The cost-effective diagnostic strategy depends on the cost-effectiveness of active treatment; however, the long-term outcomes of men with prostate cancer remain uncertain and the evidence base, perhaps, remains insufficient. The 2014 NICE clinical guideline13recommends that men with low-risk cancer should
be offered active surveillance, whereas men with intermediate- and high-risk cancer should be offered radical treatment. These recommendations are mostly based on expert consensus rather than formal quantitative evaluations to identify for whom treatment should be recommended, how to monitor men with lower-risk cancer and which treatments should be prioritised. The lack of evidence on management decisions has upstream implications for the cost-effectiveness of diagnostic strategies because their costs and benefits are partly determined by the costs and benefits of active surveillance and treatment. If treatment is more beneficial than suggested by the evidence from PIVOT,8the benefits of more-sensitive
strategies have been underestimated. Conversely, if treatment is less beneficial, the benefits of less-sensitive strategies have been underestimated. This is explored in the threshold sensitivity analysis, which showed that the less-sensitive and less-costly diagnostic strategies become cost-effective for a 15–20% reduction in the clinical effectiveness of radical treatment.
The ProtecT study9, which compared RP, radiotherapy and active monitoring in men with prostate cancer,
may help resolve this uncertainty. At the time of conducting this analysis, the 10-year follow-up results are available, but only for the entire trial population. Active treatment showed no effect on mortality, although few deaths occurred, but a significant reduction in cancer progression (HR 0.39, 95% CI 0.27 to 0.54). This is similar to the results of PIVOT8(relative risk for bone metastasis 0.44, 95% CI 0.25 to 0.76).
However, this refers to the entire trial population, almost 80% of whom had low-risk cancer and, therefore, would not be offered active treatment according to NICE’s recommendations.13Evidence is
required on the effect of active treatment specifically in men with intermediate- and high-risk (CS) cancer, for whom the NICE guidelines13recommend treatment.
Moreover, the predicted long-term outcomes are affected by the misclassification of men by TRUS-guided biopsy in PIVOT.8In PIVOT, patients were diagnosed, enrolled and classified by risk on the basis of a biopsy
result and not on the basis of their true disease status. The biopsy is an imperfect test in that it produces a considerable proportion of false-negative results, with evidence from PROMIS suggesting that only 34% of intermediate-risk patients are identified as having CS cancer. The implications are twofold. First, the men enrolled in PIVOT may not be representative of the men with cancer in the general population. This is only an issue if the patients who were missed by the biopsy have a different prognosis from the prognoses of the patients enrolled in PIVOT. Second, the PIVOT risk groups may have been misclassified and this may introduce bias in the prediction of long-term outcomes and the relative clinical effectiveness of radical treatment, and, therefore, may introduce bias to the benefits of correctly detecting CS cancer. Quality- adjusted survival may have been underestimated in the high-risk subgroup if the sensitivity of TRUS-guided biopsy is higher for higher cancer severity; hence, the high-risk subgroup may consist of men with the most severe cancer, who have worse prognoses. Because only 18 out of 576 men in PROMIS had high-risk cancer, any bias arising is unlikely to affect the results.
Quality-adjusted survival may also have been underestimated in the intermediate- and low-risk subgroups in PIVOT if these were contaminated by more severe patients. This may be an issue particularly for long-term outcomes post radical treatment if its effects are more pronounced in the intermediate-risk subgroup. If the quality-adjusted survival of men treated with radical treatment was more underestimated than that of men managed with observation, the benefits of more-sensitive strategies in detecting CS cancer may have been underestimated. This issue can be resolved only with better-quality evidence on the outcomes of men with prostate cancer, based on a perfect test such as TPM-biopsy for their diagnosis and classification.
DISCUSSION OF THE ECONOMIC EVALUATION
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There is some uncertainty regarding the costs of the tests, particularly TPM-biopsy, which affects the results. The NHS reference cost for TPM-biopsy is approximately 25% higher than the PbR tariff, which makes strategies with TPM-biopsy less likely to be cost-effective.
In principle, the unit costs of resource use should reflect the cost to the budget holder under whose perspective the analysis is conducted (i.e. the UK NHS).148This would suggest that appropriate unit costs
should reflect what the NHS actually pays external individuals or organisations–in this case, what hospitals pay staff and the suppliers of equipment, services and consumables. Hence, unit costs would be based on the mean reference costs reported by relevant hospital trusts. However, the UK NHS is devolved into a range of organisations: NHS England, clinical commissioning groups, hospital trusts, etc. When a patient is managed in secondary care, the hospital is generally reimbursed by the relevant clinical commissioning group that has referred them, according to the PbR tariff. The PbR tariff is informed by NHS reference costs, adjusted for inflation and changes in efficiency. If the specific perspective of the analysis is that of clinical commissioning groups, for example, the PbR tariff may be the more relevant basis for estimating unit costs. The impact of changes in the unit costs is provided in the multivariate sensitivity analysis (see Chapter 7,Threshold sensitivity analysis).
The value-of-information analysis explored the expected costs to population health associated with parameter uncertainty and the maximum expected benefit associated with investment in further research. It shows that, although the expected health loss attributable to parameter uncertainty is small at the per-patient level, it sums to a considerable amount at the population level over 5 years, given the large number of men referred for investigation. In order to inform which research to commission in the future, the value-of-information analysis should be developed further. This could take a number of avenues. The expected value of perfect parameter information can indicate which parameters are driving the uncertainty and have greater consequences, and hence should be prioritised for investment. The expected value of sample information can be explored in order to help inform the most cost-effective study design and sample size to obtain information on a specific parameter. There is also an important question regarding the structural uncertainties in the model. Specifically, the model assumes that men receiving expectant management have the outcomes of men in the observation arm of PIVOT. However, in practice, these men may receive active treatment over time. This was explored in the scenario analysis. However, future research could extend the model to account for this assumption as a parameter and explore its impact in the value-of-information analysis. There is little guidance on how to develop this work in practice and future methodological research may address this gap.
The results suggest that the most cost-effective strategy is using mpMRI as the first test, rather than TRUS-guided biopsy. It may be difficult to implement this change, because it implies a shift in resources from histopathology to imaging departments. Although financial resources are more straightforward to transfer, there may be issues around capacity, namely related to skilled staff and capital investment in equipment. Consequently, more consideration and research may be required on how best to implement this policy and manage change.
This study suggests that mpMRI is cost-effective as the first test for the diagnosis of prostate cancer when followed by MRI-targeted TRUS-guided biopsy in men in whom mpMRI suggests suspicion for CS cancer and a second biopsy if no CS cancer is found. The cost-effective strategy uses the most sensitive definitions and cut-off point for CS cancer: definition 2 for TRUS-guided biopsy (any Gleason pattern of≥4 and/or cancer core length of≥4 mm) and definition 2 for mpMRI (lesion volume of≥0.2 ml and/or a Gleason
score of≥3+4) at the cut-off point of≥2 on the Likert scale for suspicion of CS cancer.
This study makes it clear that the value of the diagnostic test depends on the value of the treatment options that follow. Here, the most cost-effective strategy is one that detects almost all men with CS cancer because active treatment was found to be highly cost-effective in this population. The findings are sensitive to the costs of the tests, hence the motivation for the bivariate sensitivity analysis indicating the most cost-effective strategy for a wide range of unit costs of each test. This can inform decisions in
contexts facing different sets of prices. The findings are also sensitive to the sensitivity of MRI-targeted TRUS-guided biopsy compared with TRUS-guided biopsy alone, which was tested in the threshold sensitivity analysis. As with any research, the findings are grounded on the evidence used. The cost-effectiveness of diagnostic tests depends on the cost-effectiveness of subsequent treatment decisions. If radical treatment is less beneficial than PIVOT suggests, the scope for investment in diagnosis is smaller, and less-sensitive and less-costly strategies are favoured. This study makes clear how the value of treatment can have upstream implications for the value of the diagnostic tests.
DISCUSSION OF THE ECONOMIC EVALUATION
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