PubMed Health. A service of the National Library of Medicine, National Institutes of Health.

Ara R, Blake L, Gray L, et al. What is the Clinical Effectiveness and Cost-Effectiveness of Using Drugs in Treating Obese Patients in Primary Care? A Systematic Review. Southampton (UK): NIHR Journals Library; 2012 Feb. (Health Technology Assessment, No. 16.5.)

Appendix 6Protocol


Evaluating Anti-obesity Treatments (EAT) in primary care


This project will evaluate the clinical and cost-effectiveness of using drugs in treating obese adults in a primary care setting. The purpose of the study is to apply rigorous methods of systematic reviewing, evidence synthesis and decision analytic modelling to evaluate the comparative clinical and cost-effectiveness of the three pharmaceutical treatments: Orlistat, Sibutramine and Rimonabant.

  • Population: Clinically obese adults
  • Interventions: Orlistat, Sibutramine, Rimonabant anti-obesity drugs
  • Comparators: Orlistat vs. Sibutramine vs. Rimonabant vs. No treatment
  • Outcomes: Long term weight loss, adverse events, quality of life, cardiovascular risk, lipid profiles, co-morbidity and cost effectiveness
  • Setting: Primary care
  • Perspective: NHS and Personal and Social Service (PSS)

2.1. Research aims and objectives

  1. Analyse an existing routine data base of clinical information from primary care to determine the impact of obesity on mortality and morbidity.
  2. Compare the characteristics of patients and effectiveness of anti-obesity agents in the general population with those in clinical trials.
  3. Conduct a full systematic review of the published evidence on the clinical effectiveness of Orlistat, Sibutramine and Rimonabant.
  4. Undertake a full synthesis of the available evidence. This will include the use of a higher-level synthesis of the data using Bayesian methodologies to account for indirect comparison.
  5. Undertake a full systematic review of the published evidence of the cost-effectiveness of the agents. This will include a systematic review of published economic evaluations in the area and identification of other evidence needed to populate an economic model.
  6. Use decision-analytic modelling and probabilistic sensitivity analysis to assess the relative cost-effectiveness of the three agents in terms of the incremental cost per quality adjusted life year (QALY) gained.
  7. Use expected value of information techniques to determine the value of collecting further data on input parameters, and the potential benefits of future head to head trials of the agents.

2.2. Existing research

The authors of the recent NICE obesity clinical guidelines estimate that more than 12 million adults in England will be obese (BMI ≥ 30 kg/m2) by 2010 if the increasing trend in prevalence continues.{CG43} The guidelines suggest that a large proportion of obese individuals fail to achieve and maintain weight losses without clinical support. The guideline also states that the majority of PCOs did not monitor the effectiveness of drug treatments for obesity and advocated that every necessary step is taken to tackle obesity, recommending that preventing and managing obesity is a priority and that systems should be in place to implement local obesity strategies. The guideline included a full systematic review of the clinical and economic evidence for the two pharmaceutical treatments (i.e. Orlistat and Sibutramine) available on prescription in the UK at the time.

A recently published meta-analysis which included the newer treatment, Rimonabant, found that all three agents modestly reduce weight providing on average less than 5 kg more weight loss compared with placebo.{Rucker, 2007} They found the original weight differential between the placebo and active arms was maintained for up to four years as weight regain was consistent in both groups. A recent retrospective cohort study reported persistence rates to Orlistat or Sibutramine were smaller than 2% at two years; much lower than reported in clinical trials{Padwal, 2007} and the authors suggest that the lack of adherence to treatment is a major factor limiting the efficacy of anti-obesity drugs.

The three agents have unique adverse effects profiles. The evidence on secondary end points suggests they also have differing effects on cardiovascular risk profiles. However, due to absence of data on the effects on mortality or cardiovascular morbidity the exact benefits are uncertain. Of major concern are the generalisability of the results from clinical studies to primary care settings, and as Rucker et al. mention, with very high attrition rates, the internal validity of many of the clinical studies is potentially compromised.{Rucker 2007}

There have been a number of UK economic evaluations exploring the cost-effectiveness of Sibutramine and Orlistat compared with placebo.{CG43, TAP 22, TAP 31} An ongoing NICE STA submission on behalf of Sanofi-Aventis includes an economic evaluation of the three interventions within the same modelling framework using pair-wise comparisons of primary outcomes.{ACD Rimonabant} The technology assessment group expressed concerns with discrepancies in the data presented for Orlistat and Sibutramine.{ACD Rimonabant} They also stated a major limitation in the economics is the lack of response hurdles in the clinical pathways modelled for Sibutramine and Orlistat and highlight further research is required on head to head studies and relationships between weight losses and quality of life measurements.

The current proposal describes a study of the clinical benefits and cost-effectiveness of using drugs in treating obese patients in primary care to inform future policy initiatives and primary care clinicians. The study will also identify areas in which further research would be most valuable and in particular the potential net benefits associated with future head to head trials of the three drugs.

2.3. Methods for the systematic identification of evidence

a. Scoping search

A brief scoping literature search combining search terms related to Orlistat, Sibutramine and Rimonabant retrieved the following: 544 citations from MEDLINE 1966–present; 499 citations from EMBASE 1980–present, 99 citations from CINAHL 1982–present, 241 citations from the Cochrane Library various dates–present and 501 from Web of Science 1900–present.

b. Detailed searching techniques

The search strategies will be conducted in separate stages:

i. Search strategy for identification of studies providing information on clinical effectiveness

A search for relevant studies on clinical effectiveness will be conducted by means of electronic searches of key databases including MEDLINE, EMBASE, Science Citation Index and Biological Abstracts.

Searching for clinical information as contained in systematic reviews, meta-analyses or clinical trials. This will focus on the above key databases with the addition of the Cochrane library and specific trials registers. Published methods of searching specifically for systematic reviews and clinical trials as developed by the McMaster University Health Information Research Unit will be used. Specific concepts to be included in the literature searches will include terms relating to obesity (, obese, obesity (subject heading), obesity, morbid (subject heading)), and terms relating to agents (orlistat, sibutramine, rimonabant, anti-obesity agents (subject heading), Tetrahydrolipstatin (subject heading), sibutramine (subject heading), rimonabant (subject heading)). References will also be located through review of references for relevant articles and through citation search facilities via the Web of Science's Science Citation Index and Social Science Citation Index. Where systematic reviews already exist, these will be used to identify relevant studies and to inform subsequent analyses. In addition systematic searches of the Internet using various search engines will be used to identify unpublished materials and work in progress. Key authors and commercial organisations involved in the investigation of pharmaceutical agents will be contacted and asked for unpublished materials.

We will utilise a varied range of sources and search techniques to identify relevant literature. A comprehensive literature search will be undertaken in the major medical, health-related, science and health economic electronic bibliographic databases (i.e. CDSR, NHS DARE, NHS HTA, MEDLINE, EMBASE, CINAHL, Science Citation Index, PreMEDLINE, NHS EED, HEED, CENTRAL, Pascal, ASSIA, Social Care Online, Social Science Citation Index). In addition, various health service research and guideline producing bodies (e.g. SIGN, National Guidelines Clearinghouse, etc.) will be consulted via the internet and key organisations (e.g. National Obesity Forum) will be contacted. We will utilise the expertise within the group and consult with national and international experts in research and practice in obesity. Ongoing and recently completed research in the field will be identified through searching the National Research Register, ReFeR, Current Controlled Trials and its links, HSRProj and Index to Theses. Grey literature will be identified from searches of databases including Dissertation Abstracts and Inside Conferences. Finally, the reference lists of included studies will be examined for additional relevant references and, where appropriate, the citation facility in Web of Science will be used to search for specific papers and authors.

ii. Search strategy for identification of studies providing information on adverse effects

Supplementary searches will be conducted for data on adverse effects. No study restrictions will be utilised. Specific pharmacological databases will be used at this stage of the review. Reference will be made to published work on retrieval of adverse effects literature from the NHS Centre for Reviews and Dissemination.{Golder 2006, Golder 2006} We will also write to the manufacturers of these drugs to obtain any data on file.

iii. Search strategy for identification of studies providing information on adherence to treatment

Given that primary research suggests that lack of adherence to treatment is a major factor limiting the efficacy of anti-obesity drugs it would be valuable in answering the effectiveness-related questions to examine what we know on patients' perceptions of anti-obesity drugs. Ogden and Sidhu (2006) are among the first to examine specifically the qualitative experience of patients on obesity medication. {Ogden 2006} Other qualitative research on perceptions of obesity treatments will also be valuable. We therefore propose to conduct a tightly focused qualitative evidence synthesis using accepted methods of evidence interpretation and integration.{Pope, Mays and Popay 2007} This review will complement the effectiveness review and modelling work and provide added value by identifying the main variables that can impact on the anticipated effectiveness of anti-obesity medication.

iv. Search strategy for identifying economic evidence

In addition to the search strategies identified above systematic searches will take place of the specialist health economic data sources such as DARE, HTA Database (University of York), NHS EED and the Office for Health Economics HEED database. Economics filters used by the NHS CRD to populate the NHS EED database will be adapted to other databases.

2.4. Epidemiological modelling

A key component of the project is the identification and development of an epidemiological model for the natural history (in terms of diabetes, CVD, colorectal cancer, etc. and their sequelae) of individuals who are obese. Whilst there have been a number of meta-analyses published which have considered the risk of these outcomes in obese individuals, use of Individual Patient Data (IPD) is required so that (a) the risk can be estimated at all levels of BMI, and not just the categorisations often reported, e.g. 25–29.9, 30+ etc., and (b) the risks of specific outcomes may be estimated within a competing risks framework, whilst at the same time taking account of the expected correlation between the various outcomes.

The development of a statistical model relating BMI to clinical outcomes will be undertaken using the General Practice Research Database (GPRD – Figure 1 shows an illustrative model (this may be either Markov or semi-Markov – see section 2.6 below) of a patient pathway for Otherwise Healthy Obese (OHO) individuals, and with the underling transition rates (λ1,…,λ5) estimated from GPRD as a function of BMI (and time if semi-Markov).

FIGURE 1. Illustrative Markov model for Otherwise Healthy Obese (OHO) individuals.


Illustrative Markov model for Otherwise Healthy Obese (OHO) individuals.

However, there is also existing evidence (from IPD analyses) available regarding the risk of various clinical events in relationship to BMI (especially CVD and diabetes) {Bogers et al 2007} and synthesis of the results from GPRD and reported summary data will also be undertaken {Sutton & Abrams 2001; Sutton et al 2007}.

The GPRD will also be used to explore the effect of the three drugs in general practice – both in terms of clinical effectiveness (which will then be compared to the results of the systematic review), but also the effect of patients stopping treatment, i.e. to determine the rate at which they return to their original BMI trajectory or otherwise.

The Health Survey for England (HSE) will be used to establish the distribution of BMI in those individuals who are obese (BMI > 30), and to which the risk models developed via GPRD will be applied in order to populate the initial transitions from an obese state to the various health states (representing the clinical events) in the cost-effectiveness model (see Section 2.6 below), and to which the clinical effectiveness estimates (derived from the systematic review) may then be applied. For example, in Figure 2, applying the estimated clinical effect (in terms of reduction in BMI) δBMI obtained from the systematic review and meta-analysis to individuals will enable the corresponding change in the risk of the various events being considered to be estimated, i.e. πk.

FIGURE 2. Relationships between BMI and probability of an event k.


Relationships between BMI and probability of an event k.

2.5. Systematic review methods

A. Clinical data

A key objective of the proposed study is to conduct a systematic review of the published evidence on the pharmacological agents Orlistat, Sibutramine and Rimonabant. This will also include a detailed systematic review of evidence on the adverse event profile of each agent.

The reviews of clinical effectiveness will update those contained in the systematic reviews of Sibutramine and Orlistat {HTA 31, HTA 22} and the industry submission for Rimonabant.{ACD Rimonabant} Obesity impacts on a wide range of health and social care professionals in a wide variety of settings. While the emphasis will be on UK clinical practice, non-UK evidence on effectiveness and outcomes will also be considered.

a. Search strategy (example from EMBASE)

The search strategy will use the following terms:

1,, 2, 3, obesity/4, obesity, morbid/5, or/1–4 6, orlistat 7, sibutramine 8, rimonabant 9, anti-obesity agents/10, Tetrahydrolipstatin/11, sibutramine/12, rimonabant/13, or/6–12 14, 5 and 13

Plus methodological filters as described above to locate high quality clinical effectiveness and cost-effectiveness studies. The results of the searches will be stored in a Reference Manager database.

b. Inclusion/exclusion criteria
  • Types of studies: Randomised controlled trials (RCTs), incorporating any duration of therapy and any length of follow-up will be considered for inclusion in the review.
  • Participants: i) RCTs recruiting adults (aged 18 years) defined as being overweight or obese. ii) RCTs recruiting adults wishing to maintain weight loss, having been previously overweight or obese. iii) Trials involving specific patient groups such as those with diabetes, hypertension or hyperlipidaemia will be included in the review, provided they meet the above criteria.
  • Interventions: i) Evaluations of Orlistat, Sibutramine or Rimonabant used to treat overweight/obese patients or to maintain weight loss in previously overweight or obese patients. ii) Orlistat, Sibutramine or Rimonabant may be combined with other strategies such as dietary restriction or behavioural programmes. iii) Participants in control groups may receive placebo, an alternative anti-obesity pharmacological agent or an alternative anti-obesity intervention (e.g. based on dietary regimen, physical activity or behavioural modification).
  • Studies recruiting people with eating disorders such as anorexia nervosa and bulimia nervosa will be excluded.
  • In trials where overweight/obese participants were recruited as well as those with the above eating disorders, only those where results were presented separately for the overweight/obese participants will be included.
c. Outcomes

The primary outcome of the review will be an assessment of obesity/overweight status as measured by changes in body weight, fat content or fat distribution:

  • Measures of weight change include absolute weight change and percentage weight change relative to baseline.
  • Measures of fat content include BMI, ponderal index, skinfold thickness, fat-free mass, body percentage and fat change relative to baseline.
  • Measures of fat distribution including changes in waist size, waist–hip ratio and girth–height ratio relative to baseline.

Secondary outcomes of the review will be a) physiological changes occurring in association with changes in body weight/fat content/fat distribution such as changes in lipid profiles, glycaemic control among those with diabetes, and blood pressure, b) patient-related quality of life, c) information on adverse effects and d) costs.

d. Review methods
  • References identified by the literature searches will be sifted in three stages. They will first be screened for relevance by title. The abstracts of those which are not excluded at this stage will then be read and finally, all manuscripts which seem to be potentially relevant will be obtained for a more detailed appraisal. Sifting will be undertaken by one reviewer, and to ensure consistency a sample of references will be checked by a second reviewer. All decisions will be coded and recorded in the Reference Manager database.
  • Studies will be categorised according to the type of participant (see inclusion criteria). Data extraction will be undertaken by one reviewer, using customised data extraction forms, and checked by a second reviewer. Discrepancies will be discussed, and any which cannot be resolved will be referred for discussion to the study team. Data extraction will cover the design and conduct of trials, characteristics of participants and interventions, and outcomes.
  • Quality checklists will be used to appraise each article included. The quality of randomised controlled trials will be assessed according to criteria based on those proposed by the NHS Centre for Reviews and Dissemination. Non-randomised forms of evidence of clinical effectiveness such as observational studies will be assessed using the Downs and Black checklist.{Tooth 2005} Attrition rates will be assessed and discussed as previous reviews have noted high attrition rates.
  • Heterogeneity among the results will be explored with consideration given to the following: patient characteristics, study setting, patient selection, and outcome measures.
  • Summary statistics will be derived for each study and a weighted average of the summary statistics will be computed across the studies. Statistical heterogeneity will be assessed using the I-squared measurement. The studies will be assessed clinically and methodologically to assess whether it is reasonable to meta-analyse the data. If so, the more conservative random-effects model will be used to account for small clinical and methodological variations between very similar high quality trials. Data from studies that score poorly on the quality assessment; or studies that are found to be statistically heterogeneous will not be combined. In these cases further investigation will be undertaken to identify factors that could potentially explain the heterogeneity. In addition, sensitivity analyses will be conducted to assess the impact of including these studies.

As no ‘head-to-head’ RCTs are expected (of the three drugs under consideration), a synthesis of the available evidence using indirect meta-analysis methods will be used {Caldwell et al 2005}. However, in elaborating the network to include other interventions (used either as a control intervention in pharmacological trials or as additional arms in such trials) Bayesian Mixed Treatment Comparison methods will almost certainly have to be used.{Salanti et al 2007;Lu & Ades, 2004} The analysis will incorporate both direct and indirect evidence to enable comparisons to be made between treatments, including not only estimation of all pair-wise comparisons, but also ranking of treatments in terms of clinical effectiveness. As part of the MTC analysis further issues will need to be addressed, including outcomes reported at multiple and different time points,{Lu et al 2007}, the fact that there will be heterogeneity in reporting, both in terms of outcome, e.g. BMI, weight change, hip-to-waist ratio {Nam 2007; Riley 2007}, change from baseline or otherwise {Abrams 2005}, extension of the network of evidence to include other comparators that have been evaluated in obese patients {Salanti 2007} and consistency of evidence {Lu & Ades 2006}. In addition there will be an assessment of publication bias (Sutton 2000) and exploration of whether clinical effectiveness varies with baseline obesity, e.g. BMI. The analysis will be done by the Department of Health Sciences at the University of Leicester, using the freely available software WinBUGS {Spiegelhalter, 2002}.

B. Cost-effectiveness data

A systematic review of cost-effectiveness literature will be performed with the objective of identifying and critically reviewing all English language economic evaluations of Orlistat, Sibutramine or Rimonabant. The studies identified will be used to inform assumptions concerning the structure and data sources employed within the decision-analytic model.

a. Search strategy

The search strategy will use the following terms: cost benefit, cost effectiveness, cost utility, cost consequences, cost minimisation, economic evaluation, quality of life, utility, incremental cost effectiveness analysis, incremental cost effectiveness ratio, net present value, incremental net benefit; combined with the search terms used in the effectiveness literature search strategy. Sensitive searching (e.g. economics [ec] as a floating subheading) will be used to pick up costs associated with the health conditions. The results of the searches will be stored in a Reference Manager database.

b. Inclusion criteria

English language papers reporting cost-effectiveness results in terms of cost per QALY or cost per life year gained for the three interventions Orlistat, Sibutramine or Rimonabant.

c. Screening strategy

All abstracts obtained by the computer search will be reviewed for relevance by the two economic analysts. Any disagreement will be resolved by discussion. All papers identified as relevant at the end of the abstract screening process will be obtained and entered into the quality assessment process. The results of the abstract screening will be recorded in the Reference Manager database, including the reason for excluding any paper from the quality assessment stage of the review.

Once papers selected for inclusion in the review have been obtained, a hand search of the reference lists will be undertaken to identify any potentially relevant papers not identified by the search of the literature databases. Any additional papers will be obtained and subjected to the abstract review process prior to inclusion or exclusion from the quality assessment process.

d. Quality assessment

Relevant studies will be critically appraised using the standard economic evaluation and modelling checklists.{Drummond, Eddy 1985} For papers reporting economic evaluations alongside clinical trials, the Drummond checklist will be supplemented with reference to the Good Practice Guidance produced by the ISPOR Task Force on Economic evaluations alongside clinical trials.{Weinstein 2003}

Additional searches will be conducted to identify evidence on quality of life (QoL) in obese individuals, natural history of weight gains, weight regain and relationships between weight changes and co-morbidities such as CHD and diabetes.

2.6. Decision analytic modelling

a. Analyses of an existing database of clinical information from primary care

An existing database of clinical information from primary care will be analysed (spss versions 12) using usual statistical techniques. Demographics and clinical characteristics will be discussed using the main descriptive statistics: mean standard deviation, median and range. Correlations and associations between variables will be explored using the Pearson correlation coefficient with significance set at p < 0.01. (see Section 2.4 above).

b. Proposed model structure

The aim will be to examine the cost-effectiveness of the three anti-obesity agents currently licensed in the UK in terms of the incremental quality adjusted life years (QALY) gained. The systematic review of published cost-effectiveness studies together with the MTC synthesis and epidemiological modelling will be used to inform the development of a cost-effectiveness model. The form of the model will be determined by the specification of the patient pathway, the evidence from the literature reviews and the results from the GPRD and HSE evaluations. The exact clinical pathway will be determined through discussions with the clinical experts within the study team. It is likely that a Markov will be appropriate and the model structure and modelling techniques will draw on the team's experience in performing economic evaluations involving populations who are obese, and populations with diabetes and/or CVD.{Galani 2007, Ward 2007, Ara 2007, Ara 2008, Waugh 2007; Whitfield 2006} The results from the GPRD risk models will be integrated within the model structure. Where evidence permits, treatment specific transitions to co-morbidities such as cardiovascular events and diabetes will be incorporated to reflect their differing adverse effect profiles.

  1. Parameter estimates: A full list of parameters will be constructed and the clinical and cost-effectiveness literature will be searched for evidence on each parameter. The relationships between changes in BMI and co-morbidities such as CVD and diabetes will be informed by the epidemiological model using the results of the GPRD and the HSE analyses while the results of the Bayesian Mixed Treatment Comparison will inform clinical efficacy. Health related quality of life evidence will be sought for each health state. Data on cost parameters will be obtained from national data sources such as the NHS Reference cost data set and the PSSRU Costs of Health and Social Care.{Netten, Reference costs} Only direct costs relevant to the NHS and PSS will be included in the health economic analysis. All costs and benefits will be discounted at 3.5%. Additional searches will be undertaken for key parameters in addition to those listed above.
  2. Valuation of health outcomes: A recently published review of utility values for obesity found that, while studies showed a negative relationship between Body Mass Index and utility, there was a wide variation in the estimates.{Dixon 2004} Dixon et al. concluded the choice of utility measure can be instrumental in whether the cost per quality adjusted life year estimate falls above or below a funding threshold. The scoping search indicated that published studies do not always use the generic preference-based measures of health required to meet the proposed NICE reference case for economic evaluations (the EQ-5D). The utility review will be updated and where possible non EQ-5D quality of life values will be mapped onto the EQ-5D generic preference-based index using published relationships of standard mapping techniques.{Ara 2008} {Brazier 2004}

c. Presentation of model results

The model results will be presented both in terms of the costs and consequences of each individual agent prescribed in conjunction with lifestyle advice such as diet and exercise as currently offered in primary care within the UK. Results will also be presented in terms of incremental cost per life year and incremental cost per QALY for Orlistat vs. Sibutamine vs. Rimonabant.

  1. Cost–consequence analyses: The model will be constructed to evaluate the differential impact on clinically relevant outcomes such as cardiovascular events and diabetes incidence rates based on the surrogate trial outcomes such as lipid and glucose profiles and HbA1c levels. The impact of adherence and compliance for each of the treatments will be estimated using the results of the literature searches. The differing adverse event profiles will be quantified using the data from the literature searches supported by the clinical experts in the team.
  2. Uncertainty analyses: Uncertainty surrounding the health effects and costs will be explored. Simple one-way/multi-way sensitivity analysis will be undertaken to identify key determinants of cost-effectiveness. In addition, parameter uncertainty will be examined through probabilistic sensitivity analysis. Uncertainty regarding the value of each parameter in a model will be expressed as a probability distribution, and the impact of this uncertainty will be propagated through the model using Monte Carlo simulation. The results of the analysis will be presented as incremental cost-effectiveness ratios, scatterplots on the cost-effectiveness plane, and cost-effectiveness acceptability curves.

d. Analysis of value of information

The value of information (or expected value of information EVI) approach describes the costs of the current uncertainty in the results. It can be used to provide information concerning the benefits which may be foregone as a result of withdrawing treatment. The difference between the estimated costs of uncertainty can then be compared to the relevant costs of undertaking primary data collection to estimate the net benefits associated with prospective research. Global and Partial Value of Information will be conducted using the methods described by Felli and Hazen {Felli 1998; Felli 1999} and Brennan and Kharroubi {Brennan 2007} respectively. The analysis will assume lambda = £20,000 based upon the NICE Methods of Health Technology Appraisal.

Project timescales

July 2008

First project team meeting

July 2008 – December 2008

Establish systematic review protocol

Literature searches and document acquisition for systematic reviews

Critical appraisal of literature retrieved from systematic reviews

Data extraction and meta-analysis

Produce reports from systematic reviews and meta-analysis

September 2008

Obtain database of clinical practice from primary care

October 2008 – December 2008

Analyse existing database of clinical practice from primary care

Finalise decision analytic model structure

December 2008

Produce progress report

January 2009 – April 2009

Develop decision analytic model

Assess cost-effectiveness and cost–utility of the three comparators

Undertake expected value of information analyses

April 2009

Produce draft report

May 2009 – June 2009

Peer review and final amendments to report

31st July 2009

Submit final report to NCCHTA

Preliminary findings to be presented at the 17th European Conference on Obesity (May 2009)


This is a collaborative project between a wide range of experts, intended to ensure that findings are valid, reliable and feasible in the NHS clinical setting. Our team includes clinical health experts in diabetes and obesity from primary care backgrounds and methodological experts in systematic reviewing, economics, health services research, public health medicine and information retrieval. Many of the team have a strong history of working together on collaborative reviews funded by HTA, NHS Service Delivery and Organisation (SDO) and National Co-ordinating Centre for Research Methodology (NCCRM) programmes. Together they will form a panel to guide study design, literature searching and model development; to provide independent review of articles for the literature review; and to assist in writing up and disseminating the results.

Both the School of Health and Related Research (ScHARR) at the University of Sheffield and the Department of Health Sciences at the University of Leicester are multidisciplinary health services research units carrying out a full range of primary and secondary research for major funding agencies such as the Department of Health, the former NHS Executive Trent, the NHS HTA Programme, the NHS SDO Programme and the Medical Research Council. The ScHARR staff involved in this project will be Roberta Ara (project lead), John Brazier (Health Economics), Michael Gillett (Economic Modeller), and Andrew Booth (Information Resources). The Leicester staff will include: Keith Abrams (Medical Statistics), Alex Sutton (Medical Statistics), Nicola Cooper (Health Economics), Kamlesh Khunti (Clinical expert) and Melanie Davies (Clinical expert).

Team members

Roberta Ara (RA) is a research fellow and has project managed a number of HTA and consultancy studies. She has experience of modelling the cost-effectiveness of an obesity treatment (sibutramine), and several cardiovascular treatments, leading reports for NICE and the HTA. RA will be directly responsible for supervising the project and building the mathematical model and has access to the full range of technical support and experience offered by ScHARR.

John Brazier (JB) is a Professor in Health Economics and is a leading expert in health related quality of life measurements with a particular interest in preference based measures. John has extensive experience in health economics and in particular in the quality of life and has published extensively in this area. He has led and contributed to numerous HTA reviews and lectures worldwide on quality of life evidence used in economic evaluations.

Keith Abrams (KA) is a Professor of Medical Statistics and is a leading expert in the development and use of Bayesian methods in healthcare evaluation (clinical trials, meta-analyses, and comprehensive decision modelling). He has been involved in numerous HTA evidence synthesis/modelling projects, and has published extensively in both the methodological and clinical literature, and has co-authored two books on Methods for Meta-Analysis in Medical Research and Bayesian Approaches to Clinical Trials and Health-care Evaluation, together with co-editing one of the first texts on Methods in Evidence-Based Healthcare.

Alex Sutton (AS) is a Reader in Medical Statistics and a leading expert in meta-analysis with a particular interest in synthesis for decision modelling. Alex has published extensively both methodological and substantive papers on evidence synthesis and is an author on the following two well regarded books: “Meta-analysis in medical research” and “Publication bias in meta-analysis: Prevention, assessment and adjustment”.

Nicola Cooper (NC) is a senior research fellow with expertise in both health economics and medical statistics, and her research focuses on the interface of the two. She is currently undertaking an MRC fellowship in ‘The use of evidence synthesis and uncertainty modelling in economic evidence-based health related decision models’ and has applied these methods in numerous publications. Together with AS and KA, Nicola has developed and delivered many advanced 3- and 5-day courses on evidence synthesis for decision modelling worldwide.

Andrew Booth (AB) is Director of the Information Resources at ScHARR, a specialist information resource designed to support the needs of evidence based healthcare clinical effectiveness and systematic reviews. AB has extensive experience undertaking comprehensive literature searches, and has contributed to numerous systematic reviews, including various HTA reviews and NICE rapid appraisals.

Kamlesh Khunti (KK) and Melanie Davies (MD) lead a research group in the Department of Health Sciences and Cardiovascular Sciences undertaking important research into the early identification and intervention in people with diabetes and pre-diabetes. KK and MJD are co-directors of the South East Midlands Diabetes Network and are PIs on several major studies including the Leicester Ethnic Atherosclerosis and Diabetes Risk (LEADER) Study, one of the world's largest epidemiological cohort studies of diabetes. Data from this study has informed the recent proposals by the Department of Health on Vascular Screening in primary care. They are also PIs on a NIHR funded programme grant on prevention of type 2 diabetes in high risk populations. They will bring clinical expertise in metabolic syndromes (obesity, diabetes and cardiovascular disease).

Michael Gillett (MG) is a research fellow in health economic modelling and has led numerous economic evaluations for both diabetic and cardiovascular populations.

Angie Rees is an up and coming researcher in information studies. She has undertaken the searches for a number of prominent reviews and will work under the supervision of AB.


The budget will be based at a higher education institution and will attract full economic costs, so 80% support is requested. It will support 60% of the Principal Investigator's (RA) time. She will be responsible for day-to-day running of the project, the economic evaluation, writing of reports and dissemination of the findings. Keith Abrams (Medical Statistician) will co-ordinate the research at Leicester and will be involved in advising on the clinical review, the epidemiology model and the synthesis of the evidence (9%). Alex Sutton and Nicola Cooper will be involved advising on the clinical review, the epidemiology model and the synthesis of the evidence from the GPRD database (4%, 4%). A part time researcher will be employed to conduct the clinical review and epidemiological reviews (83%). Kamlesh Khunti (5%) and Melanie Davies (2%) will provide expert clinical advice during the project and will provide access to the GPRD database. Andrew Booth will advise on the design and conduct of searches and the design and conduct of the qualitative elements of the systematic review (1%). John Brazier will advise on quality of life evidence and Michael Gillett (economic modeller) will be involved in the project in an advisory capacity for the diabetes economic components (5%). Angie Rees will design and conduct the literature searches (4%). Clerical support (Andrew Tattersall) is required to co-ordinate inter-library loans (2%). Clerical staff (to be appointed) will provide administration duties (20%).

Office costs comprise of: computing consumables £339; stationary £407; postage £203; photocopying £271. The budget will cover PCs (£1,751); contributions to bibliographic database subscriptions not currently available through the University of Sheffield (either connection or to pay external providers) £200; and inter-library loans (obtaining articles from other libraries) £1,300. Also included is £15,000 for the GPRD database. The budget will also cover travel and subsistence for members of the team (£1,000) for eight meetings over the project (either in Sheffield or in Leicester). Also included are conference and travel costs for 2 members of the team to present preliminary findings at the 17th European Conference in Amsterdam (£2,400).


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Cover of What is the Clinical Effectiveness and Cost-Effectiveness of Using Drugs in Treating Obese Patients in Primary Care? A Systematic Review
What is the Clinical Effectiveness and Cost-Effectiveness of Using Drugs in Treating Obese Patients in Primary Care? A Systematic Review.
Health Technology Assessment, No. 16.5.
Ara R, Blake L, Gray L, et al.
Southampton (UK): NIHR Journals Library; 2012 Feb.


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