
@article{lee_nices_2024,
	title = {{NICE}’s {Pathways} {Pilot}: {Pursuing} {Good} {Decision} {Making} in {Difficult} {Circumstances}},
	issn = {2509-4254},
	shorttitle = {{NICE}’s {Pathways} {Pilot}},
	url = {https://doi.org/10.1007/s41669-024-00490-x},
	doi = {10.1007/s41669-024-00490-x},
	language = {en},
	urldate = {2024-08-20},
	journal = {PharmacoEconomics - Open},
	author = {Lee, Dawn and Burns, Darren and Wilson, Ed},
	month = apr,
	year = {2024},
	file = {Full Text PDF:/home/amy/Zotero/storage/KC24RRPS/Lee et al. - 2024 - NICE’s Pathways Pilot Pursuing Good Decision Maki.pdf:application/pdf},
}

@misc{national_institute_for_health_and_care_excellence_nice_renal_nodate,
	title = {Renal cell carcinoma {Pathways} {Pilot} [{ID6186}]. {In} development [{GID}-{TA11186}]},
	url = {https://www.nice.org.uk/guidance/indevelopment/gid-ta11186},
	author = {{National Institute for Health and Care Excellence (NICE)}},
}

@misc{national_institute_for_health_and_care_excellence_nice_cabozantinib_2024,
	title = {Cabozantinib with nivolumab for untreated advanced renal cell carcinoma. {Technology} appraisal guidance [{TA964}]},
	url = {https://www.nice.org.uk/guidance/ta964},
	author = {{National Institute for Health and Care Excellence (NICE)}},
	month = apr,
	year = {2024},
}

@article{lee_correction_2024,
	title = {Correction: {NICE}’s {Pathways} {Pilot}: {Pursuing} {Good} {Decision} {Making} in {Difficult} {Circumstances}},
	issn = {2509-4254},
	shorttitle = {Correction},
	url = {https://doi.org/10.1007/s41669-024-00499-2},
	doi = {10.1007/s41669-024-00499-2},
	language = {en},
	urldate = {2024-08-20},
	journal = {PharmacoEconomics - Open},
	author = {Lee, Dawn and Burns, Darren and Wilson, Ed},
	month = jun,
	year = {2024},
	file = {Full Text PDF:/home/amy/Zotero/storage/NTDIV6DR/Lee et al. - 2024 - Correction NICE’s Pathways Pilot Pursuing Good D.pdf:application/pdf},
}

@misc{national_institute_for_health_and_care_excellence_nice_taking_nodate,
	title = {Taking a proportionate approach to technology appraisals},
	url = {https://www.nice.org.uk/about/what-we-do/proportionate-approach-to-technology-appraisals},
	urldate = {2024-08-21},
	author = {{National Institute for Health and Care Excellence (NICE)}},
	file = {Taking a proportionate approach to technology appraisals | What we do | About | NICE:/home/amy/Zotero/storage/3F9PRT2V/proportionate-approach-to-technology-appraisals.html:text/html},
}

@misc{lee_treatments_2023,
	title = {Treatments for renal cell carcinoma [{ID6186}]. {Final} {Analysis} {Plan}.},
	url = {https://www.nice.org.uk/guidance/gid-ta11186/documents/assessment-report-3},
	author = {Lee, Dawn and Muthukumar, Madhusubramanian and Robinson, Sophie and Lovell, Alan and Coelho, Helen and Matthews, Justin and Farmer, Caroline and Snowsill, Tristan and Wilson, Edward and Melendez-Torres, G.J},
	month = feb,
	year = {2023},
	file = {Full Text:/home/amy/Zotero/storage/QW2QHJYM/Lee et al. - 2023 - Treatments for renal cell carcinoma [ID6186]. Fina.pdf:application/pdf},
}

@article{national_institute_for_health_and_care_excellence_nice_renal_2024,
	title = {Renal cell carcinoma pathway model report. {Economic} analysis.},
	url = {https://www.nice.org.uk/guidance/ta964/resources/renal-cell-carcinoma-pathway-model-report-pdf-17325260829637},
	author = {{National Institute for Health and Care Excellence (NICE)}},
	month = apr,
	year = {2024},
	file = {Full Text:/home/amy/Zotero/storage/8VMR5XJY/National Institute for Health and Care Excellence (NICE) - 2024 - Renal cell carcinoma pathway model report. Economi.pdf:application/pdf},
}

@article{lee_treatments_2023-1,
	title = {Treatments for renal cell carcinoma [{ID6186}]: {A} {Pathways} {Pilot} {Appraisal} {Assessment} {Report}},
	url = {https://www.nice.org.uk/guidance/ta964/documents/assessment-report},
	journal = {Peninsula Technology Assessment Group (PenTAG)},
	author = {Lee, Dawn and Muthukumar, Madhusubramanian and Lovell, Alan and Farmer, Caroline and Burns, Darren and Matthews, Justin and Coelho, Helen and O'Toole, Brian and Trigg, Laura and Snowsill, Tristan and Barnish, Maxwell and Nikoglou, Thalia and Brand, Amanda and Ahmad, Zain and Abdelsabour, Ahmed and Crathorne, Louise and Robinson, Sophie and Wilson, Edward and Melendez-Torres, G.J},
	month = aug,
	year = {2023},
	file = {Full Text:/home/amy/Zotero/storage/M5VYP8V2/Treatments for renal cell carcinoma [ID6186] A Pa.pdf:application/pdf},
}

@article{delgado_clinical_2021,
	title = {Clinical endpoints in oncology - a primer},
	volume = {11},
	issn = {2156-6976},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085844/},
	abstract = {Clinical endpoints are essential for assessing the safety and efficacy of new cancer therapies. They are used by oncologists to help guide clinical decision making. While overall survival (OS) has frequently been regarded as the “gold standard” primary clinical endpoint, it’s utility is constrained by several disadvantages. The time-consuming nature of trials using OS has led to a recent push to explore surrogate clinical endpoints and their potential to serve as primary clinical endpoints in lieu of OS. Additionally, it is becoming evident that other endpoints add valuable information about quality of life and treatment failure as their use is becoming increasingly prevalent in oncology clinical trials. Without a doubt, the use of clinical endpoints will continue to expand and evolve as new cancer therapies are developed and novel treatments, including immunotherapy, draw interest. This review explores the roles of primary and surrogate clinical endpoints as well as the benefits and drawbacks of each specific endpoint. In addition, it directly compares the unique features of each suggesting some of the specific uses each one fulfills.},
	number = {4},
	urldate = {2024-09-13},
	journal = {American Journal of Cancer Research},
	author = {Delgado, Amanda and Guddati, Achuta Kumar},
	month = apr,
	year = {2021},
	pmid = {33948349},
	pmcid = {PMC8085844},
	pages = {1121--1131},
	file = {PubMed Central Full Text PDF:/home/amy/Zotero/storage/CFG3E9NT/Delgado and Guddati - 2021 - Clinical endpoints in oncology - a primer.pdf:application/pdf},
}

@misc{nih_national_cancer_institute_nci_nci_nodate,
	title = {{NCI} {Dictionary} of {Cancer} {Terms}},
	url = {https://www.cancer.gov/publications/dictionaries/cancer-terms},
	urldate = {2024-09-13},
	author = {{NIH National Cancer Institute (NCI)}},
}

@misc{york_health_economics_consortium_quality_2016,
	title = {Quality of {Life}},
	url = {https://yhec.co.uk/glossary/quality-of-life/},
	journal = {YHEC},
	author = {{York Health Economics Consortium}},
	year = {2016},
}

@article{challapalli_1463p_2022,
	title = {{1463P} {Patterns} of care and outcomes of metastatic renal cell carcinoma ({mRCC}) patients (pts) with bone metastases ({BM}): {A} {UK} multicenter review},
	volume = {33},
	issn = {0923-7534, 1569-8041},
	shorttitle = {{1463P} {Patterns} of care and outcomes of metastatic renal cell carcinoma ({mRCC}) patients (pts) with bone metastases ({BM})},
	url = {https://www.annalsofoncology.org/article/S0923-7534(22)03417-2/fulltext},
	doi = {10.1016/j.annonc.2022.07.1566},
	language = {English},
	urldate = {2024-09-16},
	journal = {Annals of Oncology},
	author = {Challapalli, A. and Ratnayake, G. and McGrane, J. and Frazer, R. and Gupta, S. and Parslow, D. S. and Kingdon, S. J. and Lydon, A. and Sharma, A. and Tuthill, M. and McCusker, C. and Ford, V. and Ferrera, A. and Malik, J. M. and Boh, Z. and Jones, E. and Geldart, T. R. and Nelmes, S. and Brown, J. E. and Bahl, A.},
	month = sep,
	year = {2022},
	note = {Publisher: Elsevier},
	pages = {S1215},
	file = {Full Text PDF:/home/amy/Zotero/storage/IN28MQ7J/Challapalli et al. - 2022 - 1463P Patterns of care and outcomes of metastatic .pdf:application/pdf},
}

@article{ara_populating_2010,
	title = {Populating an {Economic} {Model} with {Health} {State} {Utility} {Values}: {Moving} toward {Better} {Practice}},
	volume = {13},
	issn = {1098-3015},
	shorttitle = {Populating an {Economic} {Model} with {Health} {State} {Utility} {Values}},
	url = {https://www.sciencedirect.com/science/article/pii/S1098301510600903},
	doi = {10.1111/j.1524-4733.2010.00700.x},
	abstract = {Background
The methods used to estimate health-state utility values (HSUV) for multiple health conditions can produce very different values. Economic results generated using baselines of perfect health are not comparable with those generated using baselines adjusted to reflect the HSUVs associated with the health condition. Despite this, there is no guidance on the preferred techniques and little research describing the effect on cost per quality adjusted life-year (QALY) results when using the different methods.
Methods
Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health-state utility profiles (perfect health, no history of CVD, general population) in conjunction with models (minimum, additive, multiplicative) frequently used to approximate scores for health states with multiple health conditions. HSUVs are calculated using the EQ-5D UK preference-based algorithm.
Results
Assuming a baseline of perfect health ignores the natural decline in quality of life associated with age, overestimating the benefits of treatment. The results generated using baselines from the general population are comparable to those obtained using baselines from individuals with no history of CVD. The minimum model biases results in favor of younger-aged cohorts. The additive and multiplicative models give similar results.
Conclusion
Although further research in additional health conditions is required to support our findings, our results highlight the need for analysts to conform to an agreed reference case. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation.},
	number = {5},
	urldate = {2024-09-17},
	journal = {Value in Health},
	author = {Ara, Roberta and Brazier, John E.},
	month = jul,
	year = {2010},
	keywords = {methodology, decision models, health economics methods, health surveys, health-state utility},
	pages = {509--518},
	file = {Full Text:/home/amy/Zotero/storage/HUDKCFWI/Ara and Brazier - 2010 - Populating an Economic Model with Health State Uti.pdf:application/pdf;ScienceDirect Snapshot:/home/amy/Zotero/storage/38PAK4P8/S1098301510600903.html:text/html},
}

@article{ara_using_2011,
	title = {Using health state utility values from the general population to approximate baselines in decision analytic models when condition-specific data are not available},
	volume = {14},
	issn = {1524-4733},
	doi = {10.1016/j.jval.2010.10.029},
	abstract = {BACKGROUND: Decision analytic models in health care require baseline health-related quality of life data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per quality adjusted life years thresholds.
OBJECTIVE: The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition specific data are not available.
METHODS: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status.
RESULTS: More than 45\% of respondents (n = 41,174) reported at least one condition and almost 20\% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses, but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one condition. In these instances, if condition specific data are not available, data from respondents who report they do not have any prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on health-related quality of life may not be constant across ages for all conditions and these relationships may be condition specific. Additional research is required to validate our findings.},
	language = {eng},
	number = {4},
	journal = {Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research},
	author = {Ara, Roberta and Brazier, John E.},
	month = jun,
	year = {2011},
	pmid = {21669378},
	keywords = {Humans, Adult, Female, Male, Middle Aged, Aged, Cohort Studies, England, Population Surveillance, Decision Support Techniques, Health Status Indicators, Health Surveys},
	pages = {539--545},
	file = {Full Text:/home/amy/Zotero/storage/BMWM32XL/Ara and Brazier - 2011 - Using health state utility values from the general.pdf:application/pdf},
}

@misc{nice_decision_support_unit_calculating_nodate,
	title = {Calculating severity shortfall for nice evaluations},
	url = {https://www.sheffield.ac.uk/nice-dsu/tsds/severity-shortfall-tsd},
	urldate = {2024-09-23},
	journal = {The University of Sheffield},
	author = {{NICE Decision Support Unit}},
}

@misc{national_institute_for_health_and_care_excellence_nice_nice_2022,
	title = {{NICE} health technology evaluations: the manual. {Porcess} and methods [{PMG36}]},
	shorttitle = {Overview {\textbar} {NICE} health technology evaluations},
	url = {https://www.nice.org.uk/process/pmg36},
	abstract = {This guide describes the methods and processes, including expected timescales, that NICE follows when carrying out health technology evaluations. The methods and processes are designed to produce robust guidance for the NHS in an open, transparent and timely way, with appropriate contribution from stakeholders. Organisations invited to contribute to health technology evaluation development should read this manual in conjunction with the NICE health technology evaluation topic selection: the manual. All documents are available on the NICE website.},
	language = {eng},
	urldate = {2024-09-23},
	journal = {NICE},
	author = {{National Institute for Health and Care Excellence (NICE)}},
	year = {2022},
	note = {Publisher: NICE},
	file = {Snapshot:/home/amy/Zotero/storage/C2KX8AGM/pmg36.html:text/html},
}

@article{mcgrane_real_2024,
	title = {Real world, multicentre patterns of treatment and survival in metastatic renal cell carcinoma with the {UK} {Renal} {Oncology} {Collaborative} ({UK} {ROC}): {Is} it time to look favourably on first-line immunotherapy containing combinations in all {IMDC} groups?},
	volume = {13},
	copyright = {© 2024 The Author(s). Cancer Medicine published by John Wiley \& Sons Ltd.},
	issn = {2045-7634},
	shorttitle = {Real world, multicentre patterns of treatment and survival in metastatic renal cell carcinoma with the {UK} {Renal} {Oncology} {Collaborative} ({UK} {ROC})},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cam4.7327},
	doi = {10.1002/cam4.7327},
	abstract = {Introduction Clinical trials show improved progression-free survival (PFS) and overall survival (OS) in first-line metastatic renal cell carcinoma (mRCC) patients with immunotherapy containing systemic anti-cancer therapies (SACT). However, in the favourable international metastatic renal cell cancer database consortium (IMDC) group there is no trial evidence for OS benefit despite clear PFS improvement when comparing anti-VEGF tyrosine kinase inhibitor (TKI) monotherapy and (immunotherapy and TKI) IO/TKI combinations. Objective To assess the impact of first-line SACT choice on the clinical outcomes of PFS and OS in mRCC. To evaluate this impact of initial SACT for allcomers and the favourable IMDC group. Methods A multicentre retrospective review of patients who started SACT for mRCC (01/01/2018–30/06/2021) at 17 UK NHS trusts. Patient demographics and IMDC group were analysed. Survival data were compared using Kaplan–Meier curves, and the statistical significance of differences in outcome between the groups was assessed with the log-rank test. Univariable and multivariable Cox proportional hazard modelling estimate the hazard ratios (HRs) for survival outcomes associated with IMDC and treatment subtype. Results One thousand three hundred and nineteen patients were identified with a median age of 64. 294 (22.3\%), 695 (52.7\%) and 321 (24.3\%) were IMDC group favourable, intermediate and poor, respectively. 311 (23.6\%), 197 (14.9\%) and 778 (59\%) patients received checkpoint inhibitor and anti-CTLA4 monoclonal antibody (IO/IO), IO/TKI and TKI first-line SACT across all IMDC groups. Significant PFS improvement favouring IO/TKI versus TKI was demonstrated in allcomers HR = 0.61. In the favourable risk group, Log rank testing demonstrated a significant benefit for IO/TKI over TKI for PFS (HR = 0.60, 95\% CI [0.39, 0.91]) and OS (HR = 0.42, 95\% CI [0.18, 0.99]). Conclusion In this real-world evidence cohort, we have shown OS and PFS benefit with IO/TKI versus TKI in the favourable IMDC risk group. This has not been previously reported from trial outcomes and would support use of front-line IO/TKI in mRCC favourable risk patients.},
	language = {en},
	number = {12},
	urldate = {2024-10-22},
	journal = {Cancer Medicine},
	author = {McGrane, John and Frazer, Ricky and Challapalli, Amarnath and Ratnayake, Gihan and Boh, Zhaung and Clayton, Alison and Chau, Caroline and Sharma, Anand and Elgendy, Manal and Charnley, Natalie and Mohamed, Wael and Kingdon, Sarah and Protheroe, Andrew and Lydon, Anna and Halstead, Anna and Ford, Vicky and Muazzam, Iqtedar and Lee, Dawn and Melendez-Torres, G. J. and Bahl, Amit},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cam4.7327},
	keywords = {favourable risk, immunotherapy, overall survival, renal cell carcinoma},
	pages = {e7327},
	annote = {e7327 CAM4-2023-12-5922.R1},
	file = {Full Text PDF:/home/amy/Zotero/storage/7VXRAUS9/McGrane et al. - 2024 - Real world, multicentre patterns of treatment and .pdf:application/pdf;Snapshot:/home/amy/Zotero/storage/C79Z3N8C/cam4.html:text/html},
}

@article{frazer_real-world_2024,
	title = {Real-world patterns of treatment and response in metastatic renal cell carcinoma: a multicentre {UK}-wide review with {UK} {Renal} {Oncology} {Collaborative} ({UK} {ROC})},
	volume = {3},
	issn = {2949-8201},
	shorttitle = {Real-world patterns of treatment and response in metastatic renal cell carcinoma},
	url = {https://www.esmorwd.org/article/S2949-8201(24)00005-5/fulltext},
	doi = {10.1016/j.esmorw.2024.100027},
	language = {English},
	urldate = {2024-10-22},
	journal = {ESMO Real World Data and Digital Oncology},
	author = {Frazer, R. and McGrane, J. M. and Challapalli, A. and Ratnayake, G. and Malik, J. and Forde, C. and Alam, S. and Jones, E. and Shaheen, A. and Ferrara, A. and Gupta, S. and Moorcraft, S. Y. and Moon, N. and Parslow, D. and Wang, Y. and Walters, S. and Liu, J. and Geldart, T. and Dyke, C. and Reni, A. and Melendez-Torres, G. J. and Bahl, A.},
	month = mar,
	year = {2024},
	note = {Publisher: Elsevier},
	keywords = {drop-off rates, IMDC, immunotherapy, renal cell carcinoma, treatment sequencing},
	file = {Full Text PDF:/home/amy/Zotero/storage/IUVYWSH9/Frazer et al. - 2024 - Real-world patterns of treatment and response in m.pdf:application/pdf},
}

@article{lee_efficacy_2024,
	title = {Efficacy of cabozantinib and sunitinib for the treatment of intermediate/poor risk renal cell carcinoma based upon {UK} real-world data},
	volume = {6},
	issn = {2949-8201},
	url = {https://www.sciencedirect.com/science/article/pii/S2949820124000651},
	doi = {10.1016/j.esmorw.2024.100087},
	abstract = {Background
The purpose of this study was to explore the effectiveness of cabozantinib versus sunitinib for the treatment of first-line metastatic renal cell carcinoma in intermediate/poor risk patients.
Materials and methods
Retrospective review of cases between 1 January 2018 and 30 June 2021 across 17 UK centres. Univariable and multivariable Cox proportional hazards modelling to identify prognostic factors. Inverse probability of treatment weighting, to estimate the causal effect of first-line treatment type.
Results
Cabozantinib patients (n = 106) had poorer risk status, less prior nephrectomy, shorter time to therapy, and more clear cell histology than sunitinib patients (n = 218). More sunitinib patients received a second or third line of subsequent treatment (56\% and 23\% versus 43\% and 13\%). Though there was no significant difference between treatments in overall survival (OS) or progression-free survival (PFS) across models, the difference in PFS bordered on significant in a multipredictor analysis (benefit in favour of cabozantinib; P = 0.06). When the Kaplan–Meier curves were stratified by risk status (intermediate versus poor), patients had similar OS within the risk groups. PFS appeared to differ with poor risk patients performing better on cabozantinib. Inverse probability of treatment weighting analysis showed little difference from the unadjusted results: OS hazard ratio = 1.119 (95\% confidence interval (CI) 0.823-1.521); PFS hazard ratio 0.825 (95\% CI 0.636-1.070) for cabozantinib versus sunitinib.
Conclusions
Our results showed no significant difference in either OS or PFS between treatments. Cabozantinib trended towards improved PFS and reduced OS. Decision-making for tyrosine kinase inhibitor monotherapy should consider later-line treatment options. This analysis is of particular relevance as sunitinib is now off-patent meaning that the cost of a course of treatment has considerably reduced.},
	urldate = {2024-10-22},
	journal = {ESMO Real World Data and Digital Oncology},
	author = {Lee, D. and Melendez-Torres, G. J. and Challapalli, A. and Frazer, R. and McGrane, J. and Bahl, A.},
	month = dec,
	year = {2024},
	keywords = {cabozantinib, IMDC, observational, real-world, renal cell carcinoma, sunitinib, survival, UK ROC},
	pages = {100087},
	file = {ScienceDirect Snapshot:/home/amy/Zotero/storage/6AWDKS2J/S2949820124000651.html:text/html},
}

@article{lee_slipping_2024,
	title = {Slipping {Away}: {Slippage} in hazard ratios over datacuts and its impact on immuno-oncology combination economic evaluations},
	issn = {1098-3015},
	shorttitle = {Slipping {Away}},
	url = {https://www.sciencedirect.com/science/article/pii/S1098301524028602},
	doi = {10.1016/j.jval.2024.09.008},
	abstract = {Objectives
This study examines the impact of slippage in hazard ratios (tending towards the null over subsequent datacuts) for overall survival for combination treatment with a PD-(L)-1 inhibitor and a tyrosine kinase inhibitor (TKI) in advanced renal cell carcinoma (RCC).
Methods
Four trials' Kaplan Meier curves were digitized over several datacuts and fitted with standard parametric curves. Accuracy and consistency of early data projections were calculated versus observed restricted mean survival time (RMST) and fitted lifetime survival from the longest follow-up datacut. The change in economically justifiable price (eJP) was calculated fitting the same curve to both arms, using an assumed average utility of 0.7 and willingness-to-pay threshold of £30,000 per QALY. The eJP represents the lifetime justifiable price increment for the new treatment, including differences in drug, administration and disease-related costs.
Results
Slippage in hazard ratios was observed in trials with longer follow-up, potentially influenced by subsequent PD-(L)-1 use after TKI monotherapy, early stoppage of PD-(L)-1 and development of resistance. Lognormal and log-logistic curves were more likely to over-predict the observed result; Gompertz and gamma under-predicted. Statistical measures for goodness of fit did not reliably predict the RMST. Large differences in incremental mean life years were observed between even the penultimate and final datacuts for the majority of the fitted curves, meaningfully impacting the eJP.
Conclusions
This work demonstrates the challenge in predicting treatment benefits with novel therapies using immature data. Incorporating information on the impact of subsequent treatment is likely to play a key role in improving predictions.},
	urldate = {2024-10-22},
	journal = {Value in Health},
	author = {Lee, Dawn and Ahmad, Zain and Farmer, Caroline and Barnish, Maxwell S. and Lovell, Alan and Melendez-Torres, G. J.},
	month = oct,
	year = {2024},
	keywords = {Hazard ratio, immune-oncology, long-term treatment effectiveness, renal-cell carcinoma, survival extrapolation},
}

@article{lee_impact_2024,
	title = {The {Impact} of the {Approach} to {Accounting} for {Age} and {Sex} in {Economic} {Models} on {Predicted} {Quality}-{Adjusted} {Life}-{Years}},
	issn = {1179-1896},
	url = {https://doi.org/10.1007/s40258-024-00918-9},
	doi = {10.1007/s40258-024-00918-9},
	abstract = {The method used to model general population mortality estimates in cohort models can make a meaningful difference in appraisals; particularly in scenarios involving potentially curative treatments where a prior National Institute for Health and Care Excellence (NICE) appraisal demonstrated that this assumption alone could make a difference of {\textasciitilde}£10,000 to the incremental cost-effectiveness ratio.},
	language = {en},
	urldate = {2024-10-22},
	journal = {Applied Health Economics and Health Policy},
	author = {Lee, Dawn and Hart, Rose and Burns, Darren and McCarthy, Grant},
	month = sep,
	year = {2024},
	file = {Full Text PDF:/home/amy/Zotero/storage/9U6AJL7H/Lee et al. - 2024 - The Impact of the Approach to Accounting for Age a.pdf:application/pdf},
}
