sensitivity analysis results for acalabrutinib + obinutuzumab vs chlorambucil + obinutuzumab: Cost-effectiveness plane (a) and cost-effectiveness acceptability curves (b).

Costs ($) represent 2021 US dollars. QALY, quality-adjusted life-year.
Figure 6. Probabilistic sensitivity analysis results for acalabrutinib + obinutuzumab vs chlorambucil + obinutuzumab: Cost-effectiveness plane (a) and cost-effectiveness acceptability curves (b).
Supplemental material

Supplemental Material

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