NETSCC, HTA 20 December 2012

NETSCC, HTA

20 December 2012

The Health Technology Assessment programme is managed by NETSCC, HTA as part of the NIHR Evaluation, Trials and Studies Coordinating Centre at the University of Southampton.

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The NIHR Evaluation, Trials and Studies Coordinating Centre (NETSCC), based at the University of Southampton, manages evaluation research programmes and activities for the NIHR

Health Technology Assessment Programme tel: +44(0)23 8059 5586 email: [email protected] National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre University of Southampton, Alpha House fax: +44(0)23 8059 5639 web: www.hta.ac.uk Enterprise Road, Southampton, SO16 7NS

Cytisine for smoking cessation

HTA 12/46

Protocol

29 October 2012

1. Title of the project:

What is the clinical and cost effectiveness of cytisine compared with varenicline for smoking cessation?

2. Project lead

The University of Sheffield, School of Health and Related Research (ScHARR)

Dr Joanna Leaviss Research Associate ScHARR University of Sheffield Regent Court 30 Regent Street Sheffield, S1 4DA Tel: 0114 22 20895 Email: [email protected]

3. Background Tobacco smoking is a major cause of a number of chronic diseases, including heart disease and cancers, and is attributed as the leading cause of preventable deaths worldwide (Peto et al. 1996, Mokdad 2004). Despite these statistics, one fifth of adults in the UK are regular cigarette smokers (Allender et al 2008). In 2006-07, smoking-related ill-health cost the NHS £3.3 billion (Scarborough et al 2011). Stopping smoking is known to reduce the risk of smoking-related disease, but it is challenging. Without smoking cessation aids, only between 2% and 5% of quit attempts are successful (Hughes et al 2004, West et al 2000). Smoking cessation strategies have varied success rates. Behavioural interventions designed to encourage people to stop smoking, for example individual, group or telephone counselling, have shown modest efficacy (Motillo et al 2009). A number of pharmacological interventions exist that aid smoking cessation. These include Nicotine Replacement Therapy (NRT), typical antidepressant medications such as buproprion, and nicotine receptor partial agonists e.g. varenicline, cytisine, dianicline.

Nicotine receptor partial agonists offer a pharmacological method to aid smoking cessation. varenicline (Champix or Chantix), and cytisine (Tabex), are included in this class of drug. These drugs may aid smoking cessation by the relief of the symptoms of nicotine withdrawal and cigarette craving through agonist actions, whilst blocking the reinforcing effects of nicotine through an antagonist action (Jorenby et al 20061). Cytisine is a naturally occurring product, extracted from the seeds of the plant Cytisus Laborium. It has been used as an aid to smoking cessation for over 40 years, and is manufactured by the Bulgarian pharmaceutical company Sopharma. Varenicline is a synthentic product developed by Pfizer, with a similar structure to cytisine. Varenicline is licensed for use as an aid to smoking cessation in the US and Canada and across Europe including the UK. Cytisine is considerably less expensive than varenicline, and whilst costs vary between countries, the cost of a course of cytisine is generally about 10 – 20% that of varenicline (West et al 20112).

A recent Cochrane review of nicotine receptor partial agonists as aids to smoking cessation (Cahill et al 20123) showed a modest efficacy for cytisine over placebo in helping people to stop smoking, and a two-fold increase in quit rates for varenicline over placebo. No head to head trials between varenicline and cytisine were identified in the review, and no indirect comparisons of the two drugs have been conducted to date in the absence of such trials.

Concerns have been raised regarding the safety profile of varenicline. The US Food and Drug Administration (FDA) has issued a series of warnings, resulting from post-marketing reports of increased risk of suicidal behaviour or depression, serious adverse cardiac events and

gastrointestinal complaints. A meta-analysis of adverse gastrointestinal events by Leung et al (2011) showed an increased risk after treatment with varenicline. In a review of ten trials, Tonstad (2010) found no evidence of a link between varenicline and serious neuropsychiatric events. Reviews by Singh et al (20114) and Prochaska et al (20125) report conflicting findings, with Singh et al showing an increased risk of serious cardiovascular events after treatment with varenicline, and Prochaska finding no evidence of a link. Cahill et al (20123) found a lack of trial evidence indicating serious adverse events for varenicline. However they do not rule out the possibility of a link. It is beyond the scope of this short report to conduct a systematic review of adverse events for varenicline and cytisine, taking into account long- term observational studies. Therefore this assessment will adopt the same approach to adverse events as the Cahill review, extracting this information from RCTs retrieved through an update of their efficacy search.

This assessment aims to review the efficacy of varenicline and cytisine as an aid to smoking cessation by updating the Cahill et al (2012)3 review, and conducting indirect comparisons where appropriate. A mathematical model to compare the cost-effectiveness of cytisine with varenicline in the context of NHS stopping smoking services will also be developed.

4. Decision problem

4.1 Purpose of the decision to be made The assessment will address the questions: What is the clinical and cost effectiveness of cytisine compared with varenicline for smoking cessation? Specifically, the assessment will: i) Review evidence on the effectiveness and safety of Cytisine in smoking cessation compared with Varenicline; ii) Develop an economic model to estimate the cost effectiveness of Cytisine in the context of NHS smoking cessation services; and iii) provide recommendations based on value of information analyses as to whether a head to head trial of cytisine and varenicline would represent effective use of resources.

4.2 Clear definition of the intervention Cytisine, a nicotine receptor partial agonist, used as an aid to smoking cessation.

4.3 Place of the intervention in the treatment pathway(s) This review will focus on the use of interventions in the treatment of smoking cessation.

4.4 Relevant comparators Varenicline, in any formulation. Varenicline is a nicotine receptor partial agonist used as an aid to smoking cessation. In the absence of data from head to head studies of Cytisine with Varenicline, any comparators (e.g. placebo, Nicotine Replacement Therapy, Buproprion, Dianicline) will be considered that would allow an indirect comparison or network meta- analysis.

4.5 Population and relevant sub-groups Adult smokers.

4.6 Key factors to be addressed 1. Update the Cochrane review of clinical effectiveness and safety for Cytisine compared with Varenicline in helping people to stop smoking. Updated searches from December 2011 to 2012 will be undertaken.

2. Model the cost-effectiveness of Cytisine in the context of NHS smoking cessation services.

3. Make recommendations for commissioning a full trial.

5. Report methods for synthesis of evidence of clinical effectiveness and safety An updated review of the evidence for clinical effectiveness and safety will be undertaken systematically following the general principles recommended in the PRISMA statement. English and non-English language studies will be included. The updated review will include papers from December 2011-2012.

5.1 Population Adult smokers.

5.2 Intervention Cytisine at any dose.

5.3 Comparator Varenicline at any dose.

5.4 Settings Smoking cessation programmes/trials.

5.5 Outcomes 5.5.1 Clinical outcomes. Outcomes relating to clinical effectiveness will follow the same criteria as the existing Cochrane review: 1. The primary outcome is abstinence from smoking: minimum 6 months abstinence. Sustained cessation rates will be used in preference to point prevalence. Biochemically verified cessation rates will be used in preference to self-reported cessation. 2. Adverse events: Any reported adverse events from retrieved RCTs will be recorded. It is beyond the scope of this short report to perform specific searches for adverse events. Therefore this data will be based on adverse events reported in RCTs retrieved for the efficacy search, as in the Cahill 2012 review. Studies of long-term observational data will not be included.

5. 6 Study design Randomised Controlled Trials (RCTs) only.

5.7 Search strategy The search strategy will update the existing Cochrane review and comprise the following main elements: . Searching of electronic databases . Contact with experts in the field . Scrutiny of bibliographies of retrieved papers

The search will update the recent Cochrane Review and rerun their search strategy for trials in the period December 2011-2012, excluding ‘dianicline’ from the interventions. Although dianicline was included in the Cahill searches, development of the drug has been discontinued and will therefore not be included in the comparisons for this report. Additionally, the search will be rerun with a cost effectiveness filter with no date restriction for cost effectiveness literature. All searches will be done by an Information Specialist (AC).

5.8 Databases

The following electronic databases will be searched from inception for published and unpublished research evidence:

. MEDLINE (Ovid) 1950-;

. EMBASE (Ovid) 1980-;

. CINAHL (EBSCO) 1982-;

. The Cochrane Library including the Cochrane Systematic Reviews Database, Cochrane Controlled Trials Register, DARE, HTA and NHS EED databases 1991-;

. Biological Abstracts (via ISI Web of Science) 1969-;

. Science Citation Index (via ISI Web of Science) 1900-;

. Social Science Citation Index (via ISI Web of Science) 1956-;

. EconLit

. Conference Proceedings Citation Index- Science (CPCI-S)- (via ISI Web of Science) 1990-

. UK Clinical Trials Research Network (UKCRN) and the National Research Register archive (NRR);

. Current Controlled Trials;

. Clinical Trials.gov up;

All citations will be imported into Reference Manager software and duplicates deleted.

5.9 Inclusion criteria Titles and abstracts of all unique citations will be screened independently by two reviewers using the inclusion criteria reported in 5.1-5.6 above. Disagreement will be resolved by consensus, or with reference to a third team member when necessary. The full papers of all potentially relevant citations will be retrieved so that an in-depth assessment concerning inclusion could be made. Reference-tracking of all included studies and relevant reviews will also be performed to identify any additional, relevant studies not retrieved by the search of electronic databases.

5.10 Exclusion criteria Studies will be excluded if the focus of the study is not smoking cessation i.e. the trial evaluates the effectiveness of the treatment for other conditions e.g. alcohol dependence, or if the focus of the study is cessation of use of smokeless tobacco. Studies will be excluded which do not report a minimum follow-up of 6 months.

5.11 Data extraction strategy Data will be extracted from all new included studies using the same criteria as the existing Cochrane review. Extracted data will include country and setting; method of selection of participants; definition of smoker used; methods of randomisation and allocation, and blinding of trialists, participants and assessors; demographic characteristics of participants; intervention and control description; outcomes including definition of abstinence used, and biochemical validation of cessation; proportion of participants with follow-up data; any adverse events; sources of funding. All extractions will be checked thoroughly by a second reviewer (EEH). Discrepancies will be resolved by discussion, and with reference to a third team member if necessary.

5.12 Quality assessment strategy The quality assessment of included RCTs will be undertaken using the same criteria as the existing Cochrane review, i.e. risk of bias as assessed using the Cochrane Risk of Bias Tool (Higgins 2011). Critical appraisal of new studies included following the updated search will be performed by one reviewer (JL) and double-checked by a second reviewer (EEH). Discrepancies will be resolved by discussion, with involvement of a third team member if necessary.

5.13 Methods of analysis/synthesis 5.13.1 General modelling approaches Evidence about treatment effects will be synthesised using a network meta-analysis. The analysis will be performed using a Bayesian perspective and the parameters in the model will be estimated using Markov chain Monte Carlo (MCMC) simulation.

Each outcome measure will be analysed using a random (treatment) effects model to allow for heterogeneity in treatment effects between studies. A random effects model assumes that each study provides an estimate of the study-specific treatment effect and that the study- specific treatment effects come from a population of treatment effects with common between- study standard deviation. Direct and indirect evidence about treatment effects will be combined to deliver internally consistent results without breaking the randomisation associated with each study. All analyses will be implemented in WinBUGS.

The network meta-analyses will be conducted following the principles described in the NICE Decision Support Unit (DSU) Evidence Synthesis Technical Support Documents.6

5.13.2 Basic model The outcome measures of interest are the abstinence from smoking and adverse events. For each outcome measure, we will define a likelihood function for the data and will model the treatment effect using a link function . which maps the parameters onto the ∞ range. The model for the treatment effects will be of the form:

. ,,

where is the response in arm of study . The parameters are the study-specific baseline responses in study , which are considered to be fixed effects and treated as nuisance parameters. The parameters , are the study-specific treatment effects of the treatment in arm relative to the baseline treatment (1) in that study. In the random effects model, we will assume the study-specific treatment effects , come from a common distribution with mean and variance , where represents the common between-study standard deviation:

, ~ , .

The baseline (or reference) treatment effect and relative treatment effect will be modelled independently so that the relative treatment effect will not be affected by the assumptions made for the baseline model.7

If multi-arm trials are identified, we plan to use a hierarchical Bayesian model for - comparisons proposed by Lu G. and Ades A. E. (2004)8 to formally model all arms from the multi-arm trials in the network meta-analysis. The approach takes into account the between- arm correlations between parameters based on the assumptions of exchangeability regarding the treatment effects in the network meta-analysis.

5.13.3 Model checking The arm-specific and total residual deviance will be calculated for each model. To formally check a model’s fit, an absolute measure of fit (the overall residual deviance) will be used by comparing the value of the overall residual deviance with the number of independent data points.

6. Report methods for synthesising evidence of cost-effectiveness An economic model will be developed to estimate the cost effectiveness of cytisine in the context of NHS smoking cessation services, based on a review of the literature.

6.1 Identifying and systematically reviewing published cost effectiveness studies The search strategy and sources detailed in Section 5 will be used to identify studies of cost effectiveness. Existing cost effectiveness analyses will also be used to identify sources of evidence to inform structural modelling assumptions and parameter values for the economic model. The quality of economic literature will be assessed using a combination of key components of the British Medical Journal checklist for economic evaluations together with the Eddy checklist on mathematical modelling (see Appendix 1).

6.2 Development of a health economic model An economic evaluation model will be constructed, with the primary outcome of the model being an estimate of the incremental cost per additional quality-adjusted life year (QALY) gained associated with Cytisine for smoking cessation. The time horizon of our analysis will be a patient’s lifetime (up to 100 years) in order to reflect the long-term consequences of smoking cessation, morbidity and mortality. The perspective will be that of the National Health Service and Personal Social Services; costs and QALYs will be discounted at 3.5%. (NICE 2008)

The model structure will be based on an existing and widely used or reported Markov model.9-19 The model will follow a hypothetical cohort of smokers after making a single quit attempt. The cohort will be distributed across age categories and smoking states (smoker, short-term quitter, long-term quitter) which are associated with probabilities for smoking- related morbidities (lung cancer, COPD, stroke, CHD, asthma exacerbations). Probability of death will be a function of age, smoking status and current health states.

Given the anticipated resources provided for this research it is expected that the costs and utility data reported in Hind et al12 will be used, and inflated where necessary, for the consequences following smoking relating morbidities. The transition probabilities within the model will also be taken from Hind et al.12 Whilst this is a slight limitation, this is unlikely to affect the key conclusions from this report regarding the relative cost-effectiveness of varenicline and cytisine.

The uncertainty around parameter estimates will be modelled by the use of appropriate distributions around the central estimates. This will allow probabilistic sensitivity analysis to be undertaken on the model results.

Value of information analyses will be undertaken to establish whether a direct head to head trial of cytisine versus varenicline would represent a cost-effective use of resources. This would be potentially undertaken in three stages. The first stage involves the calculation of the expected value of perfect information (EVPI) (Claxton 1996). If the value produced appears to be greater than the cost for which an RCT comparing the efficacies of the two interventions could be undertaken, then the second stage would be performed.

The second stage would estimate the expected value of partial perfect information (EVPPI) (Felli 199820) jointly on the efficacies of varenicline and cytisine. If the value produced appears to be above the cost for which an RCT comparing the efficacies of the two interventions could be undertaken, then the third stage would be performed.

The third stage involves the calculation of the expected value of sample information (EVSI) (Ades 200421). This value explicitly evaluates the potential inaccuracy associated with trials of smaller sizes, contrasting with EVPPI which assumes that the information is perfect and thus in essence is derived from a trial of infinite size. The team have previously published manuscripts evaluating EVPPI (Stevenson 201022) and EVSI (Stevenson 201123).

7. Expertise in this TAR team

• TAR Centre: The ScHARR Technology Assessment Group (ScHARR-TAG) undertakes reviews of the effectiveness and cost effectiveness of healthcare interventions for the NHS R&D Health Technology Assessment Programme on behalf of a range of policy makers in a short timescale, including the National Institute for Health and Clinical Excellence. A list of our publications can be found at: http://www.sheffield.ac.uk/scharr/sections/heds/collaborations/scharr-tag/reports Much of this work, together with our reviews for the international Cochrane Collaboration, underpins excellence in healthcare worldwide.

 Team members’ contributions:

Joanna Leaviss, Research Associate, ScHARR: has experience of undertaking systematic reviews. JL will lead the project and will undertake the systematic reviewing. JL will coordinate the review process, protocol development, abstract assessment for eligibility, quality assessment of studies, data extraction, data entry, data analysis.

Emma Everson-Hock, Research Fellow, ScHARR: has extensive experience of undertaking systematic reviews and will be involved in the protocol development, data extraction, data entry and data analysis for the clinical effectiveness and adverse events review.

Matt Stevenson: Professor in Health Technology Assessment, ScHARR: has extensive experience in constructing mathematical models used within health technology assessments. He will primarily advise WS in constructing the economic model and interpreting the results. He will be actively involved in the value of information analyses.

William Sullivan PhD student in Health Economics : will be involved in the design and construct of the economic model. He will also parameterise and operate the economic model and interpret its results.

John Stevens, Director, Centre for Bayesian Statistics in Health Economics: has worked in ScHARR since 2006 and has extensive experience in the application of Bayesian statistics and methods of evidence synthesis for the National Institute for Health and Clinical Excellence (NICE), the National Institute for Health Research (NIHR) Health Technology Assessment programme and for pharmaceutical companies to support submissions to NICE

and other reimbursement bodies. He previously worked as a Statistical Science Director for AstraZeneca and has worked in the pharmaceutical industry for 25 years. He is a member of NICE Appraisal Committee C.

Kate Ren, Research Associate: has worked in ScHARR since 2011. She obtained a PhD in Probability and Statistics specialising in Bayesian methods in clinical trial design at the University Of Sheffield. Kate has also worked as a trial statistician at Leeds CTRU. Her current work includes conducting meta-analysis for evidence synthesis and developing new methods in health economic evaluation using Bayesian methods.

Anna Cantrell, Systematic Reviews Information Officer, ScHARR: has experience of undertaking literature searches for the ScHARR Technology Assessment Group systematic reviews and other external projects. AC will be involved in developing the search strategy and undertake the electronic literature searches.

Andrea Shippam, Programme Administrator: will assist in the retrieval of papers and in preparing and formatting the report.

 Clinical and expert advisors:

Professor Peter Hajek has an extensive track record of research in smoking behaviour and smoking cessation, with over 260 publications on psychological treatments and on smoking cessation.

Dr Michael Ussher is an internationally recognised researcher in smoking cessation. His current research includes randomised trials of improved ways of helping smokers to stop, national surveys and cohort studies of smoking behaviour, qualitative studies on the process of stopping smoking and laboratory studies on interventions for managing cigarette withdrawal symptoms. He has published around 60 peer reviewed articles related to smoking cessation and is the author of two reviews in the Cochrane Tobacco Module. He was Chair of the TSC for the MRC trial of Cytisine led by Professor Robert West, UCL.

Dr Sally Hope has been a GP Principal since 1986-2012 at the Woodstock Surgery, Oxfordshire. She has a Diploma of Medical Hypnotherapy which she uses to help people to stop smoking.

8. Competing interests of authors

The authors do not have any competing interests.

Professor Peter Hajek has received research funding from and provided consultancy to manufacturers of stop-smoking medications. He has no links with manufacturers of cytisine tablets. Neither SH nor MU has any competing interests.

9. Timetable/milestones The project is expected to run from Milestone Draft protocol 29 October 2012 Final protocol 15 December 2012 Start review 1 January 2013 Progress report 28 February 2013 Assessment report 29 March 2013

Appendix 1: Critical appraisal checklist for economic evaluations using key components of the British Medical Journal checklist for economic evaluationError! Bookmark not defined. together with the Eddy checklist on mathematical models employed in technology assessments.Error! Bookmark not defined.

Title Authors Year Modelling assessments should include: Yes/No 1 A statement of the problem; 2 A discussion of the need for modelling vs. alternative methodologies 3 A description of the relevant factors and outcomes; 4 A description of the model including reasons for this type of model and a specification of the scope including; time frame, perspective, comparators and setting. Note: n=number of health states within sub-model 5 A description of data sources (including subjective estimates), with a description of the strengths and weaknesses of each source, with reference to a specific classification or hierarchy of evidence; 6 A list of assumptions pertaining to: the structure of the model (e.g. factors included, relationships, and distributions) and the data; 7 A list of parameter values that will be used for a base case analysis, and a list of the ranges in those values that represent appropriate confidence limits and that will be used in a sensitivity analysis; 8 The results derived from applying the model for the base case; 9 The results of the sensitivity analyses; unidimensional; best/worst case; multidimensional (Monte Carlo/parametric); threshold. 10 A discussion of how the modelling assumptions might affect the results, indicating both the direction of the bias and the approximate magnitude of the effect; 11 A description of the validation undertaken including; concurrence of experts; internal consistency; external consistency; predictive validity. 12 A description of the settings to which the results of the analysis can be applied and a list of factors that could limit the applicability of the results; 13 A description of research in progress that could yield new data that could alter the results of the analysis

11. References

1. Jorenby, D.E., Hays, J.T., Rigotti, N.A., Azoulay, S., Watsky, E.J., Williams, K.E. et al. Efficacy of varenicline, an alpha4beta2 nicotinic acetylcholine receptor partial agonist, vs placebo or sustained-release bupropion for smoking cessation: a randomized controlled trial.[Erratum appears in JAMA. 2006 Sep 20;296(11):1355]. JAMA 2006; 296(1):56-63.

2. West, R., Zatonski, W., Cedzynska, M., Lewandowska, D., Pazik, J., Aveyard, P. et al. Placebo-controlled trial of cytisine for smoking cessation. The New England Journal of Medicine 2011; 365:1193-1200.

3. Cahill, K., Stead, L.F., Lancaster, T. Nicotine receptor partial agonists for smoking cessation. [Review][Update of Cochrane Database Syst Rev. 2011;(2):CD006103; PMID: 21328282]. Cochrane Database of Systematic Reviews 2012; 4:CD006103.

4. Singh, S., Loke, Y.K., Spangler, J.G., Furberg, C.D. Risk of serious adverse cardiovascular events associated with varenicline: a systematic review and meta- analysis. [Review]. CMAJ Canadian Medical Association Journal 2011; 183(12):1359- 1366.

5. Prochaska, J.J., Hilton, J.F. Risk of cardiovascular serious adverse events associated with varenicline use for tobacco cessation: systematic review and meta-analysis. [Review]. BMJ 2012; 344:e2856.

6. Dias, S., Welton, N.J., Sutton, A.J., Ades, A.E. NICE DSU Technical support document 2: A generalised linear modelling framework for pairwise and network meta-analysis of randomised controlled trials. 2011; available from http://www.nicedsu.org.uk

7. Dias, S., Welton, N.J., Sutton, A.J., Ades, A.E. NICE DSU Technical support document 5:Evidence synthesis in the baseline natural history model. 2011; available from http://www.nicedsu.org.uk

8. Lu, G., Ades, A.E. Combination of direct and indirect evidence in mixed treatment comparisons. Statistics in Medicine 2004; 23[20], 3105-3124.

9. Annemans, L., Nackaerts, K., Bartsch, P., Prignot, J., Marbaix, S. Cost effectiveness of varenicline in Belgium, compared with bupropion, nicotine replacement therapy, brief counselling and unaided smoking cessation: a BENESCO Markov cost-effectiveness analysis. Clinical Drug Investigation 2009; 29(10):655-665.

10. Hoogendoorn, M., Welsing, P., Rutten-van Molken, M.P. Cost-effectiveness of varenicline compared with bupropion, NRT, and nortriptyline for smoking cessation in the Netherlands. Current Medical Research & Opinion 2008; 24(1):51-61.

11. Linden, K., Jormanainen, V., Linna, M., Sintonen, H., Wilson, K., Kotomaki, T. Cost effectiveness of varenicline versus bupropion and unaided cessation for smoking cessation in a cohort of Finnish adult smokers (Structured abstract). Current Medical Research and Opinion 2010; 26:549-560.

12. Hind, D., Tappenden, P., Peters, J., Kenjegalieva, K. Varenicline in the management of smoking cessation: a single technology appraisal. Health Technology Assessment (Winchester, England) 2009; 13(Suppl 2):9-13.

13. Bae, J.Y., Kim, C.H., Lee, E.K. Evaluation of cost-utility of varenicline compared with existing smoking cessation therapies in South Korea. Value in Health 2009; 12 Suppl 3:S70-S73.

14. Bolin, K., Wilson, K., Benhaddi, H., de, N.E., Marbaix, S., Mork, A.C. et al. Cost- effectiveness of varenicline compared with nicotine patches for smoking cessation-- results from four European countries. European Journal of Public Health 2009; 19(6):650-654.

15. Bolin, K., Mork, A.C., Wilson, K. Smoking-cessation therapy using varenicline: the cost-utility of an additional 12-week course of varenicline for the maintenance of smoking abstinence. Journal of Evaluation in Clinical Practice 2009; 15(3):478-485.

16. Bolin, K., Mork, A.C., Willers, S., Lindgren, B. Varenicline as compared to bupropion in smoking-cessation therapy--cost-utility results for Sweden 2003. Respiratory Medicine 2008; 102(5):699-710.

17. Howard, P., Knight, C., Boler, A., Baker, C. Cost-utility analysis of varenicline versus existing smoking cessation strategies using the BENESCO Simulation model: application to a population of US adult smokers. Pharmacoeconomics 2008; 26(6):497- 511.

18. Knight, C., Howard, P., Baker, C.L., Marton, J.P. The cost-effectiveness of an extended course (12+12 weeks) of varenicline compared with other available smoking cessation strategies in the United States: an extension and update to the BENESCO model. Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research 2010; 13(2):209-214.

19. Vemer, P., Rutten-van Molken, M.P. Crossing borders: factors affecting differences in cost-effectiveness of smoking cessation interventions between European countries. Value in Health 2010; 13(2):230-241.

20. Felli, J.C., Hazen, G.B. Sensitivity analysis and the expected value of perfect information. Med Decis Making 1998; 18(1):95-109.

21. Ades, A.E., Lu, G., Claxton, K. Expected value of sample information calculations in medical decision modeling. Med Decis Making 2004; 24(2):207-227.

22. Stevenson, M.D., Scope, A., Sutcliffe, P.A. The cost-effectiveness of group cognitive behavioral therapy compared with routine primary care for women with postnatal depression in the UK. Value Health 2010; 13(5):580-584.

23. Stevenson, M.D., Jones, M.L. The cost effectiveness of a randomized controlled trial to establish the relative efficacy of vitamin K1 compared with alendronate. Med Decis Making 2011; 31(1):43-52.