Template:CLSB-UK Model Cost Analysis

Cost model

There's two parts to our cost modeling; the cost analysis which works out the cost of the test, and the much more impressive screening cost-effectiveness model that works out the value of rolling out a screening programme.

Our screening programme's cost per quality year of life gained would be just be £8300 at the current lowest threshold for lung cancer screening, and taking into account cancer recurrence. Treatments under £20,000 per quality year are deemed cost-effective by NICE, the UK public body that publishes healthcare guidance.

Cost analysis

Our test is much cheaper than existing solutions, especially when you consider the final product will be a multiplexing assay which will test for many diseases simultaneously.

Qiagen PAXgene blood RNA tube: 1 @ £752 / 100 kits	£7.52
Qiagen miRNeasy Mini Kit: 1 @ £1115 / 250 kits	£4.46
Taking a blood sample inc. labour	£3.42
Medical lab technician’s time: 15 minutes @ £8.45 / hour	£2.11
Paper-based toehold switch biosensor: 50 @ 1.7p / 1 μl switch	£0.85
Other lab equipment e.g. sterilisation: tubes: gloves (approx)	£0.30
Total	£18.66

We estimated 15 minutes of lab technician’s time based off the advice of Professor Lovat's research team and Dr Pregibon. The processing of miRNA takes approximately 2 hours for 8 samples. However, if this process was further automated, this cost could be significantly reduced.

The total figure is likely to be an overestimate, as the two major components, namely the PAXgene blood RNA tube and the miRNAeasy Mini Kit which make up over 70% of the total aren’t for clinical use. These products are targeted at researchers and small volume applications, and have many extra features to match - which would not be required for our sensor. Economies of scale and stripping the products down to their core functions could introduce further significant savings.

We believe our test would cost less than £15 ($20) once this is taken into account, making it extremely cost effective as it can test for several diseases simultaneously.

Large cost savings could come by testing saliva instead of blood. Looking into the financials:

Qiagen miRNeasy Mini Kit: 1 @ £1115 / 250 kits	£4.46
Medical lab technician’s time: 15 minutes @ £8.45 / hour	£2.11
Paper-based toehold switch biosensor: 50 @ 1.7p / 1 μl switch	£0.85
Other lab equipment e.g. sterilisation, tubes, gloves (approx)	£0.60
Total	£8.02

However, miRNAs present in saliva are less well documented. Saliva may also be less accurate as its composition can change depending on what was recently eaten so we didn’t look much more into this, but it could be a good area for further research.

Another cost involved in the test is the upfront equipment costs - see how we made a combined densitometer and fluorometer for less than £4 on our hardware page!

Screening cost-effectiveness model

To demonstrate that our test would be cost effective as part of a screening programme we built this cost effectiveness model. To do this it combines cancer prevelance data, the sensitivity and specificity values of our test, our cost analysis data and treament cost data.

We used Tammemägi et al.’s model to calculate the probability of someone getting lung cancer in the next 6 years. It is more sensitive than the simplistic criteria used by the National Lung Screening Trial, and thus a better way of selecting individuals for screening. Tammemägi et al. showed their model would be more cost effective method as more cancers were detected per number screened as it missed fewer cases of cancer.

Although Tammemägi’s model was for lung cancer as a whole, we assume the risk factors put you at equal risk of non-small cell and small-cell lung cancer. As the miRNAs on which our test is based do not indicate small cell lung cancer, we have built the model around NSCLC. All other figures are NSCLC specific.

A quality adjusted life year (QALY) represents one year in perfect health. The ‘quality adjusted’ part accounts for the quality of life for that year, for example 2 years with a 0.6 quality of life score would be 1.2 QALYs. QALYs can be used to measure the benefit a treatment provides (if any). Treatments costing less than £20,000 per QALY are deemed cost effective by NICE. Treaments between £20,000 and £30,000 per QALY may be decided to be cost effective by NICE if there is a high degree of certainty in the cost per QALY or when there are substantial benefits not captured in the cost per QALY figure.

We assumed 63% of the cancers we detected would be in stage 1 as in the National Lung Screening Trial We then assumed that the detection of the other cancers would be evenly distributed through stages 2-4. This is unrealistic for a mass screening programme, as cancers not picked up in stage 1 would likely be picked up in stage 2. This would decrease the cost per QALY as the probability of cure in stage 2 is higher than the average of stages 2-4 and treatment in stage 2 is cheaper than the average cost of stages 2-4.Therefore our model would underestimate the cost effectiveness of our sensor.

In terms of a screening tool, sensitivity relates to the false negative rate, while specificity relates to the false positive rate.

• A 100% sensitive test will always pick up the disease but may also pick up patients that do not have the disease (no false negatives).
• A 100% specific test will never pick up healthy patients but may also miss patients that do have the disease (no false positives).

A highly sensitive test will have few false negatives but lots of false positives. Switch to high specificity.

A highly specific test will have lots of false negatives but few false positives. Switch to high sensitivity.

Sensitivity was taken as 100%, as with appropriately set cutoffs our tool would identify everyone with NSCLC. This is likely to prove untrue were our test to be validated with miRNA levels in a large patient cohort - though sensitivity would still be very close to 100%. Therefore, P(False negative) = 0. However, we included it in the calculations anyway, as later we show that even if sensitivity was significantly less than 100% our test would still be cost effective. We assumed as a lower bound that false negatives will not survive - in reality, some will be detected at a later stage and survive - but were we to have included that, it would have resulted in additional QALYs gained not due to our sensor, meaning we chose to exclude them.

The specificity of our test would be 84% when sensitivity is 100%.

P(cancer) is the figure generated by Tammemägi’s model, weighted by the probability of stage 1 detection (63%) and other stage detection (37%).

• P(cancer) = P(true positive) + P(false negative)
• As sensitivity ≈ 100%, P(false negative) ≈ 0
• P(cancer) ≈ P(true positive) = specificity * P(positive test)
• P(positive test) ≈ P(cancer) / specificity
• P(false positive) = P(positive test) * (1 - specificity) ≈ P(cancer) * (1 - specificity) / specificity

Therefore the probability of a false positive is approximately proportional to the chance of having cancer. P(false positives) always amounted to less 1% of the total cost per QALY so this error of this approximation is negligible.

To recap:

• P(true positive) = P(cancer)
• P(false positive) = P(cancer) * (1 - specificity) / specificity
• P(true negative) = 1 - (P(true positive) + P(false positive))
• P(false negative) = 0

The probability of each option was calculated by multiplying P(true positive) * P(treatment success). In the table, P(treament) represents the chance of the treament being as described by the 2nd column.

Detection	Treatment	P(treatment)	Cost
Stage 1 detection	Successful treatment	0.48	£8100
Stage 1 detection	Unsuccessful treatment	0.52	£21000
Stages 2-4 detection	Successful treatment	0.16	£13000
Stages 2-4 detection	Unsuccessful treatment	0.84	£13000
False positive	No treatment required	NA	£470
True negative	No treatment required	NA	£110
False negative	Unsuccessful Treatment	1	£13,000

All figures are rounded to 2 significant figures to avoid over-stating precision

P(successful treatment) was taken as 0.48 for stage 1, the 5 year survival rate for stage one NSCLC. P(unsuccessful treatment) is simply 1 - P(successful treatment).

P(successful treatment) was taken at 0.16 for later stages, the weighted average of 5 year survival rates of stages 2-4 and for cases were stage was unknown.

The cost of a false positive was taken as the cost of a contrast enhanced chest CT scan (£359), plus the cost of the test times 6 (£18.66 x 6 = £110) as it would be done for 6 years - as that would be the follow up of a positive result of our test. We assumed that all patients with a false positive would subsequently have their cancer ruled out by a CT. Previous screening trials have showed that other procedures are needed, sometimes bronchoscopy, or a false positive proceeding all the way to surgery and treatment. However, we believe a CT in combination with our test would rule out nearly all false positives (especially if several miRNAs were used in tandem to look at the properties of the ‘tumor’ in a subsequent test) - and the probability of a patient having further measures taken would likely be negligible. There is also no data available to quantify this additional cost.

The cost of treatment in stage 1 is £8000 and in stage 4 is £10,050. We used the cost at stage 4 as the upper bound of the cost for stages 2-4. Similar to before, this is likely to decrease were a large scale screening trial to take place and figures for individual stages used - as cost of treatment in stage 2 is far lower than stage 4. We assumed the cost of unsuccessful treatment in stage 1 was the same as the cost of successful treatment in stage one plus average cost of treatment in the other stages - as if treatment is unsuccessful in stage one, a patient is likely to have their tumor progress to later stages. The cost of treatment in stage 4 is not dependent on success as the treatment is just palliative care.

We ignored the cost of recurrence of a cancer - recurrences in cancer are complex and cannot be easily modelled. Furthermore, the earlier the cancer is detected, the lower the chace of recurrence. This means our sensor that can detect NSCLC at stage 1 would drastically reduce this cost.

As Tammemägi gave the probability of cancer in the next 6 years we calculated the cost effectiveness of screening each year for the next 6 years. Hence the cost of 6 true negatives is the cost of our test times 6 (£18.66 x 6 = £110).

Expected QALYs gained was calculated using 70 as lower a bound for life expectancy. The national life expectancy at birth in the UK is 81, however for those who have NSCLC, and thus are likely to have smoked, life expectancy is at least 10 years lower. The quality of life score is assumed to be 1, i.e when someone is cured of NSCLC, the years of life gained are assumed to be in perfect health or any other factors that reduced their quality of life were not considered to be due to NSCLC. Thus QALYs gained = 70 - current age. If an individual is over 66, QALYs gained are taken to be 5, as the statistics used were for 5 year survival.

Cost per QALY was calculated by dividing expected cost by expected QALYs. If this is less than £30000, screening for the next 6 years is cost effective for that patient.

When building this model we tried to use reasonable estimates to ensure we do not overestimate the cost effectiveness of our test.

You can download our spreadsheet here.

The model showed that is is cost effective to screen most people who have smoked.

The American Cancer Society recommends screening for people:

• between 55 and 77 years old,
• who currently smoke or have quit smoking in the last 15 years
• who have at least a 30 pack-year history of smoking

We entered the details for someone at the lowest thresholds: someone aged 55, quit smoking 15 years ago, has a 30 pack-year history of smoking along with the average American’s build: male, white, no history of cancer or any lung diseases, college educated and a BMI of 27. They would cost £6900 per QALY to be screened, making our tool extremely cost effective by the NICE £20,000 per QALY target. It would therefore be cost effective, by our analysis, to screen people with our system with even lower risks of developing NSCLC. This is mainly due to the low cost of our test and significantly improved surival rates when NSCLC is diagnosed in earlier stages.

The one significant assumption we may have over-estimated is sensitivity, as it is unlikely to be 100% in reality. However, for the same individual it would remain cost effective to screen even if sensitivity were reduced to 34% (£19,800 per QALY). This would never be ethically acceptable, given the number of cases of cancer you would miss, but demonstrates the incredible cost efficacy of our sensor.

If the costs of recurrence, as estimated by Cancer Research UK were taken into account - £16000 for stage 1 and £17000 as the average cost of other stages - the cost per QALY would still only be £8300, and at 39% sensitivity would be £19900 per QALY.

Our modeling therefore proves that our sensor would be cost effective in reducing mortality rates from NSCLC. The same principle on which this model is based could be applied to any cancer to screen those with sufficiently high risk using miRNA biomarkers.