Analysis of Patient Preferences in Lung Cancer – Estimating Acceptable Tradeoffs Between Treatment Benefit and Side Effects

References

• American Cancer Society. Key statistics for lung cancer; 2019. Available from: https://www.cancer.org/cancer/non-small-cell-lung-cancer/about/key-statistics.html. Accessed July 12,2019.
   Google Scholar
• Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2019;69:7–34. doi:10.3322/caac.21551
   PubMed Web of Science ®Google Scholar
• National Cancer Institut. Surveillance, epidemiology, and end results program; 2019. Available from: https://seer.cancer.gov/statfacts/html/lungb.html. Accessed Jul 12,2019.
   Google Scholar
• Solomon BJ, Mok T, Kim DW, et al. First-line crizotinib versus chemotherapy in ALK-positive lung cancer. N Engl J Med. 2014;371:2167–2177. doi:10.1056/NEJMoa1408440
   PubMed Web of Science ®Google Scholar
• Jiang T, Su C, Ren S, et al. A consensus on the role of osimertinib in non-small cell lung cancer from the AME lung cancer collaborative group. J Thorac Dis. 2018;10:3909–3921. doi:10.21037/jtd.2018.07.61
   PubMed Web of Science ®Google Scholar
• Takeda M, Nakagawa K. First and second-generation EGFR-TKis are all replaced to osimertinib in chemo-naive EGFR mutation-positive non-small cell lung cancer? Int J Mol Sci. 2019;20:146. doi:10.3390/ijms20010146
   PubMed Web of Science ®Google Scholar
• Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372:2018–2028. doi:10.1056/NEJMoa1501824
   PubMed Web of Science ®Google Scholar
• Borghaei H, Paz-Ares L, Horn L, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015;373:1627–1639. doi:10.1056/NEJMoa1507643
   PubMed Web of Science ®Google Scholar
• Reck M, Mellemgaard A. Emerging treatments and combinations in the management of NSCLC: clinical potential of nintedanib. Biologics. 2015;9:47–56. doi:10.2147/BTT.S57356
   Google Scholar
• Scagliotti GV, Gaafar R, Nowak AK, et al. Nintedanib in combination with pemetrexed and cisplatin for chemotherapy-naive patients with advanced malignant pleural mesothelioma (LUME-Meso): a double-blind, randomised, placebo-controlled phase 3 trial. Lancet Respir Med. 2019;7:569–580. doi:10.1016/S2213-2600(19)30139-0
   PubMed Web of Science ®Google Scholar
• Soekhai V, de Bekker-grob EW, Ellis AR, Vass CM. Discrete choice experiments in health economics: past, present and future. Pharmaco Econ. 2019;37:201–226.
   PubMed Web of Science ®Google Scholar
• Jackson Y, Janssen E, Fischer R, et al. The evolving role of patient preference studies in health-care decision-making, from clinical drug development to clinical care management. Expert Rev Pharmacoecon Outcomes Res. 2019;1–14.
   Web of Science ®Google Scholar
• de Bekker-grob EW, Berlin C, Levitan B, et al. Giving patients’ preferences a voice in medical treatment life cycle: the PREFER public-private project. Patient. 2017;10:263–266. doi:10.1007/s40271-017-0222-3
   PubMedGoogle Scholar
• US Food and Drug Administration, Center for Devices and Radiological Health, Center for Drug Evaluation and Research . Patient preference information–voluntary submission, review in premarket approval applications, humanitarian device exemption applications, and de novo requests, and inclusion in decision summaries and device labeling. Guidance for industry, food and drug administration staff, and other stakeholders. 2016.
   Google Scholar
• Janssen EM, Longo DR, Bardsley JK, Bridges JF. Education and patient preferences for treating type 2 diabetes: a stratified discrete-choice experiment. Patient Prefer Adherence. 2017;11:1729–1736. doi:10.2147/PPA.S139471
   PubMed Web of Science ®Google Scholar
• Zhou M, Thayer WM, Bridges JFP. Using latent class analysis to model preference heterogeneity in health: a systematic review. Pharmaco Econ. 2018;36:175–187. doi:10.1007/s40273-017-0575-4
   PubMed Web of Science ®Google Scholar
• Schmidt K, Damm K, Prenzler A, Golpon H, Welte T. Preferences of lung cancer patients for treatment and decision-making: a systematic literature review. Eur J Cancer Care Engl. 2016;25:580–591. doi:10.1111/ecc.12425
   PubMed Web of Science ®Google Scholar
• Muhlbacher AC, Bethge S. Patients’ preferences: a discrete-choice experiment for treatment of non-small-cell lung cancer. Eur J Health Econ. 2015;16:657–670. doi:10.1007/s10198-014-0622-4
   PubMed Web of Science ®Google Scholar
• McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Front. Econom. New York: Academic Press; 1974:105–142.
   Google Scholar
• Clark MD, Determann D, Petrou S, Moro D, de Bekker-grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32:883–902. doi:10.1007/s40273-014-0170-x
   PubMed Web of Science ®Google Scholar
• de Bekker-grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21:145–172. doi:10.1002/hec.1697
   PubMed Web of Science ®Google Scholar
• Bridges JFP, Hauber AB, Marshall D, et al. Conjoint analysis applications in health–a checklist: a report of the ISPOR good research practices for conjoint analysis task force. Value Health. 2011;14:403–413. doi:10.1016/j.jval.2010.11.013
   PubMed Web of Science ®Google Scholar
• Johnson FR, Lancsar E, Marshall D, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health. 2013;16:3–13. doi:10.1016/j.jval.2012.08.2223
   PubMed Web of Science ®Google Scholar
• Hauber AB, Gonzalez JM, Groothuis-Oudshoorn CGM, et al. Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR conjoint analysis good research practices task force. Value Health. 2016;19:300–315. doi:10.1016/j.jval.2016.04.004
   PubMed Web of Science ®Google Scholar
• Bridges JFP, Janssen EM, Ferris A, Dy SM. Project transform: engaging patient advocates to share their perspectives on improving research, treatment and policy. Curr Med Res Opin. 2018;34:1755–1762. doi:10.1080/03007995.2018.1440199
   PubMed Web of Science ®Google Scholar
• Janssen EM, Bridges JFP. Art and science of instrument development for stated-preference methods. Patient. 2017;10:377–379. doi:10.1007/s40271-017-0261-9
   Google Scholar
• Janssen EM, Segal JB, Bridges JFP. A framework for instrument development of a choice experiment: an application to type 2 diabetes. Patient Patient Centered Outcomes Res. 2016;9:465–479. doi:10.1007/s40271-016-0170-3
   Web of Science ®Google Scholar
• Cella DF. The functional assessment of cancer therapy-lung (FACT-L) quality of life instrument. Assess Qual Life Patients Lung Cancer Guide Clin. 1995.
   Google Scholar
• Dow KH, Ferrell BR, Leigh S, Ly J, Gulasekaram P. An evaluation of the quality of life among long-term survivors of breast cancer. Breast Cancer Res Treat. 1996;39:261–273
   PubMed Web of Science ®Google Scholar
• Basch E, Reeve BB, Mitchell SA, et al. Development of the National Cancer Institute’s patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE). J Natl Cancer Inst. 2014;106:dju244–dju244. doi:10.1093/jnci/dju244
   PubMedGoogle Scholar
• National Cancer Institute. Common Terminology Criteria for Adverse Events (CTCAE), Version 4.0. National Cancer Institute; 2009.
   Google Scholar
• Jonker MF, Donkers B, de Bekker-grob EW, Stolk EA. Effect of level overlap and color coding on attribute non-attendance in discrete choice experiments. Value Health J Int Soc Pharmacoecon Outcomes Res. 2018;21:767–771. doi:10.1016/j.jval.2017.10.002
   Web of Science ®Google Scholar
• Bridges JF, Mohamed AF, Finnern HW, Woehl A, Hauber AB. Patients’ preferences for treatment outcomes for advanced non-small cell lung cancer: a conjoint analysis. Lung Cancer. 2012;77:224–231. doi:10.1016/j.lungcan.2012.01.016
   PubMed Web of Science ®Google Scholar
• Bridges JF, Kinter ET, Schmeding A, Rudolph I, Muhlbacher A. Can patients diagnosed with schizophrenia complete choice-based conjoint analysis tasks? Patient. 2011;4:267–275. doi:10.2165/11589190-000000000-00000
   PubMedGoogle Scholar
• Hauber AB, Mohamed AF, Johnson FR, Oyelowo O, Curtis BH, Coon C. Estimating importance weights for the IWQOL-Lite using conjoint analysis. Qual Life Res. 2010;19:701–709. doi:10.1007/s11136-010-9621-9
   PubMed Web of Science ®Google Scholar
• Tsai J-H, Janssen E, Bridges JF. Research as an event: a novel approach to promote patient-focused drug development. Patient Prefer Adherence. 2018;12:673–679. doi:10.2147/PPA.S153875
   PubMed Web of Science ®Google Scholar
• Bridges JFP, Tsai J-H, Janssen E, Crossnohere NL, Fischer R, Peay H. How do members of the duchenne and becker muscular dystrophy community perceive a discrete-choice experiment incorporating uncertain treatment benefit? An application of research as an event. Patient. 2019;12:247–257. doi:10.1007/s40271-018-0330-8
   PubMedGoogle Scholar
• Hess S, Rose JM. Can scale and coefficient heterogeneity be separated in random coefficients models? Transportation. 2012;39:1225–1239. doi:10.1007/s11116-012-9394-9
   Web of Science ®Google Scholar
• Johnson FR, Hauber AB, Ozdemir S. Using conjoint analysis to estimate healthy-year equivalents for acute conditions: an application to vasomotor symptoms. Value Health. 2009;12:146–152. doi:10.1111/j.1524-4733.2008.00391.x
   PubMed Web of Science ®Google Scholar
• Train K, Weeks M. Discrete choice models in preference space and willingness-to-pay space. In: Scarpa R, Alberini A, editors. Applications of Simulation Methods in Environmental and Resource Economics. Netherlands, Dordrecht: Springer; 2005:1–16.
   Google Scholar
• Chaudhary MA, Stearns SC. Estimating confidence intervals for cost-effectiveness ratios: an example from a randomized trial. Stat Med. 1996;15:1447–1458. doi:10.1002/(SICI)1097-0258(19960715)15:13<1447::AID-SIM267>3.0.CO;2-V
   PubMed Web of Science ®Google Scholar
• Briggs AH, Mooney CZ, Wonderling DE. Constructing confidence intervals for cost-effectiveness ratios: an evaluation of parametric and non-parametric techniques using Monte Carlo simulation. Stat Med. 1999;18:3245–3262. doi:10.1002/(SICI)1097-0258(19991215)18:23<3245::AID-SIM314>3.0.CO;2-2
   PubMed Web of Science ®Google Scholar
• Hole AR. A comparison of approaches to estimating confidence intervals for willingness to pay measures. Health Econ. 2007;16:827–840. doi:10.1002/hec.1197
   PubMed Web of Science ®Google Scholar
• McFadden D, Train KE. Mixed MNL models for discrete response. J Appl Econom. 2000;15:447–470. doi:10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
   Web of Science ®Google Scholar
• Hole AR, Kolstad JR. Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment. Empir Econ. 2012;42:445–469. doi:10.1007/s00181-011-0500-1
   Web of Science ®Google Scholar
• Scarpa R, Thiene M, Train K. Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the alps. Am J Agric Econ. 2008;90:994–1010. doi:10.1111/j.1467-8276.2008.01155.x
   Web of Science ®Google Scholar
• Johnson FR, Zhou M. Patient preferences in regulatory benefit-risk assessments: a US perspective. Value Health. 2016;19:741–745. doi:10.1016/j.jval.2016.04.008
   PubMed Web of Science ®Google Scholar
• Mott DJ. Incorporating quantitative patient preference data into healthcare decision making processes: is HTA falling behind? Patient. 2018;12(3):249–252. doi:10.1007/s40271-018-0305-9
   Google Scholar
• Soekhai V, Whichello C, Levitan B, et al. Methods for exploring and eliciting patient preferences in the medical product lifecycle: a literature review. Drug Discov Today. 2019;24:1324–1331. doi:10.1016/j.drudis.2019.05.001
   PubMed Web of Science ®Google Scholar
• Magidson J, Vermunt K. Removing the scale factor confound in multinomial logit choice models to obtain better estimates of preference. Sawtooth software conference; October, 2007.
   Google Scholar
• Raphael MJ, Robinson A, Booth CM, et al. The value of progression-free survival as a treatment end point among patients with advanced cancer: a systematic review and qualitative assessment of the literature. JAMA Oncol. 2019;5:1779. doi:10.1001/jamaoncol.2019.3338
   PubMed Web of Science ®Google Scholar
• Janssen EM, Hauber AB, Bridges JFP. Conducting a discrete-choice experiment study following recommendations for good research practices: an application for eliciting patient preferences for diabetes treatments. Value Health J Int Soc Pharmacoecon Outcomes Res. 2018;21:59–68. doi:10.1016/j.jval.2017.07.001
   Web of Science ®Google Scholar