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Cost-Effectiveness of Venetoclax Plus Obinutuzumab Versus Chlorambucil Plus Obinutuzumab for the First-Line Treatment of Adult Patients With Chronic Lymphocytic Leukemia: An Extended Societal View
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Disease Area (Primary)
Chronic Lymphocytic Leukemia
First Developed
04/01/2023
Last Developed
04/01/2023
Software Used
Microsoft Excel
Model Sponsor
Government agency
Intervention
venetoclax
Model Validation Score
– %
Coming Soon In Phase II: You will be able to pay a fee to download the CADTH Tool for your model which includes subaggregated scores.
Results
The base case resulted in a discounted ICER −49 928 EUR/QALY gained (with incremental negative costs and positive effects). None of the ICERs resulted from deterministic sensitivity and scenario analyses exceeded the chosen willingness-to-pay threshold of 20 000 EUR/QALY, and > 99% of the iterations in the probabilistic sensitivity analysis were cost-effective. VOI analyses showed a maximum expected value of eliminating all model parameter uncertainty of 183 591 EUR.
Conclusion
VenO being dominant over ClbO in treatment-naïve adult patients with CLL assuming a Dutch societal perspective
Source File(s)
Model Review
Only visible for the model owner
Summary
Validation Score
– %
Internal Comments
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Full review
| 01 Model Built Reflective | yes |
|---|---|
| 02 Model Subgroups | yes |
| 03 Model Assesses all comparators | yes |
| 04 Model Incorporates costs | yes |
| 05 Model assesses all outcomes | yes |
| 06 Model structure validated by experts | yes |
|---|---|
| 07 Model aligns with or justifies deviation from previous models | yes |
| 08 Time in health states | yes |
|---|---|
| 09 Consistency with time in states | yes |
| 10 Clinical events extractable | yes |
| 11 Consistency with number of clinical events | yes |
| 12 Impact of adverse events | yes |
| 13 Consistency with adverse events | yes |
| 14 Life-years reported | yes |
| 15 Impact on mortality | yes |
| 16a Reasons for mortality differences | yes |
| 16b Reasons for mortality differences | yes |
| 16c Reasons for mortality differences | yes |
| 16d Reasons for mortality differences | yes |
| 17 Main driver of incremental life-years | yes |
| 18 Consistency with mortality rates | yes |
| 19 No technology-specific utilities used | yes |
| 20 Main driver of cost-effectiveness | yes |
| 21 Extrapolation methods identified | yes |
| 22 Adjustable time horizon | yes |
| 23 Double counting avoided | yes |
| 24 Surrogate vs final outcomes alignment | yes |
| 25 Flexibility for treatment effect waning | yes |
| 26 Access to deterministic and Monte Carlo results | yes |
|---|---|
| 27 Clear trace from inputs to outcomes | yes |
| 28 Macros used only for simulation/navigation (Excel) | yes |
| 29 QALY equivalence across technologies | yes |
|---|---|
| 30 Extreme effectiveness impact on QALY | yes |
| 31 Slight effectiveness impact on QALY | yes |
| 32 Increased mortality lowers QALYs | yes |
| 33 Reduced mortality increases QALYs | yes |
| 34 Increased baseline risk lowers QALYs | yes |
| 35 Reduced baseline risk increases QALYs | yes |
| 36 Zero mortality leads to equal life-years | yes |
| 37 Cost change affects only total costs | yes |
| 38 Utilities = 1 makes QALYs equal life-years | yes |
| 39 No discounting increases QALYs/costs | yes |
| 40 Higher discounting decreases QALYs/costs | yes |
| 41 Shorter time horizon lowers QALYs/costs | yes |
| 42 Inputs switchable across alternatives | yes |
| 43 Cost-QALY correlation across simulations | yes |
| 44 Strong cost correlation from Monte Carlo | yes |
| 45 Strong QALY correlation from Monte Carlo | yes |
| 46 Deterministic ≈ Probabilistic results | yes |
| 47 Backward trace from results to inputs | yes |
|---|---|
| 48 Backward trace from results to inputs | yes |
| 49 No use of non-transparent Excel functions | yes |
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| 50 No hidden sheets, rows, or columns | yes |
| 51 No custom formulas inside VBA macros | yes |
| 52 Parameters persist after macros | yes |
| 53 Transparent input structure in single worksheet | yes |
| Private internal comments | – |