Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine

Student thesis: Phd


Background Precision medicine involves the use of tests, algorithms, or other information to target treatments to improve benefits or avoid harm for patients. Examples of precision medicine represent complex interventions and there are many barriers, or health system capacity constraints (hereafter "capacity constraints"), to their introduction into clinical practice. The assumptions underlying economic evaluations of healthcare interventions do not currently allow for the incorporation and measurement of the impact of capacity constraints. The overall aim of this PhD was to identify and quantify the impact of including capacity constraints in decision-analytic model-based cost-effectiveness analysis to better inform resource allocation decisions and the introduction of precision medicine into clinical practice. Methods This thesis comprised six empirical chapters using different methods. A systematic review of systematic reviews (meta-review) was used to identify existing economic evaluations of precision medicine to determine if and how these studies quantified the impact of capacity constraints. Static and dynamic value of implementation methods were adapted to allow for the varying marginal costs and benefits which may arise due to health system capacity constraints and these methods were applied to a case study in breast cancer. To develop a typology of barriers to the introduction of examples of precision medicine, qualitative interviews were conducted with stakeholders in the implementation of examples of precision medicine in non-small cell lung cancer. A case study example of precision medicine was selected based on these interviews and a base case decision analytic model, replicating a published technology appraisal conducted by the National Institute for Health and Care Excellence (NICE), was created to evaluate the cost-effectiveness of immunohistochemistry (IHC) and fluorescent in-situ hybridisation (FISH) testing for anaplastic lymphoma kinase (ALK) alterations to guide treatment with crizotinib or docetaxel. Three capacity constraints were selected based on the qualitative interviews and these were incorporated into the base case model. The impact of these capacity constraints was quantified using static value of implementation methods. Hypothetical investments to reduce the impact of the capacity constraints were proposed and their potential value was determined using dynamic value of implementation methods. Results The meta-review identified 45 previous reviews of economic evaluations of precision medicine from which a sample of 222 economic evaluations focusing on "test-treat" interventions were selected. Of these, 33 studies qualitatively discussed potential capacity constraints and nine studies attempted to quantify the impact of capacity constraints. It was identified that capacity constraints may impact the marginal costs and benefits of an intervention and therefore its cost-effectiveness. When the static and dynamic value of implementation methods were adapted to allow for this varying marginal cost-effectiveness, it was identified that the way in which examples of precision medicine are implemented would be critical to ensuring a net benefit was achieved for society. From the qualitative interviews, a typology of 17 barriers to the introduction of examples of precision medicine in NSCLC was created. A base case economic evaluation of one of these examples of precision medicine, ALK testing, provided an estimate of the cost-effectiveness of the example in the absence of capacity constraints. The estimated ICER of £38,468 was similar to that produced in the original health technology assessment of crizotinib (£41,554). In the absence of capacity constraints, ALK testing and treatment with crizotinib offered a potential total net monetary benefit of £6,373,887 per year. Incorporating three capacity constraints into this model reduced the estimated cost-effectiveness and total net monetary benefit of AL
Date of Award31 Dec 2020
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorKatherine Payne (Supervisor), William Newman (Supervisor) & Gavin Daker-White (Supervisor)


  • Capacity
  • Economic Evaluation
  • Lung Cancer
  • Precision Medicine
  • Health Economics

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