Early cost-effectiveness model of whole genome sequencing as a molecular diagnostic test versus standard of care in locally advanced and metastatic (Stage IIIB,C/ IV) non-squamous non-small cell lung cancer

$1.00

Category: NSCLC

Tags: Model 11

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Disease Area (Primary)

NSCLC

Disease Area (Secondary)

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First Developed

07/25/2021

Last Developed

08/18/2021

Software Used

Microsoft Excel

Model License

Creative Commons (e.g., CC-BY, CC-BY-NC)

Model Sponsor

Government agency

Intervention

whole_genome_sequencing

Model Validation Score

– %

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Results

WGS as a diagnostic test resulted in more QALYs (0.002) and costs (€1534 [incremental net monetary benefit –€1349]), and SoC followed by WGS resulted in fewer QALYs (–0.002) and more costs (€1059 [–€1194]) compared with SoC alone. WGS as a diagnostic test was only cost effective if it was priced at €2000 per patient and identified 2.7% more actionable patients than SoC alone. Treating these additional identified patients with new treatments costing >€4069 per month decreased the probability of cost effectiveness.

Conclusion

Our analysis suggests that providing WGS as a diagnostic test would be cost effective compared with SoC followed by WGS and SoC alone if costs for WGS decreased and additional patients with actionable targets were identified. This cost-effectiveness model can be used to incorporate new findings iteratively and to support ongoing decision making regarding the use of WGS in this rapidly evolving field.

Source File(s)

Publication(s)
Other Supporting Document(s)

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Model Review

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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
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	–