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

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

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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 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.

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Model Review

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Summary
Validation Score

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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
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