%% Bibliography file
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@article{avancena_2021,
	title = {Examining {Equity} {Effects} of {Health} {Interventions} in {Cost}-{Effectiveness} {Analysis}: {A} {Systematic} {Review}},
	volume = {24},
	issn = {10983015},
	shorttitle = {Examining {Equity} {Effects} of {Health} {Interventions} in {Cost}-{Effectiveness} {Analysis}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301520344545},
	doi = {10.1016/j.jval.2020.10.010},
	language = {en},
	number = {1},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Avanceña, Anton L.V. and Prosser, Lisa A.},
	month = jan,
	year = {2021},
	pages = {136--143},
}

@article{barendregt_2009,
	author = {Jan J. Barendregt},
	title ={The Half-Cycle Correction: Banish Rather Than Explain It},
	journal = {Medical Decision Making},
	volume = {29},
	number = {4},
	pages = {500-502},
	year = {2009},
	doi = {10.1177/0272989X09340585},
    note ={PMID: 19571330},
	abstract = { The half-cycle correction is often used in discrete Markov models to estimate state membership. This article shows that the correction, in addition to being unintuitive, actually produces the wrong results in many circumstances. These include quality-adjusted life year (QALY) weights and unit costs that differ by cycle. The half-cycle correction is also incompatible with discounting of the obtained stream of state membership. It is furthermore shown that the life table method of estimating state membership obtains correct results under these circumstances and is also much more transparent. The article concludes that the half-cycle correction should be dropped in favor of the life table method. }
}

@article{bonet_2025,
	title = {The {Price} {Effects} of {Biosimilars} in the {United} {States}},
	issn = {10983015},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301525000865},
	doi = {10.1016/j.jval.2025.02.008},
	language = {en},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Jofre-Bonet, Mireia and McGuire, Alistair and Dayer, Victoria and Roth, Joshua A. and Sullivan, Sean D.},
	month = mar,
	year = {2025},
        volume = 28,
        issue = 5,
	pages = {742--750},
}

@article{breslau_2023,
	title = {A review of {HTA} guidelines on societal and novel value elements},
	volume = {39},
	copyright = {http://creativecommons.org/licenses/by/4.0},
	issn = {0266-4623, 1471-6348},
	url = {https://www.cambridge.org/core/product/identifier/S026646232300017X/type/journal_article},
	doi = {10.1017/S026646232300017X},
	abstract = {Abstract 
             
              Objectives 
              Health technology assessment (HTA) organizations vary in terms of how they conduct assessments. We assess whether and to what extent HTA bodies have adopted societal and novel elements of value in their economic evaluations. 
             
             
              Methods 
              After categorizing “societal” and “novel” elements of value, we reviewed fifty-three HTA guidelines. We collected data on whether each guideline mentioned each societal or novel element of value, and if so, whether the guideline recommended the element’s inclusion in the base case, sensitivity analysis, or qualitative discussion in the HTA. 
             
             
              Results 
              The HTA guidelines mention on average 5.9 of the twenty-one societal and novel value elements we identified (range 0–16), including 2.3 of the ten societal elements and 3.3 of the eleven novel value elements. Only four value elements (productivity, family spillover, equity, and transportation) appear in over half of the HTA guidelines, whereas thirteen value elements are mentioned in fewer than one-sixth of the guidelines, and two elements receive no mention. Most guidelines do not recommend value element inclusion in the base case, sensitivity analysis, or qualitative discussion in the HTA. 
             
             
              Conclusions 
              Ideally, more HTA organizations will adopt guidelines for measuring societal and novel value elements, including analytic considerations. Importantly, simply recommending in guidelines that HTA bodies consider novel elements may not lead to their incorporation into assessments or ultimate decision making.},
	language = {en},
	number = {1},
	urldate = {2025-04-20},
	journal = {International Journal of Technology Assessment in Health Care},
	author = {Breslau, Rachel Milstein and Cohen, Joshua T. and Diaz, Jose and Malcolm, Bill and Neumann, Peter J.},
	year = {2023},
	pages = {e31},
}

@article{do_2021,
	title = {Bridging the gap: aligning economic research with disease burden},
	volume = {6},
	issn = {2059-7908},
	shorttitle = {Bridging the gap},
	url = {https://gh.bmj.com/lookup/doi/10.1136/bmjgh-2021-005673},
	doi = {10.1136/bmjgh-2021-005673},
	abstract = {Introduction 
              Cost-effectiveness analysis (CEA) is critical for identifying high-value interventions that address significant unmet need. This study examines whether CEA study volume is proportionate to the burden associated with 21 major disease categories. 
             
             
              Methods 
              We searched the Tufts Medical Center CEA and Global Health CEA Registries for studies published between 2010 and 2019 that measured cost per quality-adjusted life-year or cost per disability-adjusted life-year (DALY). Stratified by geographical region and country income level, the relationship between literature volume and disease burden (as measured by 2019 Global Burden of Disease estimates of population DALYs) was analysed using ordinary least squares linear regression. Additionally, the number of CEAs per intervention deemed ‘essential’ for universal health coverage by the Disease Control Priorities Network was assessed to evaluate how many interventions are supported by cost-effectiveness evidence. 
             
             
              Results 
              The results located below the regression line but with relatively high burden suggested disease areas that were ‘understudied’ compared with expected study volume. Understudied disease areas varied by region. Higher-income and upper-middle-income country (HUMIC) CEA volume for non-communicable diseases (eg, mental/behavioural disorders) was 100-fold higher than that in low-income and lower-middle-income countries (LLMICs). LLMIC study volume remained concentrated in HIV/AIDS as well as other communicable and neglected tropical diseases. Across 60 essential interventions, only 33 had any supporting CEA evidence, and only 21 had a decision context involving a low-income or middle-income country. With the exception of one intervention, available CEA evidence revealed the 21 interventions to be cost-effective, with base-case findings less than three times the GDP per capita. 
             
             
              Conclusion 
              Our analysis highlights disease areas that require significant policy attention. Research gaps for highly prevalent, lethal or disabling diseases, as well as essential interventions may be stifling potential efficiency gains. Large research disparities between HUMICs and LLMICs suggest funding opportunities for improving allocative efficiency in LLMIC health systems.},
	language = {en},
	number = {6},
	urldate = {2025-04-20},
	journal = {BMJ Global Health},
	author = {Do, Lauren A and Synnott, Patricia G and Ma, Siyu and Ollendorf, Daniel A},
	month = jun,
	year = {2021},
	pages = {e005673},
}

@article{enright_2025,
	title = {Use of health technology assessments in specialty drug coverage decisions by {US} commercial health plans},
	volume = {31},
	issn = {2376-1032},
	abstract = {BACKGROUND: Health technology assessment (HTA) involves a formal review of the clinical, economic, and societal implications of health technologies. Internationally, HTA supports decisions regarding access to novel therapeutics. However, the role of HTA in the decision-making processes of US-based health care payers remains unclear.
OBJECTIVE: To assess how frequently US commercial health plans reference HTAs in their specialty drug coverage policies.
METHODS: Using the Tufts Medical Center Specialty Drug Evidence and Coverage database, we reviewed the evidence cited in the publicly available specialty drug coverage policies of 17 US commercial health plans. We assessed the frequency of HTA citations and characterized them by (1) country of origin, (2) publishing organization, (3) whether it addressed a treatment's cost-effectiveness, (4) disease category addressed, and (5) whether it assessed orphan or nonorphan treatments.
RESULTS: HTAs accounted for 450 of the 14,033 citations in our analysis (3.2\%), with the frequency of HTA citations varying across health plans (0.1\% to 7.4\% of cited evidence). Ex-US HTAs were cited more frequently than US-based HTAs (65.3\%). However, a single health plan accounted for the majority of HTA citations (57.1\%) and ex-US citations (76.2\%). Most cited HTAs included cost-effectiveness assessments (78.7\%). The 3 disease categories for which plans most often cited HTAs were neurological disorders (24.8\%), musculoskeletal disorders (21.5\%), and cancers (14.6\%). Health plans cited HTAs for nonorphan drugs more often than for orphan drugs (59.4\%).
CONCLUSIONS: HTAs represented a small portion of the evidence cited by health plans in specialty drug coverage decisions, with significant variation in citation frequency across plans. Plans cited both US and ex-US HTAs, and most cited HTAs included a cost-effectiveness assessment. These findings suggest that health plans may consider the information provided by HTAs when formulating coverage policies.},
	language = {eng},
	number = {3},
	journal = {Journal of Managed Care \& Specialty Pharmacy},
	author = {Enright, Daniel E. and van Duijnhoven, Emma G. and Ollendorf, Daniel A. and Chambers, James D.},
	month = mar,
	year = {2025},
	pmcid = {11871157},
	pmid = {40021468},
	keywords = {Cost-Benefit Analysis, Decision Making, Humans, Insurance Coverage, Insurance, Pharmaceutical Services, Technology Assessment, Biomedical, United States},
	pages = {289--295},
}

@article{espinosa_2024,
	title = {Use of {Cost}-{Effectiveness} {Thresholds} in {Healthcare} {Public} {Policy}: {Progress} and {Challenges}},
	volume = {22},
	issn = {1175-5652, 1179-1896},
	shorttitle = {Use of {Cost}-{Effectiveness} {Thresholds} in {Healthcare} {Public} {Policy}},
	url = {https://link.springer.com/10.1007/s40258-024-00900-5},
	doi = {10.1007/s40258-024-00900-5},
	language = {en},
	number = {6},
	urldate = {2025-04-20},
	journal = {Applied Health Economics and Health Policy},
	author = {Espinosa, Oscar and Rodríguez-Lesmes, Paul and Romano, Giancarlo and Orozco, Esteban and Basto, Sergio and Ávila, Diego and Mesa, Lorena and Enríquez, Hernán},
	month = nov,
	year = {2024},
	pages = {797--804},
}

@misc{dynamicpv,
  title        = {{dynamcem} - A package for {R} to evaluate cost-effectiveness models with dynamic pricing and uptake.},
  author       = {Merck {\& Co.}, {Inc.}},
  url	= {https://github.com/MSDLLCpapers/dynamicpv},
  urldate      = {Accessed: 1 July 2025}
}

@misc{excel_lambda,
  title        = {LAMBDA function},
  author       = {{Microsoft}, {Inc.}},
  year         = 2025,
  url	= {https://support.microsoft.com/en-us/office/lambda-function-bd212d27-1cd1-4321-a34a-ccbf254b8b67},
  urldate      = {Accessed: 1 July 2025}
}

@online{excel_spill,
  title        = {Spilled range operator},
  author       = {Microsoft, Inc.},
  year         = 2025,
  url	= {https://support.microsoft.com/en-us/office/spilled-range-operator-3dd5899f-bca2-4b9d-a172-3eae9ac22efd},
  urldate      = {Accessed: 25 June 2025}
}

@article{flexsurv,
  title = {{flexsurv}: A Platform for Parametric Survival Modeling in {R}},
  author = {Christopher Jackson},
  journal = {Journal of Statistical Software},
  year = {2016},
  volume = {70},
  number = {8},
  pages = {1--33},
  doi = {10.18637/jss.v070.i08},
}

 @article{heemod,
    author = {Antoine Filipovi\'c-Pierucci and Kevin Zarca and Isabelle
      Durand-Zaleski},
    title = {Markov Models for Health Economic Evaluation: The R
      Package heemod},
    journal = {ArXiv e-prints},
    archiveprefix = {arXiv},
    primaryclass = {stat.AP},
    year = {2017},
    eprint = {1702.03252},
    note = {R package version 1.0.2},
    month = {feb},
    adsurl = {https://adsabs.harvard.edu/abs/2017arXiv170203252F},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
  }

@misc{hema_2025,
  author = {{Institute of Clinical and Economic Review}},
    title = {{Health Economics Methods Advisory} Group Selects First Area for Study: Assessing Treatment Benefits Appropriate to Consider in HTA Decision-Making},
  url = {https://icer.org/news-insights/press-releases/health-economics-methods-advisory-group-selects-first-area-for-study-assessing-treatment-benefits-appropriate-to-consider-in-hta-decision-making/},
  note = {Accessed: 17 May 2025}
}

@article{hoyle_anderson_2010,
	title = {Whose {Costs} and {Benefits}? {Why} {Economic} {Evaluations} {Should} {Simulate} {Both} {Prevalent} and {All} {Future} {Incident} {Patient} {Cohorts}},
	volume = {30},
	copyright = {https://journals.sagepub.com/page/policies/text-and-data-mining-license},
	issn = {0272-989X, 1552-681X},
	shorttitle = {Whose {Costs} and {Benefits}?},
	url = {https://journals.sagepub.com/doi/10.1177/0272989X09353946},
	doi = {10.1177/0272989X09353946},
	abstract = {Background. Most health technology economic evaluations simulate only the prevalent cohort or the next incident cohort of patients. They therefore do not capture all future patient-related benefits and costs. Objective. We show how to estimate and aggregate the incremental cost-effectiveness ratios (ICERs) for both currently eligible (prevalent) and future (incident) patient cohorts within the same model-based analysis. We show why, and in what circumstances, the prevalent and incident cohort ICERs are likely to differ. Methods. Algebraic expressions were developed to capture all components of the ICER in hypothetical cohorts of all prevalent patients and future incident patients. Numerical examples are used to illustrate the approach. Results. The ICER for the first (i.e., next) incident cohort is equivalent to the ICER for all future incident cohorts only when the discount rates for costs and benefits are the same; otherwise, when the discount rate for benefits is lower than for costs, the ICER for all future incident cohorts is lower than the ICER for the first incident cohort. Separate simulation of prevalent and incident patients treated for a hypothetical progressive chronic disease shows widely different ICERs according to which patient cohorts were included when the discount rates were equal. Conclusions. In many circumstances, both the prevalent cohort and all future incident cohorts should be modeled. The need for this approach will depend on the likely difference in the ICERs for prevalent and incident patients, the relative size of the 2 types of cohort, and whether costs and benefits are discounted at equal rates.},
	language = {en},
	number = {4},
	urldate = {2025-04-20},
	journal = {Medical Decision Making},
	author = {Hoyle, Martin and Anderson, Rob},
	month = jul,
	year = {2010},
	pages = {426--437},
}

@article{ispor_value_flower,
	title = "Defining elements of value in health care—a health economics approach: an ISPOR Special Task Force report [3]",
	journal = "Value In Health",
	volume = 21,
	doi = "10.1016/j.jval.2017.12.007",
	number = 2,
	author = "Lakdawalla, Darius N and Doshi, Jalpa A and Garrison, Louis P Jr and Phelps, Charles E and Basu, Anirban and Danzon, Patricia M",
	month = "Feb",
	year = 2018,
	pages = {131--139},
}

@article{laube_2024,
	title = {Launch and Postlaunch Price Developments of New Drugs in the US, Germany, and Switzerland},
	doi = {10.1001/jamahealthforum.2024.4461},
	language = {en},
	journal = {JAMA Health Forum},
	author = {Laube, Y. and Serra-Burriel, M. and Glaus, C. C. E. G. and Vokinger, K. N.},
	volume = 5,
        number = 11,
	year = {2024},
	pages = {e244461},
}

@article{lee_2025,
	title = "The Impact of the Approach to Accounting for Age and Sex in Economic Models on Predicted Quality-Adjusted Life-Years",
	author = "Lee, D and Hart, R and Burns, D and McCarthy, G",
	journal = {Applied Health Economics and Health Policy},
	volume = 23,
	number = 1,
	pages = {131--140},
	doi = "10.1007/s40258-024-00918-9",
	month = "Jan",
	year = 2025,
}

@article{lin_2025,
	title = {{US} {Drug} {Pricing} {Patterns} {Before} {Loss} of {Exclusivity}},
	issn = {10983015},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301525001275},
	doi = {10.1016/j.jval.2025.03.008},
	language = {en},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Lin, Ching-Hsuan and Campbell, Jonathan D. and Motyka, James and Cohen, Joshua T.},
	month = apr,
	year = {2025},
        volume = 28,
        number = 6,
	pages = {907--914},
}

@article{mcqueen_2023,
	title = {Incorporating Dynamic Pricing in Cost-Effectiveness Analysis: Are Known Unknowns Valuable?},
	doi = {10.1007/s40273-022-01230-x},
	language = {en},
	urldate = {},
	journal = Pharmacoeconomics,
	author = {McQueen, R. B. and Anderson, K. E. and Levy, J. F. and Carlson, J. J.},
	number = 41,
	year = 2023,
	pages = {321--327},
}

@article{michaeli_2022,
	title = {Value and Price of Multi‑indication Cancer Drugs in the USA, {Germany}, {France}, {England}, {Canada}, {Australia}, and {Scotland}},
	doi = {10.1007/s40258-022-00737-w},
	language = {en},
	journal = {Applied Health Economics and Health Policy},
	author = {Michaeli, Daniel Tobias and Mills, Mackenzie and Kanavos, Panos},
	volume = 20,
	year = 2022,
	pages = {757--768},
}

@article{moreno_2016,
	title = {The value of innovation under value-based pricing},
	volume = 7,
	number = 4,
	doi = {10.3402/jmahp.v4.30754},
	language = {en},
	journal = {Journal of Market Access and Health Policy},
	author = {Moreno, Santiago G and Ray, Joshua A},
	month = Apr,
	year = {2016}
}

@article{neumann_2022,
	title = {Do {Cost}-{Effectiveness} {Analyses} {Account} for {Drug} {Genericization}? {A} {Literature} {Review} and {Assessment} of {Implications}},
	volume = {25},
	issn = {10983015},
	shorttitle = {Do {Cost}-{Effectiveness} {Analyses} {Account} for {Drug} {Genericization}?},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301521016454},
	doi = {10.1016/j.jval.2021.06.014},
	language = {en},
	number = {1},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Neumann, Peter J. and Podolsky, Meghan I. and Basu, Anirban and Ollendorf, Daniel A. and Cohen, Joshua T.},
	month = jan,
	year = {2022},
	pages = {59--68},
}

@article{puls_2024,
	title = {Challenges of {Incorporating} {Life} {Cycle} {Drug} {Pricing} in {Cost}-{Effectiveness} {Models}: {A} {Review} of {Methods} and {Modeling} {Suggestions}},
	volume = {27},
	issn = {10983015},
	shorttitle = {Challenges of {Incorporating} {Life} {Cycle} {Drug} {Pricing} in {Cost}-{Effectiveness} {Models}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301524001219},
	doi = {10.1016/j.jval.2024.03.006},
	language = {en},
	number = {7},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Puls, Mathilde and Horscroft, James and Kearns, Benjamin and Gladwell, Daniel and Church, Edward and Johannesen, Kasper and Malcolm, Bill and Borrill, John},
	month = jul,
	year = {2024},
	pages = {978--985},
}

@article{schottler_2023,
	title = {The {Effect} of the {Drug} {Life} {Cycle} {Price} on {Cost}-{Effectiveness}: {Case} {Studies} {Using} {Real}-{World} {Pricing} {Data}},
	volume = {26},
	issn = {10983015},
	shorttitle = {The {Effect} of the {Drug} {Life} {Cycle} {Price} on {Cost}-{Effectiveness}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301522020447},
	doi = {10.1016/j.jval.2022.06.007},
	language = {en},
	number = {1},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Schöttler, Marcel H. and Coerts, Friso B. and Postma, Maarten J. and Boersma, Cornelis and Rozenbaum, Mark H.},
	month = jan,
	year = {2023},
	pages = {91--98},
}

@article{serra_2024,
	title = {Drug Prices After Patent Expirations in High-Income Countries and Implications for Cost-Effectiveness Analyses},
	doi = {10.1001/jamahealthforum.2024.2530},
	language = {en},
	journal = {JAMA Health Forum},
	author = {Serra-Burriel, M. and Martin-Bassols, N. and Perényi, G. and Vokinger, K. N.},
	volume = 5,
	number = 8,
	year = {2024},
	pages = {e242530},
}

@article{shafrin_2024,
	title = {Valuing the {Societal} {Impact} of {Medicines} and {Other} {Health} {Technologies}: {A} {User} {Guide} to {Current} {Best} {Practices}},
	volume = {27},
	issn = {1558-9544},
	shorttitle = {Valuing the {Societal} {Impact} of {Medicines} and {Other} {Health} {Technologies}},
	doi = {10.1515/fhep-2024-0014},
	abstract = {This study argues that value assessment conducted from a societal perspective should rely on the Generalized Cost-Effectiveness Analysis (GCEA) framework proposed herein. Recently developed value assessment inventories - such as the Second Panel on Cost-Effectiveness's "impact inventory" and International Society of Pharmacoeconomics Outcomes Research (ISPOR) "value flower" - aimed to more comprehensively capture the benefits and costs of new health technologies from a societal perspective. Nevertheless, application of broader value elements in practice has been limited in part because quantifying these elements can be complex, but also because there have been numerous methodological advances since these value inventories have been released (e.g. generalized and risk-adjusted cost effectiveness). To facilitate estimation of treatment value from a societal perspective, this paper provides an updated value inventory - called the GCEA value flower - and a user guide for implementing GCEA for health economics researchers and practitioners. GCEA considers 15 broader value elements across four categories: (i) uncertainty, (ii) dynamics, (iii) beneficiary, and (iv) additional value components. The uncertainty category incorporates patient risk preferences into value assessment. The dynamics category petals account for the evolution of real-world treatment value (e.g. option value) and includes drug pricing trends (e.g. future genericization). The beneficiary category accounts for the fact health technologies can benefit others (e.g. caregivers) and also that society may care to whom health benefits accrue (e.g. equity). Finally, GCEA incorporates additional broader sources of value (e.g. community spillovers, productivity losses). This GCEA user guide aims to facilitate both the estimation of each of these value elements and the incorporation of these values into health technology assessment when conducted from a societal perspective.},
	language = {eng},
	number = {1},
	journal = {Forum for Health Economics \& Policy},
	author = {Shafrin, Jason and Kim, Jaehong and Cohen, Joshua T. and Garrison, Louis P. and Goldman, Dana A. and Doshi, Jalpa A. and Krieger, Joshua and Lakdawalla, Darius N. and Neumann, Peter J. and Phelps, Charles E. and Whittington, Melanie D. and Willke, Richard},
	month = jun,
	year = {2024},
	pmid = {39512185},
	pmcid = {PMC11567015},
	keywords = {Biomedical Technology, Cost-Benefit Analysis, Humans, Technology Assessment, Biomedical, cost-effectiveness analysis, generalized cost-effectiveness analysis, health technology assessment, value assessment},
	pages = {29--116},
}

@article{sullivan_2014,
	title = {Budget {Impact} {Analysis}—{Principles} of {Good} {Practice}: {Report} of the {ISPOR} 2012 {Budget} {Impact} {Analysis} {Good} {Practice} {II} {Task} {Force}},
	volume = {17},
	copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
	issn = {10983015},
	shorttitle = {Budget {Impact} {Analysis}—{Principles} of {Good} {Practice}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301513042356},
	doi = {10.1016/j.jval.2013.08.2291},
	language = {en},
	number = {1},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Sullivan, Sean D. and Mauskopf, Josephine A. and Augustovski, Federico and Jaime Caro, J. and Lee, Karen M. and Minchin, Mark and Orlewska, Ewa and Penna, Pete and Rodriguez Barrios, Jose-Manuel and Shau, Wen-Yi},
	month = jan,
	year = {2014},
	pages = {5--14},
}

@article{vondeling_2018,
	title = {The impact of patent expiry on drug prices: a systematic literature review},
	doi = {},
	language = {en},
	journal = {Applied Health Economics and Health Policy},
	author = {Vondeling, Gerard T and Cao, Qi and Postma, Maarten J and Rozenbaum, Mark H},
	year = {2018},
	pages = {653--660},
}

@article{whittington_2025a,
	title = {Identifying the {Influential} {Dynamic} {Inputs} in {Cost}-{Effectiveness} {Analyses}},
	issn = {10983015},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1098301525022867},
	doi = {10.1016/j.jval.2025.03.016},
	language = {en},
	urldate = {2025-04-20},
	journal = {Value in Health},
	author = {Whittington, Melanie D. and Cohen, Joshua T. and Neumann, Peter J. and Wagner, Tyler D. and Campbell, Jonathan D.},
	month = apr,
	year = {2025},
	pages = {S1098301525022867},
}

@article{whittington_2025b,
	title = {Estimating a {Drug}’s {Price} {After} {Loss} of {Exclusivity} as a {Function} of {Its} {Cost} of {Goods} {Sold}},
	volume = {23},
	issn = {1175-5652, 1179-1896},
	url = {https://link.springer.com/10.1007/s40258-024-00928-7},
	doi = {10.1007/s40258-024-00928-7},
	language = {en},
	number = {1},
	urldate = {2025-04-20},
	journal = {Applied Health Economics and Health Policy},
	author = {Whittington, Melanie D. and Mattingly, T. Joseph},
	month = jan,
	year = {2025},
	pages = {75--83},
}

@article{woods_2024,
	title = {Achieving dynamic efficiency in pharmaceutical innovation: Identifying the optimal share of value and payments required},
	volume = {33},
	doi = {10.1002/hec.4795},
	language = {en},
	number = 4,
	journal = {Health Economics},
	author = {Woods, Beth and Lomas, James and Sculpher, Mark and Weatherly, Helen and Claxton, Karl},
	month = Jan,
	year = {2024},
	pages = {804--819},
}
