Lapatinib and trastuzumab in combination with an aromatase inhibitor for the first-line treatment of metastatic hormone receptor-positive breast cancer which over-expresses human epidermal growth factor 2 (HER2): a systematic review and economic analysis

Aetiology

After gender, the strongest risk factor for breast cancer is age. The incidence of breast cancer increases with age, doubling every 10 years until menopause, after which the rate of increase slows. Breast cancer is rare under the age of 20 years.

Genetic and hormonal risk factors have also been identified in the aetiology of breast cancer,2,3 and women with a family history of breast cancer have an increased risk of developing the disease. 4 Mutations in some genes can increase the risk of developing breast cancer. Mutations of the breast cancer 1 (BRAC1) gene, which belongs to a class of genes known as tumour suppressor genes, account for 2% of breast cancers, where the risk is as high as 85% by the age of 35 years. 5 Breast cancer 2 (BRCA2) mutations account for up to 1% of breast cancers, with a 60% chance of breast cancer. Other gene mutations contributing less frequently to familial breast cancer include mutations in tumour protein 53 (p53), MutS Homolog1 (MSH1), MutS Homolog2 (MSH2) and phosphatase tensin homologue (PTEN).

Higher concentrations of some endogenous hormones appear to increase breast cancer risk. 6 Early age at menarche, late natural menopause, later age at first full-term pregnancy and never breastfeeding are all associated with an increased risk of breast cancer,6 whereas childbearing and higher numbers of full-term pregnancies increase protection. 6 Use of exogenous hormones such as oral contraception, oestrogen replacement therapy and combined endocrine therapy increase the risk of breast cancer, as do other factors such as breast density (a risk factor independent of endogenous hormones), a body mass index of 25+ in post-menopausal women, moderate-to-heavy alcohol intake and a sedentary lifestyle. 6

Pathology and prognosis

There are several prognostic factors that are taken into account by clinicians when deciding on treatment options and making a clinical prognosis. 7 These include age, tumour size, histological type, nuclear grade, histological grade, number of metastatic axillary lymph nodes and clinical stage. Patients with stage IV disease are classified as having mBC according to the tumour/nodes/metastasis staging system developed and maintained by the American Joint Committee on Cancer8 and the Union for International Cancer Control. 9

Hormone receptor status and human epidermal growth factor 2 (HER2) status are two other predictive factors that are taken into consideration in estimating the prognosis of patients with breast cancer. As noted above, many breast cancer tumours are stimulated to grow and change by ERs and PgRs. Tumours that express either oestrogen receptor-positive (ER+) or progesterone receptor-positive (PgR+) are commonly referred to as being hormone receptor positive (HR+), and patients with HR+ breast cancer generally have an improved prognosis compared with those who are hormone receptor negative (HR–). Overviews of hormonal therapy, at least in the adjuvant therapy of early breast cancer,10,11 suggest that, where such evidence is sufficiently well defined, it is the ER rather than the PgR activity that is the most useful prognostic factor. More recently it has been discovered that over-expression of ErbB2 protein (also known as HER2), which is a member of the epidermal growth factor receptor family, and/or amplification of the HER2 gene results in an abnormally high number of HER2 genes per cancer cell, which results in cancer cells growing and dividing more quickly. Thus, human epidermal growth factor 2-positive (HER2+) breast cancer is considered to be an aggressive disease and there is growing evidence that the prognosis of HER2+ patients is generally poor, whether or not they are HR– or HR+. It should be emphasised that prior to this understanding of the role of HER2, trials did not routinely present data on this subgroup of patients.

Both HR+ tumours and HER2+ tumours are determined by immunohistochemistry (IHC). 5 Fluorescence in situ hybridisation (FISH) can also be used to measure HER2 expression by measuring the number of gene copies present. An IHC score of ≥ 3 or a FISH amplification of > 2.1 confirms a HER2+ status. An IHC of > 2 is usually confirmed by FISH. 5 Biological markers such as HER2 are also used as a predictor of prognosis and as a guide to therapy.

In England and Wales, 80%, 72% and 64% of people diagnosed with breast cancer live for at least 5 years, 10 years and 20 years after diagnosis, respectively. 12 Although therapeutic innovations have provided modest improvements in survival rates over the past two decades, mBC remains an incurable disease and the aim of treatment is to prolong survival and palliation. 13 Following a diagnosis of mBC, the average length of survival has been reported to be 12 months for those receiving no treatment,14 compared with 18–24 months for those receiving chemotherapy, a figure reduced by up to 50% for patients who are HER2+. 15

Epidemiology

Breast cancer is the most common cancer in the UK with 48,034 new cases diagnosed in 2008, 99% (47,693) being in women. 16 Accounting for almost one-third (31%) of all new cases of cancer in women in the UK, the lifetime risk of breast cancer for a woman is one in nine. 16

There is little regional variation in breast cancer rates in the UK,17 although there appears to be geographical variation within Europe. Breast cancer is one of the few cancers to show a clear trend of increasing rates from the most to least deprived groups,16 with rates in the most deprived groups around 20% lower than in the most affluent. 16 The European age-standardised incidence rate (EASR) for women has increased by 5% from 114 per 100,000 in 1998 to 120 per 100,000 in 2007, with the number of cases rising from 40,377 to 45,695, an increase of 13%. The EASR has been projected to increase from 119 per 100,000 in 2000–4 to 124 per 100,000 in 2020–4, with the average number of new cases per year rising from 41,900 to 55,700 over the same time period. Analysis of breast cancer survival by level of deprivation has, however, consistently shown higher survival for more affluent women. 18

UK data on breast cancer by stage of disease are not routinely collected and so neither the incidence nor the prevalence of mBC in the UK is known. However, assuming that 25% of all women diagnosed with breast cancer have mBC, of whom 6% are HR+/HER2+ [based on 2008 and 2009 data obtained from the Clatterbridge Centre for Oncology NHS Foundation Trust (Nicky Thorp, Clatterbridge Centre for Oncology NHS Foundation Trust, June 2010, personal communication)], approximately 700 patients each year may be diagnosed with HR+/HER2+ mBC in the UK. An independent estimate from 2008 data derived from the IMS Oncology Analyzer™ (IMS Health^®, Plymouth Meeting, PA, USA) obtained by GlaxoSmithKline19 has estimated there may be around 450 new cases of HR+/HER2+ mBC each year in the UK, whereas Roche20 has estimated the number to be nearer to 1000 based on data from Dybdal et al. 21 and their own market research.

Impact of health problem

The impact of a diagnosis of mBC breast cancer on a patient is both physiological and psychological,22 affecting not only the patients but also their families and wider social network. Physical ill-health can stem from both the disease and disease treatment. The National Institute for Health and Clinical Excellence (NICE) Clinical Guideline No. 81 (CG81)23 gives guidance for the management of complications such as lymphoedema, fatigue and metastases. Adequate rehabilitation is vital as women may be less productive after treatment for the disease. 24 The psychological impact on the patient can be debilitating, including depression and fear of loss of autonomy,25 sexuality and body image. 26

Lapatinib

Lapatinib (LAP) (Tyverb^®/Tykerb^®, GlaxoSmithKline) inhibits the tyrosine kinase components of the epidermal growth factor receptors (ErbB1 and ErbB2), implicated in the growth of various tumours. 27 LAP belongs to a group of medicines called protein kinase inhibitors that work by blocking enzymes known as protein kinases. Protein kinases can be found in some receptors on the surface of cancer cells including HER2. HER2, a receptor for epidermal growth factor, is involved in stimulating the cells to divide uncontrollably. By blocking these receptors, LAP helps to control cell division.

The most common side effects of LAP are loss of appetite, diarrhoea, nausea, vomiting, rash and fatigue. Monitoring of left ventricular function and for pulmonary toxicity should be carried out regularly. Monitoring of liver function should be performed before treatment and at monthly intervals. 28 The manufacturer has advised caution in the use of LAP in patients with moderate-to-severe hepatic impairment and severe renal impairment. Pregnancy should be avoided and breastfeeding discontinued during treatment with LAP.

Lapatinib is an orally active drug given once per day and is available as 250-mg tablets.

Currently, LAP is licensed in combination with capecitabine (Xeloda^®, Roche) for patients with advanced or metastatic disease with progression following prior therapy, which must have included anthracyclines and taxanes and therapy with trastuzumab (TRA) (Herceptin^®, Roche) in the metastatic setting. 29 It is not, however, recommended for first-line treatment by NICE.

In June 2010, the European Medicines Agency (EMA) granted conditional approval for the use of LAP in combination with an aromatase inhibitor (AI) for the first-line treatment of post-menopausal women with HR+/HER2+ mBC. 30,31

Trastuzumab

Trastuzumab is a recombinant humanised immunoglobulin G1 (IgG1) monoclonal antibody directed against HER2. It is administered by intravenous (i.v.) infusion, the regimen and dose is dependent on several clinical factors including the patient’s weight, other medications and stage of disease. It is commonly administered every 3 weeks, with the infusion taking approximately 30–90 minutes each time.

The most common side effects of TRA are fatigue and diarrhoea. Recent clinical trial data suggest that patients require a left ventricular ejection fraction (LVEF) of > 55% for treatment with TRA and the summary of product characteristics for TRA states that cardiac monitoring is required every 12 weeks during treatment. However, the optimal frequency of cardiac monitoring in the clinical practice setting is not universally agreed. 32

Trastuzumab should be used with caution in patients with symptomatic heart failure, a history of hypertension, coronary artery disease and uncontrolled arrhythmias. Pregnancy should be avoided during treatment and breastfeeding should be avoided during treatment and for 6 months after. 28

Trastuzumab is currently licensed in the UK for the following indications:33

1. the treatment of early breast cancer which over-expresses HER2

2. in combination with paclitaxel or docetaxel (Taxotere^®, Sanofi-Aventis), for mBC in patients with HER2+ tumours who have not received chemotherapy for mBC and in whom anthracycline treatment is inappropriate

3. in combination with an AI, for mBC in post-menopausal patients with HR+/HER2+ tumours not previously treated with TRA

4. as a monotherapy for mBC in patients with HER2+ tumours who have received at least two chemotherapy regimens including, where appropriate, an anthracycline and a taxane; women with ER+ breast cancer should also have received endocrine therapy.

The AG contacted the EMA for clarification about point 3 above, as the interpretation of this licence varied among NHS clinicians. In particular, it was not clear whether or not TRA was indicated for a woman who had been given TRA during the treatment of early breast cancer, who subsequently progressed to mBC. The EMA responded by stating that TRA was licensed for use in mBC in TRA-naive patients. Of note, at the time the trials were recruiting, the use of TRA for patients with early or advanced breast cancer was relatively rare. In its submission, Roche also state that the majority (76%) of patients who present for mBC have not previously received TRA for adjuvant therapy based on its own market research,20 although it is not clear over which time period this research was conducted.

Aromatase inhibitors

Aromatase inhibitors are not, per se, one of the technologies under assessment in this appraisal. However, they are being assessed in combination with LAP and TRA and are one of the comparators. NICE issued guidance regarding the use of AIs in 2006. 34 During this appraisal, the Appraisal Committee ‘agreed that there is insufficient evidence to conclude that any one AI (used within the licensed indications) or treatment strategy is more clinically effective than another’. As such, the AIs considered in this technology appraisal are assumed to be equally clinically effective. However, in practice only letrozole (LET) (Femara^®, Novartis) and anastrozole (ANA) (Arimidex^®, AstraZeneca) are commonly used as first-line treatments for women with mBC (and they may also be offered as a second-line treatment), with exemestane (EXE) (Aromasin^®, Pharmacia & Upjohn, Kalamazoo, MI, USA) being mostly used as a second-line treatment.

Aromatase inhibitors are a form of endocrine therapy and act predominantly by blocking the conversion of androgens to oestrogens in the peripheral tissues. As such, they are classified as antioestrogen therapies. AIs are classified into irreversible steroidal inhibitors (e.g. EXE) and non-steroidal inhibitors (e.g. ANA and LET), the latter inhibiting the enzyme by reversible competition.

Letrozole is indicated as adjuvant treatment of HR+ early breast cancer in post-menopausal women, advanced breast cancer in post-menopausal women (including those in whom other endocrine therapy has failed), early invasive breast cancer in post-menopausal women after standard adjuvant tamoxifen (TAM) therapy and pre-operative treatment in post-menopausal women with localised HR+ breast cancer to allow subsequent breast-conserving surgery.

Cautions and contraindication include the avoidance of use during pregnancy and breastfeeding. Avoidance has also been advised in severe hepatic impairment, whereas caution has been advised if creatinine clearance is < 10 ml/minute.

Anastrozole is indicated as adjuvant treatment of ER+ early invasive breast cancer in post-menopausal women, adjuvant treatment of ER+ early breast cancer in post-menopausal women following 2–3 years of TAM therapy and in advanced breast cancer in post-menopausal women which are ER+ or responsive to TAM. 28

Caution has been advised for the use of ANA in patients susceptible to osteoporosis; bone mineral density should be measured before treatment and at regular intervals during treatment. ANA is contraindicated in pre-menopausal women. Its use should also be avoided in patients with moderate-to-severe hepatic impairment and renal impairment where creatinine clearance is < 20 ml/minute. As with LET, it should also be avoided in pregnancy and breastfeeding.

Common side effects of ANA include hot flushes, vaginal dryness, vaginal bleeding, hair thinning, anorexia, nausea, vomiting, diarrhoea, headache, arthralgia, bone fractures and rash (including Stevens–Johnson syndrome). 28

Exemestane is indicated as adjuvant treatment of ER+ early breast cancer in post-menopausal women following 2–3 years of TAM therapy and in advanced breast cancer in post-menopausal women where endocrine therapy has failed.

As with other AIs, EXE is contraindicated in pre-menopausal women and should be avoided in pregnant and breastfeeding women. Caution in its use is advised in patients with renal and hepatic impairment. Common side effects include nausea, vomiting, abdominal pain, dyspepsia, constipation, anorexia, dizziness, fatigue, headache, depression, insomnia, hot flushes, sweating, alopecia and rash.

Interventions

The following two interventions are being considered:

• LAP + AI

• TRA + AI.

Population including subgroups

The population of interest is patients with mBC receiving first-line treatment who must:

• have HR+ tumours, and

• have tumours over-expressing the ErbB2 receptor, i.e. HER2+.

Relevant comparators

For LAP + AI, the relevant comparators are:

• AIs alone

• TRA + AI.

For TRA + AI, the relevant comparators are:

• AIs alone

• LAP + AI.

Outcomes

The NICE scope identified the following relevant outcomes:

• overall survival (OS)

• progression-free survival (PFS)

• time to progression (TTP)

• response rate, which (although not specified in the scope) may further be broken down to:

  1. – overall response rate (ORR)

  2. – complete response (CR)

  3. – partial response (PR)

• AEs

• clinical benefit rate (CBR)

• health-related QoL.

Key issues

It is important to note that the following criteria were to be fulfilled a priori.

• Only trials that measure clinical effectiveness in the population of interest were to be included in the systematic review, i.e. women must have mBC, have tumours that are HR+/HER2+ and have had no prior treatment for mBC.

  1. – Women were to be considered to have HR+ breast cancer if they had ER+ or PgR+ tumours.

• Where head-to-head comparisons do not exist, indirect comparisons were to be attempted.

• Cost-effectiveness of treatments was to be expressed in terms of incremental cost per quality-adjusted life-year (QALY) gained.

• The time horizon for estimating clinical effectiveness and cost-effectiveness was to be sufficiently long to reflect any differences in costs or outcomes between the technologies being compared.

• Costs were to be considered from an NHS and Personal Social Services (PSS) perspective.

Identification of studies

Randomised controlled trials (RCTs) were identified by searching major electronic medical databases including MEDLINE, EMBASE and the Cochrane Library. The search strategy was broad and not limited to RCTs. Information on studies in progress, unpublished research or research reported in the grey literature were sought by searching a range of relevant databases including the National Research Register and Controlled Clinical Trials. In addition, bibliographies of previous reviews and retrieved articles were searched for further studies. The search strategy used for MEDLINE is presented in Appendix 1. The same search strategies were used to identify economic evaluations.

Further attempts to identify studies were made by contacting clinical experts and examining the reference lists of all retrieved articles. The manufacturers’ submissions (MS) were assessed for unpublished data.

Inclusion and exclusion criteria

Two reviewers (NF/MM) independently screened all titles and abstracts. Full-paper manuscripts of any titles/abstracts that were considered relevant by either reviewer were obtained where possible. The relevance of each study was assessed (NF/MM) according to the criteria in Table 1. Studies that did not meet the criteria were excluded and their bibliographic details were listed alongside reasons for their exclusion. These are listed in Appendix 2. Any discrepancies were resolved by consensus.

Data abstraction strategy

Data were extracted by one reviewer (MM) using a standardised data extraction form in Microsoft Word 2007 (Microsoft Corporation, Redmond, WA, USA) and checked independently by a second reviewer (NF). Disagreements were resolved by discussion.

Critical appraisal strategy

The quality of the individual clinical effectiveness studies was assessed according to criteria based on the CRD’s guidance for undertaking reviews in health care. 38 The assessment of risk of bias was conducted independently by both reviewers (MM/NF). Disagreements were resolved through discussion.

Methods of data synthesis

The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. The possible effects of study quality on the effectiveness data and review findings are discussed.

It was intended by the Assessment Group (AG) that meta-analyses would be conducted in which direct evidence would be pooled using a standard meta-analysis39 and, where a direct comparison between LAP + AI and TRA + AI was not possible, by indirect comparisons. 40 However, the AG considered it inappropriate to conduct either of these analyses, as discussed further in the next section.

Quantity and quality of research available

Identification of studies

Once duplicates were removed, a total of 2069 references were identified (Figure 1); a scan of the titles and abstracts resulted in 11 potential records. 41–51 Four of these citations48–51 reporting on two trials [the efficacy and safety of lapatinib combined with letrozole (EGF30008)49 trial and the efficacy and safety of trastuzumab combined with anastrozole (TAnDEM)50 trial] met the inclusion criteria and a further trial [efficacy and safety of letrozole combined with trastuzumab (eLEcTRA)52 trial], reported only as a conference abstract, was also suitable for inclusion following information passed on to the AG by Roche at the NICE consultation meeting in February 2010. Thus, three trials were included in the systematic review. Additional data on these trials were submitted to NICE from the manufacturer of LAP (GlaxoSmithKline19) and the manufacturer of TRA (Roche20), including the relevant clinical study report (CSR) for the EGF3000849 and the TAnDEM. 50 Of the seven excluded citations, three45–47 were excluded either because they did not examine LAP or TRA in combination with an AI or because it was a conference report in relation to the TAnDEM. 50 Four citations41–44 were excluded because they could not be obtained. Each was a Physician Data Query (identified through The Cochrane Library) relating to the three included trials and indexed prior to the final study publication dates. In their submissions, both Roche and GlaxoSmithKline identified additional studies that they utilised as indirect evidence. The majority of these trials had also been identified by the AG’s search, but as none were limited to the HR+/HER2+ mBC population (or at least did not include subgroup analysis on the HR+/HER2+ population), they did not meet the review inclusion criteria. Reasons outlining all of the excluded citations, including those identified and included by the manufacturers, are given in Appendix 2.

FIGURE 1.

Identification of eligible studies.

Assessment of effectiveness

Meta-analysis (Roche)

The fixed-effect standard meta-analyses undertaken by Roche20 examined PFS and were conducted for ANA versus TAM (two trials68,69) and ANA versus megestrol acetate (Magace^®, Bristol-Myers Squibb) (two trials70,71). There were insufficient trials to conduct meta-analyses for any other comparisons, such as LAP + LET versus LET or TRA + AI versus AI.

For the meta-analysis, forest plots for HR for individual studies and pooled studies were presented. The I² statistic was calculated to assess the potential heterogeneity between studies. The studies68–71 included in these meta-analyses appeared to be associated with statistical and clinical heterogeneity. No significant differences were found for PFS between treatment groups in either meta-analysis.

Given that ANA was being compared with TAM or megestrol acetate, and given that it was unclear how many patients were HR+/HER2+, the AG believes that the relevance of these analyses to the current appraisal is limited. They were, however, relevant to the Roche submission20 because the results from these meta-analyses were used in its indirect comparison analyses.

Indirect comparisons analyses

Both manufacturers performed indirect comparisons analyses, although different approaches were employed, as summarised in Table 15.

A complex network meta-analysis using the methods described by Puhan et al. 75 was planned by GlaxoSmithKline,19 but was not possible because of the lack of data for the outcomes of interest: OS and PFS/TTP. Thus, adjusted indirect comparisons analyses were performed for single outcomes as available, using the methods and principles as described by Bucher et al. 73 and incorporating data from five studies; the EGF3000849 and the TAnDEM50 trials were included as well as one study comparing LET (2.5 mg/day) with TAM (20 mg/day)76 and two studies comparing ANA (1 mg/day) with TAM (20 mg/day). 68,69 The eLEcTRA52 study was not included in the GlaxoSmithKline analyses as it was published only as an abstract and the AG agrees with the manufacturer’s argument that a lack of data from this trial justifies its exclusion.

The findings for both OS and PFS/TTP presented by the manufacturer suggest that there are no significant differences between any of the interventions for OS. Both LAP + LET and TRA + LET result in significantly improved outcomes for PFS/TTP when compared with ANA, LET and TAM. For reasons discussed below, the AG believes these findings should be treated with extreme caution and so the findings are not presented in this report.

Roche20 employed an indirect network meta-analyses based on a Bayesian approach,74 in which a number of different analyses were performed for OS (base case of 12 trials49,50,68–71,77–82) and PFS (base case of seven trials49,50,68–70,78,81). A number of assumptions were made and tested by sensitivity analyses. These included an assumption that PFS = TTP (which enabled four additional trials52,77,79,80 to be considered) and that OS findings for the TAnDEM50 trial based on the RPSFT adjustment should be used in the base case. In addition, for every outcome, the assumption that AIs hold a ‘class effect’ (i.e. LET = ANA, as suggested by clinical experts and as found in a head-to-head trial of second-line ANA vs LET82) was tested. This assumption related to the mixed HER2 status population (i.e. the population in which the proportion of patients with HER2+ breast cancer was unknown, as in the aforementioned ANA vs LET trial82). The mixed HER2 population was chosen because the HR+/HER2+ population was too specific to allow the inclusion of any trials other than the EGF30008,49 the TAnDEM50 and the eLEcTRa trials. 52

The findings presented by the manufacturer in which a ‘class effect’ was assumed for AIs and which were derived from the EGF3000849 and the TAnDEM50 for OS and PFS and from the EGF30008,49 the TAnDEM50 and the eLEcTRa52 for PFS/TTP suggest that there are no significant differences between LAP + LET and TRA + ANA for OS, PFS or PFS/TTP. For reasons discussed below, the AG believes these findings should be treated with extreme caution and so findings are not presented in this report.

Aside from the fact that the EGF3000849 and the TAnDEM50 trials were too dissimilar in terms of patient populations, the AG believes that both the manufacturers’ indirect comparisons analyses had one other major limitation, namely that the basic requirement for indirect comparisons with regard to exchangeability of relative treatment effect between trials in the two MS could not be assumed. This is a limitation recognised by the manufacturers themselves19,20 and is amplified when patient population characteristics are considered. Crucially, it was unknown how many patients with HR+/HER2+ mBC were included in the trials. Only three trials49,50,52 presented data for patients with HR+/HER2+ mBC. The other trials68–71,76,77,78,82–90 included patients of mixed/unknown status. The importance of missing data on HR+/HER2+ status is twofold. Firstly, patients with HR+/HER2+ mBC are the population of interest to this review and, as Roche has acknowledged (MS, p. 18):20 ‘All results should be treated with extreme caution when applied to the HR+/HER2+ population as there is no evidence base capable of informing this analysis in the population specified by the decision problem’. Secondly, both the EGF3000849 and the eLEcTRA52 trials suggest that the effects of LET in patients with HR+/HER2+ mBC tumours are significantly compromised when compared with those with HR+/HER2– mBC. Thus, the indirect comparisons analyses may be overstating the benefit of AIs and, if so, there is a need to adjust for the results based on HER2+ status. However, given that the proportion of such patients is unknown, such adjustments are currently impossible. It is important to note, as Roche has also stated (MS, p. 18):20 ‘…understanding of HER2 was not fully developed at the period when most of the evidence base identified was formed as many of the trials conducted were not stratified for HER2 positivity and it is clearly plausible that an imbalance in this strong indicator of extremely poor prognosis could have biased the estimates of relative efficacy generated’.

Roche20 also acknowledges a number of additional limitations to its indirect comparisons analyses, namely ‘the low number of trials by pairwise comparison, the heterogeneity in the length of follow-up observed in the selected studies and the different methods used to adjust for cross-over in the individual studies’ (MS, p. 17). A final limitation is the fact that not all trials included patients receiving first-line treatment. In fact, two trials78,82 were second line, including the trial by Rose et al. ,82 which was a key trial for suggesting a ‘class effect’ for AIs. The AG believes that pooling trials with different lines of treatment is inappropriate and misleading; thus, these results should be interpreted with caution.

In summary, given the limitations described above, the AG believes that conducting indirect comparisons analyses with the limited data available is inappropriate. Therefore, any findings generated from these analyses should be treated with caution.

Review of published cost-effectiveness studies

Full details of the search strategy conducted by the AG and the methods used for selecting evidence are presented in Chapter 3, Quantity and quality of research available. The AG concluded that none of the 107 economics studies identified from the electronic searches were eligible for inclusion in the literature review as they did not include any of the relevant interventions (LAP + AI or TRA + AI). The authors of the GlaxoSmithKline MS noted that ‘no economic evaluations of lapatinib plus an AI were identified’ (MS, p. 100). The authors of the Roche MS stated that, although they summarised the characteristics and results of five studies,92–96 ‘four were of poor relevance to the decision problem as they were not in the population of relevance and were not set in the UK’ (MS, p. 209). The only study that Roche deemed to be relevant to the review was the poster by Hastings et al. ,96 which is discussed below.

The AG notes that the poster presented at American Society of Clinical Oncology (ASCO) conference (June 2010) by Hastings et al. 96 is relevant to the technologies under assessment. It is noted that the authors of this poster are employees of GlaxoSmithKline, yet the poster was only discussed in the MS submitted by Roche.

Conclusions of the review of existing cost-effectiveness evidence

There is no relevant, currently available, published cost-effectiveness evidence to describe the use of LAP + LET or TRA + ANA in women who are HR+/HER2+ with mBC.

Overview of submitted economic evaluation and economic model

The purpose of the manufacturer’s model is to assess the cost-effectiveness of first-line treatment with LAP + LET in HR+/HER2+ patients with mBC. In the MS, the combination of LAP + LET is compared with the following interventions: LET monotherapy, TRA + ANA and ANA monotherapy. A decision-analytic model was developed by the manufacturer to estimate PFS, OS, lifetime costs of treatment of mBC and QALYs. The model schema is presented in Figure 2. The model features three health states: alive and no progression, alive with progression and dead. The manufacturer estimates costs from the perspective of the NHS and PSS and health outcomes in terms of life-years, progression-free life-years (PFLYs), post-progression life-years (PPLY) and QALYs; variables are estimated daily for 10 years. The economic evaluation has a time horizon of 10 years and both costs and benefits are discounted at 3.5% per annum. The manufacturer’s reference case adequately reflects the NICE reference case97 (Table 17).

FIGURE 2.

The structure of the model submitted by GlaxoSmithKline.

Summary and critique: clinical effectiveness data

Summary and critique: costs and resource use

The manufacturer estimates the following costs for each treatment strategy: acquisition and administration of medications, patient monitoring, treatment of AEs, other costs during PFS and PPS and total costs. The key cost parameters used in the model are summarised in Table 18.

The economic model makes use of pre- and post-progression cost data from the Remak and Brazil study;100 however, the manufacturer does not comment on the relevance and/or generalisability of the assumptions employed in this study to HR+/HER2+patients with mBC in England and Wales.

In summary, the AG notes that in the MS, the methods used by the manufacturer to identify, measure and value cost items are not fully described. The AG notes that further information is provided in the manufacturer’s accompanying cost-effectiveness report. Table 19 identifies key costs which could have been discussed in further detail by the manufacturer.

Summary and critique: utilities

Utility values for PFS without AEs were estimated using data from the EGF3000849 trial on the Functional Assessment of Cancer Therapy-General Scale (FACT-G)101 plus Breast Cancer subscale (FACT-B)19 and an algorithm was used to map from the FACT-G to patient preference-based utilities. 102,103 The pre-progression utility value used in the model was 0.86.

In the EGF0008 trial,49 FACT assessments were routinely completed by patients only until withdrawal of study medications (i.e. typically at disease progression). This means that post-progression utility values for patients are largely unavailable, and the manufacturer states that the generalisability of the values that are available is uncertain. In order to identify a utility decrement for PD to apply to patients with PD, the manufacturer used the results of a study by Lloyd et al. 104 of societal preferences for different stages of mBC in the UK. The absolute reduction in utility compared with no progression used in the model was 0.23; this means that PP utility value can be no higher than 0.62.

Disutility values from grades 3 or higher AEs were obtained from published and unpublished sources,104–106 and where no data were available, assumptions were made. The utility decrements employed in the economic model include nausea (0.1), vomiting (0.1), diarrhoea (0.1), alopecia (0.11), asthenia/fatigue/lethargy (0.12), and skin and nail disorders (0.15).

The AG notes that the manufacturer does not sufficiently describe the results of the FACT assessments from the EGF30008 trial,49 nor does the manufacturer adequately describe (or test the sensitivity of) the mapping exercise undertaken. Therefore, it is difficult to comment on the usefulness of the PFS utility values.

For PPS, the AG agrees with the manufacturer that the paper by Lloyd et al. 104 describing UK-based societal preferences is relevant to health-care decision-making in the UK. However, as (1) the health states described in the Lloyd et al. 104 paper were derived from literature reviews, exploratory interviews with physicians and an oncology focus group made up of specialist nurses and (2) the health states were gender neutral and there was no mention of ‘cancer’ in the health-state descriptions, the AG is also very aware that health-state descriptions and the valuations of the general public may not fully reflect the experiences of patients with cancer nor the true preferences of the general public.

Summary and critique: results

The manufacturer presents detailed summaries of costs and outcomes (PFLYs, PPLYs, Life-years and QALYs) for the following regimens: LAP + LET, LET monotherapy, TRA + ANA and ANA monotherapy. Base-case results for the pair-wise comparisons are reported in Table 20. The results show that LAP + LET is not cost-effective compared with any of the AIs. LAP + LET appears to be cost-effective compared with TRA + ANA; however, as there is much uncertainty about the reliability of the indirect comparison results, these results are not considered by the AG to be meaningful.

Summary and critique: sensitivity analysis and probabilistic sensitivity analysis

The AG notes that 51 scenarios were examined by the manufacturer using sensitivity analysis. The results of the sensitivity analysis are presented in table 41 of the MS. For the comparison with LET monotherapy, the incremental cost per QALY is in the range of £41,877 to LAP + LET being dominated by LET monotherapy. The cost per QALY gained versus ANA monotherapy ranges from £38,170 to £378,674. For the comparison with TRA + ANA, the range is LAP + LET dominating the comparator to a cost per QALY estimate of £45,106. The approach to sensitivity analysis adopted by the manufacturer makes it is difficult for the AG to acquire any real insight into the true drivers affecting the size of the incremental cost-effectiveness ratios (ICERs).

A summary of the probabilistic sensitivity analysis (PSA) is presented in Figure 3 and the cost-effectiveness acceptability curve (CEAC) is shown in Figure 4. The CEAC shows that, at a cost-effectiveness threshold of £30,000 per QALY gained, the probability of LAP + LET being cost-effective is very low (< 25%) compared with any AI and low (approximately 50%) compared with TRA + ANA.

FIGURE 3.

Incremental cost-effectiveness ratios for LAP + LET versus comparators.

FIGURE 4.

Cost-effectiveness acceptability curve for LAP + LET versus LET, TRA + ANA and ANA.

Summary and critique: end-of-life treatment criteria

The AG notes that the manufacturer has not requested that LAP + LET be considered by NICE as an end-of-life treatment.

Conclusions of the Assessment Group

From the information presented in the MS, the AG agrees with the manufacturer that LAP + LET is not cost-effective when compared with LET.

The AG also considers that the methods used in the indirect analysis undertaken by the manufacturer are unreliable and concludes that the ICERs derived from the remaining comparisons (LAP + LET vs TRA + ANA; LAP + LET vs ANA) are not meaningful.

GlaxoSmithKline did not make a case for LAP to be considered as an end-of-life treatment.

Overview of submitted economic evaluation and economic model

The purpose of the manufacturer’s model is to assess the cost-effectiveness of first-line treatment with TRA + ANA in HR+/HER2+ patients with mBC. In the MS, the combination of TRA + ANA is compared with the following interventions: ANA monotherapy, LAP + LET and LET monotherapy. An area under the curve (AUC) model was designed to calculate the present value of the health outcomes and NHS/PSS costs attributable to each possible treatment option calculated. The model schema is presented in Figure 5. The model features three health states (PFS, PD and death) and has a cycle length of 1 month. The manufacturer estimates costs from the perspective of the NHS/PSS and health outcomes in terms of life-years gained (LYG) and QALYs. The economic evaluation has a time horizon of 15 years and both costs and benefits are discounted at 3.5% per annum (implemented monthly). The manufacturer’s economic evaluation adequately reflects the NICE reference case97 (Table 21).

FIGURE 5.

The structure of the model submitted by Roche.

Summary and critique: clinical effectiveness data

Summary and critique: resource use and costs

The manufacturer presents a detailed and comprehensive description of resource use and costs used in the economic model. The cost categories are presented as follows: monthly drug costs, treatment duration, administration and monitoring costs, pharmacy preparation, response assessment, cardiac monitoring, PFS best supportive care, AEs, progressed disease costs and end-of-life costs. The key cost parameters used in the economic model are summarised in Table 22. In terms of costs, the key difference between the treatment regimens is the acquisition costs of the drugs (LAP and TRA are much more expensive than the AIs), followed by administration costs (i.v. TRA is much more expensive to administer than the other oral drugs) and finally, the costs of ‘PD, BSC and second-line treatment’ (post-PFS costs are higher for the patients taking LAP and TRA).

The AG notes that the economic evaluation in the MS uses a 3-weekly schedule for TRA; although this is not the weekly schedule used in the TAnDEM trial,50 the AG agrees that the 3-weekly schedule is typically used in clinical practice in England and Wales.

Table 23 summarises the key costs that the AG believes the manufacturer could have considered in more detail in the MS.

Summary and critique: utilities and adverse events

The TAnDEM50 conducted by Roche did not collect data using a generic health-utility instrument. In order to estimate utility values for HR+/HER2+ patients, the manufacturer underook a focused review of the literature, identified 20 relevant studies (1996–2009) and presented utility values from six of these published studies (MS, p. 247). However, as no studies were identified as being relevant specifically to HR+/HER2+ patients, the manufacturer made a decison to use the assumptions of Winstanley and Murray in the recent breast cancer publication23 and to apply utilities as identified by Cooper et al. ;109 this decision was made to ensure alignment with the most recent relevant piece of research23 commissioned by NICE in breast cancer. In the sensitivity analysis, the manuacturer made use of the utility values cited by Hastings et al. 96 in the indirect comparison of LAP + LET versus TRA + ANA. The values used by Hastings et al. 96 were derived via mapping FACT-G101 data collected in the EGF3000849 trial to European Quality of Life-5 Dimensions (EQ-5D). Both sets of values are shown in Table 22. Disutility from AEs is not a feature of the economic model developed by Roche.

The Cooper et al. 109 paper pools utilities from many different sources (all derived from oncology nurses using the standard gamble technique). In contrast, the AG notes that the paper by Lloyd et al. ,104 identified by the manufacturer, asks 100 members of the general public to rank health states using the standard gamble technique to determine utility values. As the study by Lloyd et al. 104 is a large preference study designed to obtain UK-based societal preferences for distinct stages of mBC, the AG considers the paper by Lloyd et al. ,104 with caveats previously mentioned, to be the most useful evidence available that could help to inform the decision problem.

In the Roche model, only grade 3 or grade 4 AEs are considered. In the MS it is assumed that the AEs recorded for TRA + ANA are the same for LAP + LET and that the AEs recorded for ANA can be applied to LET. For comparison of TRA and LAP this seems unlikely as episodes of diarrhoea are reported more often for LAP patients. The AG also notes that the costs of several of the AEs listed in the MS (e.g. anaemia, cardiac failure, hypercalcaemia and hypertension) are not the cost inputs used in the economic model (‘AE cost data’). In summary, the AG is of the opinion that the AE costs used in the economic model are underestimated and require revision to make them reliable.

Results: summary and critique

The manufacturer presents detailed summaries of estimated costs and outcomes (time in PFS, time in PD, life-years, QALYs in PFS, QALYs in PD and total QALYs) for the following regimens: TRA + ANA, LAP + LET, ANA monotherapy and LET monotherapy (Table 24). As there are four regimens of interest, the manufacturer has chosen to represent the results of the economic evaluation in terms of the efficiency frontier. The efficiency frontier links the regimens that are not dominated. Figure 6 shows that, using this approach, LAP + LET does not lie on the efficiency frontier and that the key comparison is between TRA + ANA versus LET. When TRA + ANA is compared with LET, the ICER is estimated at approximately £54,336 per QALY gained. When TRA + ANA is compared with LAP + LET, it appears to be cost-effective.

FIGURE 6.

Cost-effectiveness plane.

Note: although ANA is, technically speaking, dominated by LET, it is only marginally more expensive, and in this diagram the separation between ANA and LET is indistinguishable.

Summary and critique: sensitivity analysis and probabilistic sensitivity analysis

End-of-life treatment criteria: summary and critique

This section provides an overview and critique of the manufacturer’s case for TRA + ANA as an end-of-life maintenance treatment for patients mBC. The NICE end-of-life treatment criteria have three key points:

• the treatment is indicated for patients with a short life expectancy, normally < 24 months, and

• there is sufficient evidence to indicate that the treatment offers an extension to life, normally of at least an additional 3 months, compared with current NHS treatment, and

• the treatment is licensed or otherwise indicated for small patient populations.

Conclusions of the Assessment Group

From the information presented in the MS, the AG agrees with the manufacturer that TRA + ANA is not cost-effective when compared with ANA. The AG also considers that the methods used in the indirect analysis undertaken by the manufacturer are unreliable and concludes that the ICERs derived from the remaining comparisons (TRA + ANA vs LET; TRA + ANA vs LAP + LET) are not meaningful.

The manufacturer submitted a case for TRA + ANA to be considered as an end-of-life treatment for women with HR+/HER2+ mBC. The AG agrees that TRA + ANA meets the criteria as a treatment for patients with a short life expectancy and that it extends life by an additional 3 months when compared with current NHS treatment. However, the AG makes no comment on whether or not the criterion of a small patient population is met.

Methods: common model features and parameters

Model design

A common model structure has been adopted for both de novo cost-effectiveness analyses (Figure 7) and, wherever possible, has been implemented using the same parameter values. The de novo model employs outcome data derived from the relevant clinical trial in the form of Kaplan–Meier estimated survival values augmented by projected survival estimates calibrated against the observed data. The preferred approach uses PFS and PPS estimates directly as the basis for calculating expected OS in each group of the RCT. PFS and PPS values then furnish the information required to calculate all components of health service costs and also to estimate the expected future patient utility. These survival estimates are calculated separately for each date on which a resource is expected to be used (e.g. when prescriptions are dispensed or when a hospital visit or test takes place), avoiding the need for a general model cycle or for mid-cycle corrections.

FIGURE 7.

A schematic of the AG’s model structure for each treatment option.

Both of the manufacturers’ models use PFS and OS as the primary sources for survival information, and derive time in PPS as the difference between OS and PFS. The AG finds this approach generally liable to generate substantial bias in OS estimates when projective parametric modelling is used. This is because recorded OS data are a result of combining patient experience in two distinct phases in which hazard rates would be expected to exhibit quite different dynamics (in PFS the patient is likely to have reduced event risks although the active drug continues to be effective, but in PPS event risks are more likely to revert to higher levels of uncontrolled disease progression). In most cases, standard parametric statistical models cannot accurately represent an outcome measure (such as OS) which is a compound of two very different processes, and modelled OS projected over several decades can result in very large cumulative errors. By contrast, in advanced disease the risk profile of patients entering PPS is usually quite stable and allows projective modelling with greater confidence [i.e. narrow confidence intervals (CIs)] and limits the risk of some of the more extreme estimates of long-term survival which can occur when modelling OS directly. At a pragmatic level, deriving PPS as the difference between OS and PFS can sometimes lead to modelling anomalies with negative estimates of PPS during projection, an error which cannot occur when PFS and PPS estimates are summed.

Undiscounted and discounted (3.5% per annum for costs and outcomes) deterministic results were generated for each year of remaining life up to 30 years. A PSA was carried out for all model variables for which sampling uncertainty could be estimated, and a range of univariate sensitivity analyses were performed for other variables and assumptions.

Specific model features and parameters: lapatinib in combination with letrozole versus letrozole alone

The manufacturer of LAP provided full details of survival analyses (PFS, PPS and OS) requested by the AG relating to data from the EGF30008 trial. 49 The AG employed these data to estimate the components of mean survival that could be expected over the lifetime of a patient, the results of which are presented below. Note that these may differ in detail from those generated by the AG model because of the approximations required to implement the continuous mathematical survival functions in a structure designed around discrete cycle periods.

Specific model features and parameters: trastuzumab in combination with anastrozole versus anastrozole alone

Results

The results obtained from modelling the costs and outcomes of each of the trial-based appraisals are shown separately in this section. No attempt has been made to make any comparisons between the groups of the two trials, as the populations are not considered to be directly comparable and reliable indirect comparisons of treatment effects could not be undertaken (see Chapter 3, Quantity and quality of research available). Therefore, the only questions that may be addressed legitimately are as follows.

1. Can LAP + LET be considered a cost-effective alternative to LET alone?

2. Can TRA + ANA be considered a cost-effective alternative to ANA alone?

Probabilistic sensitivity analysis

Probabilistic sensitivity was explored, running 1000 random iterations for all variables subject to measurable parameter uncertainty, using the base-case scenario over a 20-year time horizon. The PSA results are compared with the corresponding deterministic results in Table 29.

TABLE 29 - Comparison of deterministic and probabilistic cost-effectiveness results for LAP + LET versus LET (base case with a 20-year time horizon)

Sensitivity analysis	Incremental cost (£)	Incremental QALYs	ICER (£)
Deterministic	25,150	0.116	225,131
Probabilistic	25,034	0.109	228,913

The scatterplot of iteration results in the cost-effectiveness plane as shown in Figure 8, the scatterplot, indicates that all iterations lie substantially outside the region normally considered cost-effective. Figure 9 confirms that the probability of the combination therapy being cost-effective at a willingness-to-pay threshold of £50,000 per QALY gained is 0.1%, and does not reach 50% probability until £231,000 per QALY gained.

FIGURE 8.

Probabilistic sensitivity analysis of LAP + LET versus LET only: scatterplot of 1000 probabilistic iterations.

FIGURE 9.

Probabilistic sensitivity analysis of LAP + LET versus LET only: CEAC.

Probabilistic sensitivity analysis

Probabilistic sensitivity was explored, running 1000 random iterations for all variables subject to measurable parameter uncertainty, using the base-case scenario over a 20-year time horizon. The PSA results are compared with the corresponding deterministic results in Table 32.

TABLE 32 - Comparison of deterministic and probabilistic cost-effectiveness results for TRA + ANA versus ANA (base case with a 20-year time horizon)

Sensitivity analysis	Incremental cost (£)	Incremental QALYs	ICER (£)
Deterministic	37,899	0.669	69,514
Probabilistic	33,489	0.513	65,284

The scatterplot of iteration results in the cost-effectiveness plane (Figure 10) indicates a strong positive correlation between incremental cost and incremental benefit. Figure 11 confirms that there is no measurable probability of the combination therapy being cost-effective at a willingness-to-pay threshold of £40,000 per QALY gained, and only a 6.3% probability at £50,000 per QALY gained.

FIGURE 10.

Probabilistic sensitivity analysis of TRA + ANA versus ANA only: scatterplot of 1000 probabilistic iterations.

FIGURE 11.

Probabilistic sensitivity analysis of TRA + ANA versus ANA only: CEAC.

Cost-effectiveness review

In summary, the AG did not identify any relevant papers for inclusion in the cost-effectiveness review of LAP + AI or TRA + AI in patients who are HR+/HER2+ with mBC. The manufacturer of TRA identified a poster96 that was presented at the ASCO 2010 conference; the study described compared LAP + LET versus TRA + ANA, using an indirect comparisons analysis. The AG is of the opinion that the results of the indirect analysis performed by Hastings et al. 96 are unreliable as the studies that make up the evidence network are inappropriate. In addition, the AG notes that without access to more detailed information on costs, it is difficult to comment on the reliability of the cost-effectiveness results in this study.

Submitted economic evaluations by manufacturers

The two economic evaluations submitted by the manufacturers appear to meet the NICE reference case criteria. 97 However, the AG is critical of the approaches used by the manufacturers to estimate OS in each of their models; the AG is of the opinion that projective modelling in this group of patients can lead to substantial bias in OS estimates. In addition, the AG also identified several costing inaccuracies and inconsistencies in both of the economic evaluations submitted.

For the direct comparisons, GlaxoSmithKline demonstrated that LAP + LET is not cost-effective compared with LET and Roche demonstrated that TRA + ANA is not cost-effective compared with ANA.

Both of the manufacturers undertook indirect comparisons analyses in order to be able to compare LAP + LET versus TRA + ANA. GlaxoSmithKline demonstrated that LAP + LET is cost-effective compared with TRA + ANA. Roche demonstrated that TRA + ANA is cost-effective compared with LAP + LET. The AG concludes that the indirect comparisons analyses conducted by the manufacturers are unreliable and that only the ICERs estimated from the direct comparisons are valid.

Roche makes the case for TRA + ANA to be considered as an end-of-life treatment for women with HR+/HER2+ mBC. The AG does not have sufficient information to verify whether or not all three NICE criteria for consideration of end-of-life treatments are met.

Assessment Group’s cost-effectiveness results and sensitivity analysis

The AG reports the results of two separate de novo cost-effectiveness analyses using a common framework and common parameter values, but employing effectiveness data drawn only from a single RCT (the EGF3000849 or the TAnDEM53 trial). The AG model employs outcome data derived from the relevant clinical trial in the form of Kaplan–Meier estimated survival values augmented by projected survival estimates calibrated against the observed data. The AG uses PFS and PPS estimates directly as the basis for calculating expected OS in each group of the RCT.

As the AG is of the opinion that the evidence base is too unstable to allow meaningful comparison of LAP + AI versus TRA + ANA, the only questions that may be addressed legitimately are as follows.

• Can LAP + LET be considered a cost-effective treatment compared with LET alone?

• Can TRA + ANA be considered a cost-effective treatment compared with ANA alone?

Prioritisation Group

1. Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

2. Professor Imti Choonara, Professor in Child Health, Academic Division of Child Health, University of Nottingham

   Chair – Pharmaceuticals Panel

3. Dr Bob Coates, Consultant Advisor – Disease Prevention Panel

4. Dr Andrew Cook, Consultant Advisor – Intervention Procedures Panel

5. Dr Peter Davidson, Director of NETSCC, Health Technology Assessment

6. Dr Nick Hicks, Consultant Adviser – Diagnostic Technologies and Screening Panel, Consultant Advisor–Psychological and Community Therapies Panel

7. Ms Susan Hird, Consultant Advisor, External Devices and Physical Therapies Panel

8. Professor Sallie Lamb, Director, Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick

   Chair – HTA Clinical Evaluation and Trials Board

9. Professor Jonathan Michaels, Professor of Vascular Surgery, Sheffield Vascular Institute, University of Sheffield

   Chair – Interventional Procedures Panel

10. Professor Ruairidh Milne, Director – External Relations

11. Dr John Pounsford, Consultant Physician, Directorate of Medical Services, North Bristol NHS Trust

    Chair – External Devices and Physical Therapies Panel

12. Dr Vaughan Thomas, Consultant Advisor – Pharmaceuticals Panel, Clinical

    Lead – Clinical Evaluation Trials Prioritisation Group

13. Professor Margaret Thorogood, Professor of Epidemiology, Health Sciences Research Institute, University of Warwick

    Chair – Disease Prevention Panel

14. Professor Lindsay Turnbull, Professor of Radiology, Centre for the MR Investigations, University of Hull

    Chair – Diagnostic Technologies and Screening Panel

15. Professor Scott Weich, Professor of Psychiatry, Health Sciences Research Institute, University of Warwick

    Chair – Psychological and Community Therapies Panel

16. Professor Hywel Williams, Director of Nottingham Clinical Trials Unit, Centre of Evidence-Based Dermatology, University of Nottingham

    Chair – HTA Commissioning Board

    Deputy HTA Programme Director

HTA Commissioning Board

1. Professor of Dermato-Epidemiology, Centre of Evidence-Based Dermatology, University of Nottingham

2. Department of Public Health and Epidemiology, University of Birmingham

3. Professor of Clinical Pharmacology, Director, NIHR HTA programme, University of Liverpool

1. Professor Ann Ashburn, Professor of Rehabilitation and Head of Research, Southampton General Hospital

2. Professor Peter Brocklehurst, Professor of Women’s Health, Institute for Women’s Health, University College London

3. Professor Jenny Donovan, Professor of Social Medicine, University of Bristol

4. Professor Jonathan Green, Professor and Acting Head of Department, Child and Adolescent Psychiatry, University of Manchester Medical School

5. Professor John W Gregory, Professor in Paediatric Endocrinology, Department of Child Health, Wales School of Medicine, Cardiff University

6. Professor Steve Halligan, Professor of Gastrointestinal Radiology, University College Hospital, London

7. Professor Freddie Hamdy, Professor of Urology, Head of Nuffield Department of Surgery, University of Oxford

8. Professor Allan House, Professor of Liaison Psychiatry, University of Leeds

9. Dr Martin J Landray, Reader in Epidemiology, Honorary Consultant Physician, Clinical Trial Service Unit, University of Oxford

10. Professor Stephen Morris, Professor of Health Economics, University College London, Research Department of Epidemiology and Public Health, University College London

11. Professor Irwin Nazareth, Professor of Primary Care and Head of Department, Department of Primary Care and Population Sciences, University College London

12. Professor E Andrea Nelson, Professor of Wound Healing and Director of Research, School of Healthcare, University of Leeds

13. Professor John David Norrie, Chair in Clinical Trials and Biostatistics, Robertson Centre for Biostatistics, University of Glasgow

14. Dr Rafael Perera, Lecturer in Medical Statisitics, Department of Primary Health Care, University of Oxford

15. Professor Barney Reeves, Professorial Research Fellow in Health Services Research, Department of Clinical Science, University of Bristol

16. Professor Martin Underwood, Professor of Primary Care Research, Warwick Medical School, University of Warwick

17. Professor Marion Walker, Professor in Stroke Rehabilitation, Associate Director UK Stroke Research Network, University of Nottingham

18. Dr Duncan Young, Senior Clinical Lecturer and Consultant, Nuffield Department of Anaesthetics, University of Oxford

1. Dr Tom Foulks, Medical Research Council

2. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

HTA Clinical Evaluation and Trials Board

1. Director, Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick and Professor of Rehabilitation, Nuffield Department of Orthopaedic, Rheumatology and Musculoskeletal Sciences, University of Oxford

2. Professor of the Psychology of Health Care, Leeds Institute of Health Sciences, University of Leeds

3. Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

1. Professor Keith Abrams, Professor of Medical Statistics, Department of Health Sciences, University of Leicester

2. Professor Martin Bland, Professor of Health Statistics, Department of Health Sciences, University of York

3. Professor Jane Blazeby, Professor of Surgery and Consultant Upper GI Surgeon, Department of Social Medicine, University of Bristol

4. Professor Julia M Brown, Director, Clinical Trials Research Unit, University of Leeds

5. Professor Alistair Burns, Professor of Old Age Psychiatry, Psychiatry Research Group, School of Community-Based Medicine, The University of Manchester & National Clinical Director for Dementia, Department of Health

6. Dr Jennifer Burr, Director, Centre for Healthcare Randomised trials (CHART), University of Aberdeen

7. Professor Linda Davies, Professor of Health Economics, Health Sciences Research Group, University of Manchester

8. Professor Simon Gilbody, Prof of Psych Medicine and Health Services Research, Department of Health Sciences, University of York

9. Professor Steven Goodacre, Professor and Consultant in Emergency Medicine, School of Health and Related Research, University of Sheffield

10. Professor Dyfrig Hughes, Professor of Pharmacoeconomics, Centre for Economics and Policy in Health, Institute of Medical and Social Care Research, Bangor University

11. Professor Paul Jones, Professor of Respiratory Medicine, Department of Cardiac and Vascular Science, St George‘s Hospital Medical School, University of London

12. Professor Khalid Khan, Professor of Women’s Health and Clinical Epidemiology, Barts and the London School of Medicine, Queen Mary, University of London

13. Professor Richard J McManus, Professor of Primary Care Cardiovascular Research, Primary Care Clinical Sciences Building, University of Birmingham

14. Professor Helen Rodgers, Professor of Stroke Care, Institute for Ageing and Health, Newcastle University

15. Professor Ken Stein, Professor of Public Health, Peninsula Technology Assessment Group, Peninsula College of Medicine and Dentistry, Universities of Exeter and Plymouth

16. Professor Jonathan Sterne, Professor of Medical Statistics and Epidemiology, Department of Social Medicine, University of Bristol

17. Mr Andy Vail, Senior Lecturer, Health Sciences Research Group, University of Manchester

18. Professor Clare Wilkinson, Professor of General Practice and Director of Research North Wales Clinical School, Department of Primary Care and Public Health, Cardiff University

19. Dr Ian B Wilkinson, Senior Lecturer and Honorary Consultant, Clinical Pharmacology Unit, Department of Medicine, University of Cambridge

1. Ms Kate Law, Director of Clinical Trials, Cancer Research UK

2. Dr Morven Roberts, Clinical Trials Manager, Health Services and Public Health Services Board, Medical Research Council

Diagnostic Technologies and Screening Panel

1. Scientific Director of the Centre for Magnetic Resonance Investigations and YCR Professor of Radiology, Hull Royal Infirmary

2. Professor Judith E Adams, Consultant Radiologist, Manchester Royal Infirmary, Central Manchester & Manchester Children’s University Hospitals NHS Trust, and Professor of Diagnostic Radiology, University of Manchester

3. Mr Angus S Arunkalaivanan, Honorary Senior Lecturer, University of Birmingham and Consultant Urogynaecologist and Obstetrician, City Hospital, Birmingham

4. Dr Diana Baralle, Consultant and Senior Lecturer in Clinical Genetics, University of Southampton

5. Dr Stephanie Dancer, Consultant Microbiologist, Hairmyres Hospital, East Kilbride

6. Dr Diane Eccles, Professor of Cancer Genetics, Wessex Clinical Genetics Service, Princess Anne Hospital

7. Dr Trevor Friedman, Consultant Liason Psychiatrist, Brandon Unit, Leicester General Hospital

8. Dr Ron Gray, Consultant, National Perinatal Epidemiology Unit, Institute of Health Sciences, University of Oxford

9. Professor Paul D Griffiths, Professor of Radiology, Academic Unit of Radiology, University of Sheffield

10. Mr Martin Hooper, Public contributor

11. Professor Anthony Robert Kendrick, Associate Dean for Clinical Research and Professor of Primary Medical Care, University of Southampton

12. Dr Nicola Lennard, Senior Medical Officer, MHRA

13. Dr Anne Mackie, Director of Programmes, UK National Screening Committee, London

14. Mr David Mathew, Public contributor

15. Dr Michael Millar, Consultant Senior Lecturer in Microbiology, Department of Pathology & Microbiology, Barts and The London NHS Trust, Royal London Hospital

16. Mrs Una Rennard, Public contributor

17. Dr Stuart Smellie, Consultant in Clinical Pathology, Bishop Auckland General Hospital

18. Ms Jane Smith, Consultant Ultrasound Practitioner, Leeds Teaching Hospital NHS Trust, Leeds

19. Dr Allison Streetly, Programme Director, NHS Sickle Cell and Thalassaemia Screening Programme, King’s College School of Medicine

20. Dr Matthew Thompson, Senior Clinical Scientist and GP, Department of Primary Health Care, University of Oxford

21. Dr Alan J Williams, Consultant Physician, General and Respiratory Medicine, The Royal Bournemouth Hospital

1. Dr Tim Elliott, Team Leader, Cancer Screening, Department of Health

2. Dr Joanna Jenkinson, Board Secretary, Neurosciences and Mental Health Board (NMHB), Medical Research Council

3. Professor Julietta Patrick, Director, NHS Cancer Screening Programme, Sheffield

4. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

5. Professor Tom Walley, CBE, Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

6. Dr Ursula Wells, Principal Research Officer, Policy Research Programme, Department of Health

Disease Prevention Panel

1. Professor of Epidemiology, University of Warwick Medical School, Coventry

2. Dr Robert Cook, Clinical Programmes Director, Bazian Ltd, London

3. Dr Colin Greaves, Senior Research Fellow, Peninsula Medical School (Primary Care)

4. Mr Michael Head, Public contributor

5. Professor Cathy Jackson, Professor of Primary Care Medicine, Bute Medical School, University of St Andrews

6. Dr Russell Jago, Senior Lecturer in Exercise, Nutrition and Health, Centre for Sport, Exercise and Health, University of Bristol

7. Dr Julie Mytton, Consultant in Child Public Health, NHS Bristol

8. Professor Irwin Nazareth, Professor of Primary Care and Director, Department of Primary Care and Population Sciences, University College London

9. Dr Richard Richards, Assistant Director of Public Health, Derbyshire County Primary Care Trust

10. Professor Ian Roberts, Professor of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine

11. Dr Kenneth Robertson, Consultant Paediatrician, Royal Hospital for Sick Children, Glasgow

12. Dr Catherine Swann, Associate Director, Centre for Public Health Excellence, NICE

13. Mrs Jean Thurston, Public contributor

14. Professor David Weller, Head, School of Clinical Science and Community Health, University of Edinburgh

1. Ms Christine McGuire, Research & Development, Department of Health

2. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

3. Professor Tom Walley, CBE, Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

External Devices and Physical Therapies Panel

1. Consultant Physician North Bristol NHS Trust

2. Reader in Wound Healing and Director of Research, University of Leeds

3. Professor Bipin Bhakta, Charterhouse Professor in Rehabilitation Medicine, University of Leeds

4. Mrs Penny Calder, Public contributor

5. Dr Dawn Carnes, Senior Research Fellow, Barts and the London School of Medicine and Dentistry

6. Dr Emma Clark, Clinician Scientist Fellow & Cons. Rheumatologist, University of Bristol

7. Mrs Anthea De Barton-Watson, Public contributor

8. Professor Nadine Foster, Professor of Musculoskeletal Health in Primary Care Arthritis Research, Keele University

9. Dr Shaheen Hamdy, Clinical Senior Lecturer and Consultant Physician, University of Manchester

10. Professor Christine Norton, Professor of Clinical Nursing Innovation, Bucks New University and Imperial College Healthcare NHS Trust

11. Dr Lorraine Pinnigton, Associate Professor in Rehabilitation, University of Nottingham

12. Dr Kate Radford, Senior Lecturer (Research), University of Central Lancashire

13. Mr Jim Reece, Public contributor

14. Professor Maria Stokes, Professor of Neuromusculoskeletal Rehabilitation, University of Southampton

15. Dr Pippa Tyrrell, Senior Lecturer/Consultant, Salford Royal Foundation Hospitals’ Trust and University of Manchester

16. Dr Nefyn Williams, Clinical Senior Lecturer, Cardiff University

1. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

2. Dr Morven Roberts, Clinical Trials Manager, Health Services and Public Health Services Board, Medical Research Council

3. Professor Tom Walley, CBE, Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

4. Dr Ursula Wells, Principal Research Officer, Policy Research Programme, Department of Health

Interventional Procedures Panel

1. Professor of Vascular Surgery, University of Sheffield

2. Consultant Colorectal Surgeon, Bristol Royal Infirmary

3. Mrs Isabel Boyer, Public contributor

4. Mr Sankaran Chandra Sekharan, Consultant Surgeon, Breast Surgery, Colchester Hospital University NHS Foundation Trust

5. Professor Nicholas Clarke, Consultant Orthopaedic Surgeon, Southampton University Hospitals NHS Trust

6. Ms Leonie Cooke, Public contributor

7. Mr Seumas Eckford, Consultant in Obstetrics & Gynaecology, North Devon District Hospital

8. Professor Sam Eljamel, Consultant Neurosurgeon, Ninewells Hospital and Medical School, Dundee

9. Dr Adele Fielding, Senior Lecturer and Honorary Consultant in Haematology, University College London Medical School

10. Dr Matthew Hatton, Consultant in Clinical Oncology, Sheffield Teaching Hospital Foundation Trust

11. Dr John Holden, General Practitioner, Garswood Surgery, Wigan

12. Dr Fiona Lecky, Senior Lecturer/Honorary Consultant in Emergency Medicine, University of Manchester/Salford Royal Hospitals NHS Foundation Trust

13. Dr Nadim Malik, Consultant Cardiologist/Honorary Lecturer, University of Manchester

14. Mr Hisham Mehanna, Consultant & Honorary Associate Professor, University Hospitals Coventry & Warwickshire NHS Trust

15. Dr Jane Montgomery, Consultant in Anaesthetics and Critical Care, South Devon Healthcare NHS Foundation Trust

16. Professor Jon Moss, Consultant Interventional Radiologist, North Glasgow Hospitals University NHS Trust

17. Dr Simon Padley, Consultant Radiologist, Chelsea & Westminster Hospital

18. Dr Ashish Paul, Medical Director, Bedfordshire PCT

19. Dr Sarah Purdy, Consultant Senior Lecturer, University of Bristol

20. Dr Matthew Wilson, Consultant Anaesthetist, Sheffield Teaching Hospitals NHS Foundation Trust

21. Professor Yit Chiun Yang, Consultant Ophthalmologist, Royal Wolverhampton Hospitals NHS Trust

1. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

2. Dr Morven Roberts, Clinical Trials Manager, Health Services and Public Health Services Board, Medical Research Council

3. Professor Tom Walley, CBE, Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

4. Dr Ursula Wells, Principal Research Officer, Policy Research Programme, Department of Health

Pharmaceuticals Panel

1. Professor in Child Health, University of Nottingham

2. Senior Lecturer in Clinical Pharmacology, University of East Anglia

3. Dr Martin Ashton-Key, Medical Advisor, National Commissioning Group, NHS London

4. Dr Peter Elton, Director of Public Health, Bury Primary Care Trust

5. Dr Ben Goldacre, Research Fellow, Division of Psychological Medicine and Psychiatry, King’s College London

6. Dr James Gray, Consultant Microbiologist, Department of Microbiology, Birmingham Children’s Hospital NHS Foundation Trust

7. Dr Jurjees Hasan, Consultant in Medical Oncology, The Christie, Manchester

8. Dr Carl Heneghan, Deputy Director Centre for Evidence-Based Medicine and Clinical Lecturer, Department of Primary Health Care, University of Oxford

9. Dr Dyfrig Hughes, Reader in Pharmacoeconomics and Deputy Director, Centre for Economics and Policy in Health, IMSCaR, Bangor University

10. Dr Maria Kouimtzi, Pharmacy and Informatics Director, Global Clinical Solutions, Wiley-Blackwell

11. Professor Femi Oyebode, Consultant Psychiatrist and Head of Department, University of Birmingham

12. Dr Andrew Prentice, Senior Lecturer and Consultant Obstetrician and Gynaecologist, The Rosie Hospital, University of Cambridge

13. Ms Amanda Roberts, Public contributor

14. Dr Gillian Shepherd, Director, Health and Clinical Excellence, Merck Serono Ltd

15. Mrs Katrina Simister, Assistant Director New Medicines, National Prescribing Centre, Liverpool

16. Professor Donald Singer, Professor of Clinical Pharmacology and Therapeutics, Clinical Sciences Research Institute, CSB, University of Warwick Medical School

17. Mr David Symes, Public contributor

18. Dr Arnold Zermansky, General Practitioner, Senior Research Fellow, Pharmacy Practice and Medicines Management Group, Leeds University

1. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

2. Mr Simon Reeve, Head of Clinical and Cost-Effectiveness, Medicines, Pharmacy and Industry Group, Department of Health

3. Dr Heike Weber, Programme Manager, Medical Research Council

4. Professor Tom Walley, CBE, Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

5. Dr Ursula Wells, Principal Research Officer, Policy Research Programme, Department of Health

Psychological and Community Therapies Panel

1. Professor of Psychiatry, University of Warwick, Coventry

2. Consultant & University Lecturer in Psychiatry, University of Cambridge

3. Professor Jane Barlow, Professor of Public Health in the Early Years, Health Sciences Research Institute, Warwick Medical School

4. Dr Sabyasachi Bhaumik, Consultant Psychiatrist, Leicestershire Partnership NHS Trust

5. Mrs Val Carlill, Public contributor

6. Dr Steve Cunningham, Consultant Respiratory Paediatrician, Lothian Health Board

7. Dr Anne Hesketh, Senior Clinical Lecturer in Speech and Language Therapy, University of Manchester

8. Dr Peter Langdon, Senior Clinical Lecturer, School of Medicine, Health Policy and Practice, University of East Anglia

9. Dr Yann Lefeuvre, GP Partner, Burrage Road Surgery, London

10. Dr Jeremy J Murphy, Consultant Physician and Cardiologist, County Durham and Darlington Foundation Trust

11. Dr Richard Neal, Clinical Senior Lecturer in General Practice, Cardiff University

12. Mr John Needham, Public contributor

13. Ms Mary Nettle, Mental Health User Consultant

14. Professor John Potter, Professor of Ageing and Stroke Medicine, University of East Anglia

15. Dr Greta Rait, Senior Clinical Lecturer and General Practitioner, University College London

16. Dr Paul Ramchandani, Senior Research Fellow/Cons. Child Psychiatrist, University of Oxford

17. Dr Karen Roberts, Nurse/Consultant, Dunston Hill Hospital, Tyne and Wear

18. Dr Karim Saad, Consultant in Old Age Psychiatry, Coventry and Warwickshire Partnership Trust

19. Dr Lesley Stockton, Lecturer, School of Health Sciences, University of Liverpool

20. Dr Simon Wright, GP Partner, Walkden Medical Centre, Manchester

1. Dr Kay Pattison, Senior NIHR Programme Manager, Department of Health

2. Dr Morven Roberts, Clinical Trials Manager, Health Services and Public Health Services Board, Medical Research Council

3. Professor Tom Walley, CBE, Director, NIHR HTA programme, Professor of Clinical Pharmacology, University of Liverpool

4. Dr Ursula Wells, Principal Research Officer, Policy Research Programme, Department of Health

Expert Advisory Network

1. Professor Douglas Altman, Professor of Statistics in Medicine, Centre for Statistics in Medicine, University of Oxford

2. Professor John Bond, Professor of Social Gerontology & Health Services Research, University of Newcastle upon Tyne

3. Professor Andrew Bradbury, Professor of Vascular Surgery, Solihull Hospital, Birmingham

4. Mr Shaun Brogan, Chief Executive, Ridgeway Primary Care Group, Aylesbury

5. Mrs Stella Burnside OBE, Chief Executive, Regulation and Improvement Authority, Belfast

6. Ms Tracy Bury, Project Manager, World Confederation of Physical Therapy, London

7. Professor Iain T Cameron, Professor of Obstetrics and Gynaecology and Head of the School of Medicine, University of Southampton

8. Professor Bruce Campbell, Consultant Vascular & General Surgeon, Royal Devon & Exeter Hospital, Wonford

9. Dr Christine Clark, Medical Writer and Consultant Pharmacist, Rossendale

10. Professor Collette Clifford, Professor of Nursing and Head of Research, The Medical School, University of Birmingham

11. Professor Barry Cookson, Director, Laboratory of Hospital Infection, Public Health Laboratory Service, London

12. Dr Carl Counsell, Clinical Senior Lecturer in Neurology, University of Aberdeen

13. Professor Howard Cuckle, Professor of Reproductive Epidemiology, Department of Paediatrics, Obstetrics & Gynaecology, University of Leeds

14. Professor Carol Dezateux, Professor of Paediatric Epidemiology, Institute of Child Health, London

15. Mr John Dunning, Consultant Cardiothoracic Surgeon, Papworth Hospital NHS Trust, Cambridge

16. Mr Jonothan Earnshaw, Consultant Vascular Surgeon, Gloucestershire Royal Hospital, Gloucester

17. Professor Martin Eccles, Professor of Clinical Effectiveness, Centre for Health Services Research, University of Newcastle upon Tyne

18. Professor Pam Enderby, Dean of Faculty of Medicine, Institute of General Practice and Primary Care, University of Sheffield

19. Professor Gene Feder, Professor of Primary Care Research & Development, Centre for Health Sciences, Barts and The London School of Medicine and Dentistry

20. Mr Leonard R Fenwick, Chief Executive, Freeman Hospital, Newcastle upon Tyne

21. Mrs Gillian Fletcher, Antenatal Teacher and Tutor and President, National Childbirth Trust, Henfield

22. Professor Jayne Franklyn, Professor of Medicine, University of Birmingham

23. Mr Tam Fry, Honorary Chairman, Child Growth Foundation, London

24. Professor Fiona Gilbert, Consultant Radiologist and NCRN Member, University of Aberdeen

25. Professor Paul Gregg, Professor of Orthopaedic Surgical Science, South Tees Hospital NHS Trust

26. Bec Hanley, Co-director, TwoCan Associates, West Sussex

27. Dr Maryann L Hardy, Senior Lecturer, University of Bradford

28. Mrs Sharon Hart, Healthcare Management Consultant, Reading

29. Professor Robert E Hawkins, CRC Professor and Director of Medical Oncology, Christie CRC Research Centre, Christie Hospital NHS Trust, Manchester

30. Professor Richard Hobbs, Head of Department of Primary Care & General Practice, University of Birmingham

31. Professor Alan Horwich, Dean and Section Chairman, The Institute of Cancer Research, London

32. Professor Allen Hutchinson, Director of Public Health and Deputy Dean of ScHARR, University of Sheffield

33. Professor Peter Jones, Professor of Psychiatry, University of Cambridge, Cambridge

34. Professor Stan Kaye, Cancer Research UK Professor of Medical Oncology, Royal Marsden Hospital and Institute of Cancer Research, Surrey

35. Dr Duncan Keeley, General Practitioner (Dr Burch & Ptnrs), The Health Centre, Thame

36. Dr Donna Lamping, Research Degrees Programme Director and Reader in Psychology, Health Services Research Unit, London School of Hygiene and Tropical Medicine, London

37. Professor James Lindesay, Professor of Psychiatry for the Elderly, University of Leicester

38. Professor Julian Little, Professor of Human Genome Epidemiology, University of Ottawa

39. Professor Alistaire McGuire, Professor of Health Economics, London School of Economics

40. Professor Neill McIntosh, Edward Clark Professor of Child Life and Health, University of Edinburgh

41. Professor Rajan Madhok, Consultant in Public Health, South Manchester Primary Care Trust

42. Professor Sir Alexander Markham, Director, Molecular Medicine Unit, St James’s University Hospital, Leeds

43. Dr Peter Moore, Freelance Science Writer, Ashtead

44. Dr Andrew Mortimore, Public Health Director, Southampton City Primary Care Trust

45. Dr Sue Moss, Associate Director, Cancer Screening Evaluation Unit, Institute of Cancer Research, Sutton

46. Professor Miranda Mugford, Professor of Health Economics and Group Co-ordinator, University of East Anglia

47. Professor Jim Neilson, Head of School of Reproductive & Developmental Medicine and Professor of Obstetrics and Gynaecology, University of Liverpool

48. Mrs Julietta Patnick, Director, NHS Cancer Screening Programmes, Sheffield

49. Professor Robert Peveler, Professor of Liaison Psychiatry, Royal South Hants Hospital, Southampton

50. Professor Chris Price, Director of Clinical Research, Bayer Diagnostics Europe, Stoke Poges

51. Professor William Rosenberg, Professor of Hepatology and Consultant Physician, University of Southampton

52. Professor Peter Sandercock, Professor of Medical Neurology, Department of Clinical Neurosciences, University of Edinburgh

53. Dr Philip Shackley, Senior Lecturer in Health Economics, Sheffield Vascular Institute, University of Sheffield

54. Dr Eamonn Sheridan, Consultant in Clinical Genetics, St James’s University Hospital, Leeds

55. Dr Margaret Somerville, Director of Public Health Learning, Peninsula Medical School, University of Plymouth

56. Professor Sarah Stewart-Brown, Professor of Public Health, Division of Health in the Community, University of Warwick, Coventry

57. Dr Nick Summerton, GP Appraiser and Codirector, Research Network, Yorkshire Clinical Consultant, Primary Care and Public Health, University of Oxford

58. Professor Ala Szczepura, Professor of Health Service Research, Centre for Health Services Studies, University of Warwick, Coventry

59. Dr Ross Taylor, Senior Lecturer, University of Aberdeen

60. Dr Richard Tiner, Medical Director, Medical Department, Association of the British Pharmaceutical Industry

61. Mrs Joan Webster, Consumer Member, Southern Derbyshire Community Health Council

62. Professor Martin Whittle, Clinical Co-director, National Co-ordinating Centre for Women’s and Children’s Health, Lymington