Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test

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Category: Cancer

Tags: Model 14

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Disease Area (Primary)

Cancer

Disease Area (Secondary)

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First Developed

01/13/2020

Last Developed

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Software Used

R (e.g., heemod, BCEA, dampack, hesim)

Model Sponsor

Pharmaceutical or medical device company

Intervention

screening

Model Validation Score

– %

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Results

The MCED test could intercept 485 cancers per year per 100,000 persons, reducing late-stage (III-IV) incidence by 78% in those intercepted. Accounting for lead time, this could reduce 5-year cancer mortality by 39% in those intercepted, resulting in an absolute reduction of 104 deaths per 100,000, or 26% of all cancer-related deaths. Findings are robust across tumor growth scenarios.

Conclusion

What are the key conclusions or current applications of this model?

Source File(s)

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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
Private internal comments	–