Deep Brain Stimulation for Parkinson’s Disease with Early Motor Complications: A UK Cost-Effectiveness Analysis
Identifieur interne : 000156 ( Pmc/Corpus ); précédent : 000155; suivant : 000157Deep Brain Stimulation for Parkinson’s Disease with Early Motor Complications: A UK Cost-Effectiveness Analysis
Auteurs : Tomasz Fundament ; Paul R. Eldridge ; Alexander L. Green ; Alan L. Whone ; Rod S. Taylor ; Adrian C. Williams ; W. M. Michael SchuepbachSource :
- PLoS ONE [ 1932-6203 ] ; 2016.
Abstract
Parkinson’s disease (PD) is a debilitating illness associated with considerable impairment of quality of life and substantial costs to health care systems. Deep brain stimulation (DBS) is an established surgical treatment option for some patients with advanced PD. The EARLYSTIM trial has recently demonstrated its clinical benefit also in patients with early motor complications. We sought to evaluate the cost-effectiveness of DBS, compared to best medical therapy (BMT), among PD patients with early onset of motor complications, from a United Kingdom (UK) payer perspective.
We developed a Markov model to represent the progression of PD as rated using the Unified Parkinson's Disease Rating Scale (UPDRS) over time in patients with early PD. Evidence sources were a systematic review of clinical evidence; data from the EARLYSTIM study; and a UK Clinical Practice Research Datalink (CPRD) dataset including DBS patients. A mapping algorithm was developed to generate utility values based on UPDRS data for each intervention. The cost-effectiveness was expressed as the incremental cost per quality-adjusted life-year (QALY). One-way and probabilistic sensitivity analyses were undertaken to explore the effect of parameter uncertainty.
Over a 15-year time horizon, DBS was predicted to lead to additional mean cost per patient of £26,799 compared with BMT (£73,077/patient versus £46,278/patient) and an additional mean 1.35 QALYs (6.69 QALYs versus 5.35 QALYs), resulting in an incremental cost-effectiveness ratio of £19,887 per QALY gained with a 99% probability of DBS being cost-effective at a threshold of £30,000/QALY. One-way sensitivity analyses suggested that the results were not significantly impacted by plausible changes in the input parameter values.
These results indicate that DBS is a cost-effective intervention in PD patients with early motor complications when compared with existing interventions, offering additional health benefits at acceptable incremental cost. This supports the extended use of DBS among patients with early onset of motor complications.
Url:
DOI: 10.1371/journal.pone.0159340
PubMed: 27441637
PubMed Central: 4956248
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PMC:4956248Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Deep Brain Stimulation for Parkinson’s Disease with Early Motor Complications: A UK Cost-Effectiveness Analysis</title><author><name sortKey="Fundament, Tomasz" sort="Fundament, Tomasz" uniqKey="Fundament T" first="Tomasz" last="Fundament">Tomasz Fundament</name><affiliation><nlm:aff id="aff001"><addr-line>HTA Consulting, Krakow, Poland</addr-line></nlm:aff></affiliation></author><author><name sortKey="Eldridge, Paul R" sort="Eldridge, Paul R" uniqKey="Eldridge P" first="Paul R." last="Eldridge">Paul R. Eldridge</name><affiliation><nlm:aff id="aff002"><addr-line>The Walton Centre NHS Foundation Trust and Liverpool University, Liverpool, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Green, Alexander L" sort="Green, Alexander L" uniqKey="Green A" first="Alexander L." last="Green">Alexander L. Green</name><affiliation><nlm:aff id="aff003"><addr-line>Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Whone, Alan L" sort="Whone, Alan L" uniqKey="Whone A" first="Alan L." last="Whone">Alan L. Whone</name><affiliation><nlm:aff id="aff004"><addr-line>Bristol Brain Centre, Southmead Hospital, Bristol, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Taylor, Rod S" sort="Taylor, Rod S" uniqKey="Taylor R" first="Rod S." last="Taylor">Rod S. Taylor</name><affiliation><nlm:aff id="aff005"><addr-line>University of Exeter Medical School, Exeter, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Williams, Adrian C" sort="Williams, Adrian C" uniqKey="Williams A" first="Adrian C." last="Williams">Adrian C. Williams</name><affiliation><nlm:aff id="aff006"><addr-line>University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Schuepbach, W M Michael" sort="Schuepbach, W M Michael" uniqKey="Schuepbach W" first="W. M. Michael" last="Schuepbach">W. M. Michael Schuepbach</name><affiliation><nlm:aff id="aff007"><addr-line>Movement Disorders Center, Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff008"><addr-line>Assistance Publique Hôpitaux de Paris, Centre d’Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Département de Neurologie, Université Pierre et Marie Curie–Paris 6 et INSERM, CHU Pitié-Salpêtrière, Paris, France</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27441637</idno><idno type="pmc">4956248</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4956248</idno><idno type="RBID">PMC:4956248</idno><idno type="doi">10.1371/journal.pone.0159340</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000156</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000156</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Deep Brain Stimulation for Parkinson’s Disease with Early Motor Complications: A UK Cost-Effectiveness Analysis</title><author><name sortKey="Fundament, Tomasz" sort="Fundament, Tomasz" uniqKey="Fundament T" first="Tomasz" last="Fundament">Tomasz Fundament</name><affiliation><nlm:aff id="aff001"><addr-line>HTA Consulting, Krakow, Poland</addr-line></nlm:aff></affiliation></author><author><name sortKey="Eldridge, Paul R" sort="Eldridge, Paul R" uniqKey="Eldridge P" first="Paul R." last="Eldridge">Paul R. Eldridge</name><affiliation><nlm:aff id="aff002"><addr-line>The Walton Centre NHS Foundation Trust and Liverpool University, Liverpool, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Green, Alexander L" sort="Green, Alexander L" uniqKey="Green A" first="Alexander L." last="Green">Alexander L. Green</name><affiliation><nlm:aff id="aff003"><addr-line>Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Whone, Alan L" sort="Whone, Alan L" uniqKey="Whone A" first="Alan L." last="Whone">Alan L. Whone</name><affiliation><nlm:aff id="aff004"><addr-line>Bristol Brain Centre, Southmead Hospital, Bristol, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Taylor, Rod S" sort="Taylor, Rod S" uniqKey="Taylor R" first="Rod S." last="Taylor">Rod S. Taylor</name><affiliation><nlm:aff id="aff005"><addr-line>University of Exeter Medical School, Exeter, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Williams, Adrian C" sort="Williams, Adrian C" uniqKey="Williams A" first="Adrian C." last="Williams">Adrian C. Williams</name><affiliation><nlm:aff id="aff006"><addr-line>University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom</addr-line></nlm:aff></affiliation></author><author><name sortKey="Schuepbach, W M Michael" sort="Schuepbach, W M Michael" uniqKey="Schuepbach W" first="W. M. Michael" last="Schuepbach">W. M. Michael Schuepbach</name><affiliation><nlm:aff id="aff007"><addr-line>Movement Disorders Center, Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="aff008"><addr-line>Assistance Publique Hôpitaux de Paris, Centre d’Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Département de Neurologie, Université Pierre et Marie Curie–Paris 6 et INSERM, CHU Pitié-Salpêtrière, Paris, France</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Background</title><p>Parkinson’s disease (PD) is a debilitating illness associated with considerable impairment of quality of life and substantial costs to health care systems. Deep brain stimulation (DBS) is an established surgical treatment option for some patients with advanced PD. The EARLYSTIM trial has recently demonstrated its clinical benefit also in patients with early motor complications. We sought to evaluate the cost-effectiveness of DBS, compared to best medical therapy (BMT), among PD patients with early onset of motor complications, from a United Kingdom (UK) payer perspective.</p></sec><sec id="sec002"><title>Methods</title><p>We developed a Markov model to represent the progression of PD as rated using the Unified Parkinson's Disease Rating Scale (UPDRS) over time in patients with early PD. Evidence sources were a systematic review of clinical evidence; data from the EARLYSTIM study; and a UK Clinical Practice Research Datalink (CPRD) dataset including DBS patients. A mapping algorithm was developed to generate utility values based on UPDRS data for each intervention. The cost-effectiveness was expressed as the incremental cost per quality-adjusted life-year (QALY). One-way and probabilistic sensitivity analyses were undertaken to explore the effect of parameter uncertainty.</p></sec><sec id="sec003"><title>Results</title><p>Over a 15-year time horizon, DBS was predicted to lead to additional mean cost per patient of £26,799 compared with BMT (£73,077/patient versus £46,278/patient) and an additional mean 1.35 QALYs (6.69 QALYs versus 5.35 QALYs), resulting in an incremental cost-effectiveness ratio of £19,887 per QALY gained with a 99% probability of DBS being cost-effective at a threshold of £30,000/QALY. One-way sensitivity analyses suggested that the results were not significantly impacted by plausible changes in the input parameter values.</p></sec><sec id="sec004"><title>Conclusion</title><p>These results indicate that DBS is a cost-effective intervention in PD patients with early motor complications when compared with existing interventions, offering additional health benefits at acceptable incremental cost. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, CA USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27441637</article-id><article-id pub-id-type="pmc">4956248</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0159340</article-id><article-id pub-id-type="publisher-id">PONE-D-16-13291</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Neurology</subject><subj-group><subject>Neurodegenerative Diseases</subject><subj-group><subject>Movement Disorders</subject><subj-group><subject>Parkinson Disease</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Bioassays and Physiological Analysis</subject><subj-group><subject>Electrophysiological Techniques</subject><subj-group><subject>Brain Electrophysiology</subject><subj-group><subject>Deep-Brain Stimulation</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Physiology</subject><subj-group><subject>Electrophysiology</subject><subj-group><subject>Neurophysiology</subject><subj-group><subject>Brain Electrophysiology</subject><subj-group><subject>Deep-Brain Stimulation</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Physiology</subject><subj-group><subject>Electrophysiology</subject><subj-group><subject>Neurophysiology</subject><subj-group><subject>Brain Electrophysiology</subject><subj-group><subject>Deep-Brain Stimulation</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Neuroscience</subject><subj-group><subject>Neurophysiology</subject><subj-group><subject>Brain Electrophysiology</subject><subj-group><subject>Deep-Brain Stimulation</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Neuroscience</subject><subj-group><subject>Brain Mapping</subject><subj-group><subject>Deep-Brain Stimulation</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Social Sciences</subject><subj-group><subject>Economics</subject><subj-group><subject>Economic Analysis</subject><subj-group><subject>Cost-Effectiveness Analysis</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Health Care</subject><subj-group><subject>Quality of Life</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Social Sciences</subject><subj-group><subject>Economics</subject><subj-group><subject>Health Economics</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Health Care</subject><subj-group><subject>Health Economics</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Pharmaceutics</subject><subj-group><subject>Drug Therapy</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>People and Places</subject><subj-group><subject>Demography</subject><subj-group><subject>Death Rates</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Population Biology</subject><subj-group><subject>Population Metrics</subject><subj-group><subject>Death Rates</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Biotechnology</subject><subj-group><subject>Medical Devices and Equipment</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Medical Devices and Equipment</subject></subj-group></subj-group></article-categories><title-group><article-title>Deep Brain Stimulation for Parkinson’s Disease with Early Motor Complications: A UK Cost-Effectiveness Analysis</article-title><alt-title alt-title-type="running-head">Cost-Effectiveness of Deep Brain Stimulation in the UK</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Fundament</surname><given-names>Tomasz</given-names></name><xref ref-type="aff" rid="aff001"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Eldridge</surname><given-names>Paul R.</given-names></name><xref ref-type="aff" rid="aff002"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Green</surname><given-names>Alexander L.</given-names></name><xref ref-type="aff" rid="aff003"><sup>3</sup></xref><xref ref-type="corresp" rid="cor001">*</xref></contrib><contrib contrib-type="author"><name><surname>Whone</surname><given-names>Alan L.</given-names></name><xref ref-type="aff" rid="aff004"><sup>4</sup></xref></contrib><contrib contrib-type="author"><name><surname>Taylor</surname><given-names>Rod S.</given-names></name><xref ref-type="aff" rid="aff005"><sup>5</sup></xref></contrib><contrib contrib-type="author"><name><surname>Williams</surname><given-names>Adrian C.</given-names></name><xref ref-type="aff" rid="aff006"><sup>6</sup></xref></contrib><contrib contrib-type="author"><name><surname>Schuepbach</surname><given-names>W. M. Michael</given-names></name><xref ref-type="aff" rid="aff007"><sup>7</sup></xref><xref ref-type="aff" rid="aff008"><sup>8</sup></xref></contrib></contrib-group><aff id="aff001"><label>1</label><addr-line>HTA Consulting, Krakow, Poland</addr-line></aff><aff id="aff002"><label>2</label><addr-line>The Walton Centre NHS Foundation Trust and Liverpool University, Liverpool, United Kingdom</addr-line></aff><aff id="aff003"><label>3</label><addr-line>Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom</addr-line></aff><aff id="aff004"><label>4</label><addr-line>Bristol Brain Centre, Southmead Hospital, Bristol, United Kingdom</addr-line></aff><aff id="aff005"><label>5</label><addr-line>University of Exeter Medical School, Exeter, United Kingdom</addr-line></aff><aff id="aff006"><label>6</label><addr-line>University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom</addr-line></aff><aff id="aff007"><label>7</label><addr-line>Movement Disorders Center, Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland</addr-line></aff><aff id="aff008"><label>8</label><addr-line>Assistance Publique Hôpitaux de Paris, Centre d’Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Département de Neurologie, Université Pierre et Marie Curie–Paris 6 et INSERM, CHU Pitié-Salpêtrière, Paris, France</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Kassubek</surname><given-names>Jan</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>University of Ulm, GERMANY</addr-line></aff><author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>The authors of this manuscript have read the journal’s policy and have the following competing interests: Medtronic funded the development of the model. TF is an employee of HTA Consulting, a health economic consultancy firm that received consulting fees from Medtronic International Sàrl for their work on this project. PRE has received honoraria for consulting services and meeting sponsorship from Medtronic, St. Jude Medical, Boston Scientific and Nevro. He has also served as a member of the UK adult commissioning group for Neurosurgery. ALG has received honoraria for consulting services from Medtronic, St. Jude Medical and Boston Scientific. Over the last 12 months, he has also been faculty on two St. Jude Medical courses (DRG stimulation) and attended an advisory board with Boston Scientific. ALW has received honoraria from Medtronic for consulting services. ACW has received honoraria from Medtronic for consulting services. RST has received honoraria from Medtronic for consulting services. WMMS has received lecture fees from Medtronic, and travel reimbursements from Merz Pharma, Boston Scientific, Actelion, Zambon and Medtronic. He has been serving as a consultant for Medtronic, Aleva, Zambon and Lundbeck. He has received unrestricted research grants from Medtronic, Boston Scientific and Actelion. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.</p></fn><fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: TF PRE ALG ALW RST ACW WMMS. Analyzed the data: TF. Wrote the paper: TF PRE ALG ALW RST ACW WMMS. Model development: TF. Interpretation of data: TF PRE ALG ALW RST ACW WMMS.</p></fn><corresp id="cor001">* E-mail: <email>alex.green@nds.ox.ac.uk</email></corresp></author-notes><pub-date pub-type="epub"><day>21</day><month>7</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>11</volume><issue>7</issue><elocation-id>e0159340</elocation-id><history><date date-type="received"><day>29</day><month>4</month><year>2016</year></date><date date-type="accepted"><day>30</day><month>6</month><year>2016</year></date></history><permissions><copyright-statement>© 2016 Fundament et al</copyright-statement><copyright-year>2016</copyright-year><copyright-holder>Fundament et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="pone.0159340.pdf"></self-uri><abstract><sec id="sec001"><title>Background</title><p>Parkinson’s disease (PD) is a debilitating illness associated with considerable impairment of quality of life and substantial costs to health care systems. Deep brain stimulation (DBS) is an established surgical treatment option for some patients with advanced PD. The EARLYSTIM trial has recently demonstrated its clinical benefit also in patients with early motor complications. We sought to evaluate the cost-effectiveness of DBS, compared to best medical therapy (BMT), among PD patients with early onset of motor complications, from a United Kingdom (UK) payer perspective.</p></sec><sec id="sec002"><title>Methods</title><p>We developed a Markov model to represent the progression of PD as rated using the Unified Parkinson's Disease Rating Scale (UPDRS) over time in patients with early PD. Evidence sources were a systematic review of clinical evidence; data from the EARLYSTIM study; and a UK Clinical Practice Research Datalink (CPRD) dataset including DBS patients. A mapping algorithm was developed to generate utility values based on UPDRS data for each intervention. The cost-effectiveness was expressed as the incremental cost per quality-adjusted life-year (QALY). One-way and probabilistic sensitivity analyses were undertaken to explore the effect of parameter uncertainty.</p></sec><sec id="sec003"><title>Results</title><p>Over a 15-year time horizon, DBS was predicted to lead to additional mean cost per patient of £26,799 compared with BMT (£73,077/patient versus £46,278/patient) and an additional mean 1.35 QALYs (6.69 QALYs versus 5.35 QALYs), resulting in an incremental cost-effectiveness ratio of £19,887 per QALY gained with a 99% probability of DBS being cost-effective at a threshold of £30,000/QALY. One-way sensitivity analyses suggested that the results were not significantly impacted by plausible changes in the input parameter values.</p></sec><sec id="sec004"><title>Conclusion</title><p>These results indicate that DBS is a cost-effective intervention in PD patients with early motor complications when compared with existing interventions, offering additional health benefits at acceptable incremental cost. This supports the extended use of DBS among patients with early onset of motor complications.</p></sec></abstract><funding-group><funding-statement>Medtronic funded the development of the model, including consulting fees to physicians and health economic specialists, sponsored a medical writer and reviewed the manuscript. The funders provided input on the study design, decision to publish, and preparation of the manuscript. HTA Consulting provided support in the form of salaries for authors [TF], and staff resources to support evidence review and synthesis. They did not have any additional role in the study design and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.</funding-statement></funding-group><counts><fig-count count="7"></fig-count><table-count count="1"></table-count><page-count count="18"></page-count></counts><custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name><meta-value>All relevant data are within the paper and its Supporting Information files. A more detailed report of this study will be made available online for interested researchers, providing additional details of the analysis and methods.</meta-value></custom-meta></custom-meta-group></article-meta><notes><title>Data Availability</title><p>All relevant data are within the paper and its Supporting Information files. A more detailed report of this study will be made available online for interested researchers, providing additional details of the analysis and methods.</p></notes></front><body><sec sec-type="intro" id="sec005"><title>Introduction</title><p>Parkinson’s disease (PD) is a chronic progressive neurodegenerative disorder involving dopaminergic neurons, which results in a lack of the neurotransmitter dopamine. Clinically the hallmark of PD is a movement disorder with bradykinesia, rigidity and often rest tremor, although non-motor signs and symptoms are also common [<xref rid="pone.0159340.ref001" ref-type="bibr">1</xref>,<xref rid="pone.0159340.ref002" ref-type="bibr">2</xref>]. A recent-meta-analysis concluded that the worldwide prevalence of PD is around 315 cases per 100,000 population [<xref rid="pone.0159340.ref003" ref-type="bibr">3</xref>]. During early stages of the disease, patients are effectively treated with oral medication such as levodopa; however, over time, medication-induced motor complications such as unpredictable fluctuations in motor symptoms and abnormal involuntary movements (dyskinesias) develop [<xref rid="pone.0159340.ref004" ref-type="bibr">4</xref>]. Progressively, the disease leads to increasingly severe motor signs, worsening of medication-related complications and a decrease in the time between ‘off’ periods when symptoms are not well-controlled. In advanced stages axial motor signs such as impaired balance and gait that respond less favourably to levodopa or DBS become more prevalent and non-motor, especially psychiatric and cognitive problems cause significant loss of quality of life [<xref rid="pone.0159340.ref004" ref-type="bibr">4</xref>–<xref rid="pone.0159340.ref008" ref-type="bibr">8</xref>]. Thus, there is a time window between the occurrence of levodopa-induced motor complications and the development of levodopa-resistant symptoms of PD when DBS can potentially improve the patients’ condition.</p><p>PD imposes a significant burden upon patients’ health-related quality of life [<xref rid="pone.0159340.ref009" ref-type="bibr">9</xref>–<xref rid="pone.0159340.ref015" ref-type="bibr">15</xref>], and creates a major economic burden for health care systems, driven mainly by hospitalisations and medication [<xref rid="pone.0159340.ref009" ref-type="bibr">9</xref>,<xref rid="pone.0159340.ref016" ref-type="bibr">16</xref>–<xref rid="pone.0159340.ref020" ref-type="bibr">20</xref>]. A broader socio-economic impact also occurs due to lost income owing to a reduced capacity to work, early retirement and institutional or unpaid care provided by patients’ relatives [<xref rid="pone.0159340.ref017" ref-type="bibr">17</xref>,<xref rid="pone.0159340.ref018" ref-type="bibr">18</xref>,<xref rid="pone.0159340.ref021" ref-type="bibr">21</xref>–<xref rid="pone.0159340.ref027" ref-type="bibr">27</xref>]. One European study has shown that a one-unit increase on the dyskinesia severity scale (Part IVa of the Unified Parkinson’s Disease Rating Scale (UPDRS)) results in additional mean combined medical and non-medical cost of €737 per patient over a 6-month period [<xref rid="pone.0159340.ref021" ref-type="bibr">21</xref>]. When indirect costs are included, this cost increases threefold [<xref rid="pone.0159340.ref027" ref-type="bibr">27</xref>]. For patients who in principle respond well to dopaminergic medication but whose benefit from oral medication is hampered by motor complications (fluctuations and dyskinesia), treatment options include deep brain stimulation (DBS), a surgical treatment which involves the implantation of a device for electrical stimulation of precise areas in the brain. Modulation of the activity of specific target structures in the brain results in improvement of certain parkinsonian motor signs. Current guidelines in the United Kingdom (UK) from the National Institute for Health and Care Excellence (NICE) recommend the use of DBS. These guidelines are currently being updated and will consider all relevant interventions including DBS [<xref rid="pone.0159340.ref028" ref-type="bibr">28</xref>].</p><p>Randomised controlled trial (level I) evidence support the use of DBS in advanced PD [<xref rid="pone.0159340.ref029" ref-type="bibr">29</xref>–<xref rid="pone.0159340.ref031" ref-type="bibr">31</xref>]. Previous economic studies have concluded that DBS is a cost-effective intervention, when compared with best medical therapy (BMT) among patients with advanced PD [<xref rid="pone.0159340.ref032" ref-type="bibr">32</xref>–<xref rid="pone.0159340.ref035" ref-type="bibr">35</xref>]. Furthermore, recent clinical evidence (the EARLYSTIM trial) has demonstrated that DBS is also effective in PD patients earlier in the disease course, with recent onset motor complications [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>,<xref rid="pone.0159340.ref037" ref-type="bibr">37</xref>]. Significant improvements were observed compared with BMT in motor disability, activities of daily living, levodopa-induced motor complications and time with good mobility [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>].</p><p>This study sought to assess the cost effectiveness of DBS versus BMT in treating PD from early onset of motor complications from a UK payer (National Health Service) perspective, employing a novel approach of associating UPDRS subscales with health-related quality of life to capture the multi-faceted aspects of PD.</p></sec><sec sec-type="materials|methods" id="sec006"><title>Materials and Methods</title><p>This economic modelling study was undertaken and reported in accord with the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) best practice modelling guidelines [<xref rid="pone.0159340.ref038" ref-type="bibr">38</xref>].</p><sec id="sec007"><title>Overview of the economic model</title><p>We developed a Markov (state-transition) model to calculate the costs and health outcomes (in terms of life-years and quality-adjusted life-years [QALYs]) associated with a range of interventions for PD patients. Specifically, the model considered two treatment options:</p><list list-type="bullet"><list-item><p>BMT: BMT alone</p></list-item><list-item><p>DBS: DBS in combination with BMT.</p></list-item></list><p>Baseline characteristics for patients for both treatment options were based upon data from the EARLYSTIM study [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>], i.e. all patients were assumed to have PD with early motor complications at model entry (i.e. those with motor fluctuations or dyskinesias present for 3 years or less). The model was developed in Microsoft Excel (Microsoft Corporation, Redmond, MA, USA), and in the base-case analysis a 15-year horizon was used to capture long-term results and progression to more advanced disease stages. A lifetime horizon in the base-case analyses was not considered appropriate given the uncertainty in long-term outcomes for patients on the therapies evaluated. A one-year cycle length was used for transitions between health states, and a half-cycle correction was applied to reflect the fact that patients move between states, on average, halfway through a cycle [<xref rid="pone.0159340.ref039" ref-type="bibr">39</xref>]. Health states were based around treatment interventions (see <xref ref-type="fig" rid="pone.0159340.g001">Fig 1</xref>), and for each treatment, disease progression was modelled according to changes in the UPDRS domain scores (Parts I to IV). The changes in UPDRS domain scores were recorded over time within the model, but were not explicitly used to derive health states. Health-related quality of life was accounted for using an existing mapping algorithm (in the short-term), and via the development of a new algorithm to link UPDRS scores to the Euroqol-5D (EQ-5D) in the long-term (see below). Costs and QALYs were both discounted at 3.5% per year, according to NICE methods guidance [<xref rid="pone.0159340.ref040" ref-type="bibr">40</xref>]. A systematic literature review was undertaken to identify relevant clinical data for each intervention; unit cost data were sourced from device price lists, national drug prices, hospital payment tariffs and social care cost data. Input was sought from a panel of clinical experts (PE, AG, AW, MS, AW) to ensure appropriate use of the data and for validation of the model structure and assumptions. A full table of input parameter values can be found in the Supplementary Information (<xref ref-type="supplementary-material" rid="pone.0159340.s003">S1 Table</xref>).</p><fig id="pone.0159340.g001" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g001</object-id><label>Fig 1</label><caption><title>Model diagram BMT = best medical therapy; DBS = Deep brain stimulation.</title></caption><graphic xlink:href="pone.0159340.g001"></graphic></fig></sec><sec id="sec008"><title>Model structure and treatment comparisons</title><p><xref ref-type="fig" rid="pone.0159340.g001">Fig 1</xref> shows the model structure and health states for each intervention.</p><p>DBS patients would continue therapy until withdrawal, after which they would continue with BMT until the end of the model horizon or until death. No further interventions were modelled for patients withdrawing from DBS.</p></sec><sec id="sec009"><title>Clinical data</title><p>A systematic literature review (including searches of PubMed, Embase, and The Cochrane Library) was undertaken to identify relevant short- and long-term clinical outcomes data for PD patients, and for each of interventions considered in the analysis. The systematic review considered studies among adult patients with PD which reported mean UPDRS scores at specified time points in the ‘ON’ state (i.e. on medication) (details of the systematic review are available in the supplementary information).</p><p>Disease progression and treatment effectiveness were modelled in terms of changes in UPDRS scores (Parts I to IV) over time for each intervention. Baseline characteristics of patients (in terms of mean UPDRS scores on each domain, and with a mean age of 52 years) in the model were based on pooled data from the DBS and BMT treatment groups of the EARLYSTIM study [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>]. In the DBS and BMT treatment groups of the EARLYSTIM study, UPDRS domain scores were collected at treatment initiation and at 5, 12 and 24 months, which were used to calculate the percentage change from baseline in each domain score.</p><p>Disease progression beyond two years was modelled in a uniform way across all treatments, due to a lack of consistent long-term data on UPDRS outcomes for each intervention. Each UPDRS domain was modelled using data pooled from various studies, since no studies reported all domains consistently. Data from studies with a BMT treatment group were used to model the annual progression rate of UPDRS Parts I, II and IV [<xref rid="pone.0159340.ref005" ref-type="bibr">5</xref>,<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>,<xref rid="pone.0159340.ref041" ref-type="bibr">41</xref>–<xref rid="pone.0159340.ref044" ref-type="bibr">44</xref>]; a long-term study of DBS was used to represent progression of UPDRS III [<xref rid="pone.0159340.ref045" ref-type="bibr">45</xref>]. These rates were applied to both treatment options after two years, with the exception of UPDRS Part IV score. Clinical evidence suggests that UPDRS Part IV improvements may be more long-term for DBS patients [<xref rid="pone.0159340.ref045" ref-type="bibr">45</xref>–<xref rid="pone.0159340.ref052" ref-type="bibr">52</xref>]; in particular, two studies have demonstrated UPDRS IV scores at 8 years which are significantly better than baseline scores and not significantly different from corresponding scores at one year [<xref rid="pone.0159340.ref050" ref-type="bibr">50</xref>,<xref rid="pone.0159340.ref051" ref-type="bibr">51</xref>]. On the basis of this evidence, the clinical expert panel advised an extension of DBS response for this outcome; thus, the model held the two-year Part IV score constant for a further eight years, after which progression occurred at the same rate as for BMT. Alternative assumptions regarding the long-term effect of DBS upon UPDRS IV scores were tested in sensitivity analyses.</p></sec><sec id="sec010"><title>Mortality data</title><p>Mortality was incorporated using a two-step approach. Firstly, an age- and gender-specific baseline mortality risk was applied using UK all-cause mortality rates [<xref rid="pone.0159340.ref053" ref-type="bibr">53</xref>], with patients entering the model at age 52.5 years, based on the EARLYSTIM study [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>]. Whilst evidence suggests that in the early stages of PD, mortality is not significantly different to that of the general population [<xref rid="pone.0159340.ref054" ref-type="bibr">54</xref>–<xref rid="pone.0159340.ref056" ref-type="bibr">56</xref>], a recent review of mortality data in PD indicated that patients with advanced disease have a higher mortality risk [<xref rid="pone.0159340.ref057" ref-type="bibr">57</xref>]. Using data from studies reporting the relationship between UPDRS Part III and mortality, a 10-point increase in the UPDRS Part III score was associated with an increased mortality risk by applying a hazard ratio of 1.31 to the baseline risk [<xref rid="pone.0159340.ref058" ref-type="bibr">58</xref>,<xref rid="pone.0159340.ref059" ref-type="bibr">59</xref>,<xref rid="pone.0159340.ref060" ref-type="bibr">60</xref>]. This increased risk was applied to patients with a UPDRS Part III score of 15 or more, to reflect the impact only amongst patients with advanced disease.</p></sec><sec id="sec011"><title>Adverse events</title><p>The model accounted for both treatment-specific and disease-related adverse events (AEs). Three AE types among DBS patients were included, i.e. surgery-related AEs (such as bleeding events and infections); hardware-related AEs (e.g. lead fractures and migrations); and other AEs such as worsening of mobility. Serious adverse event data from the EARLYSTIM study were used to inform the frequency of each event type [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>]. Surgery-related AEs were modelled to only occur in the first two years following implantation; hardware-related and other AEs were associated with an ongoing risk. For BMT, EARLYSTIM study data were again used to model incidence of serious AEs, including worsening of mobility and motor fluctuations [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>]. These risks were assumed to be constant over time.</p><p>PD progression is associated with increasing postural instability, leading to falls and in some cases serious injury to patients. To reflect the risk of falling, we pooled data from a series of studies to define the baseline proportion of patients falling per year as 42.78% [<xref rid="pone.0159340.ref061" ref-type="bibr">61</xref>–<xref rid="pone.0159340.ref075" ref-type="bibr">75</xref>]. An odds ratio of 1.07 for each point increase in UPDRS III score was then applied, derived from three studies of fall incidence [<xref rid="pone.0159340.ref061" ref-type="bibr">61</xref>,<xref rid="pone.0159340.ref064" ref-type="bibr">64</xref>,<xref rid="pone.0159340.ref066" ref-type="bibr">66</xref>]. Of these falls, 50.9% were assumed to require hospitalisation of the patient [<xref rid="pone.0159340.ref062" ref-type="bibr">62</xref>,<xref rid="pone.0159340.ref065" ref-type="bibr">65</xref>,<xref rid="pone.0159340.ref072" ref-type="bibr">72</xref>]. The clinical panel suggested that withdrawal from DBS is rare; therefore, we modelled DBS withdrawal only as a consequence of specific adverse events. Withdrawal probabilities in the other treatment groups were calculated based on studies reporting such data. Upon withdrawal from DBS patients were assumed to receive BMT until death or the end of the model horizon, with UPDRS scores reflecting this change of treatment.</p></sec><sec id="sec012"><title>Quality of life data</title><p>UK guidelines for economic evaluations state that health outcomes should be expressed in terms of QALYs, with the EuroQol-5D (EQ-5D) being the preferred measure of health-related quality of life [<xref rid="pone.0159340.ref040" ref-type="bibr">40</xref>]. EQ-5D data were not collected in the EARLYSTIM study, and we therefore used a published algorithm to map from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) data from EARLYSTIM to the EQ-5D [<xref rid="pone.0159340.ref076" ref-type="bibr">76</xref>]. The algorithm used a multinomial logistic regression approach to predict the spread of patients between the three levels of each of the five domains of the EQ-5D. These values were then used to derive a utility index for a UK population [<xref rid="pone.0159340.ref077" ref-type="bibr">77</xref>], with a utility weight calculated for DBS and BMT patients separately by averaging across the data from months 5 and 12 (for the year 1 utility) and months 12 and 24 (for the year 2 utility) in the EARLYSTIM study. These utility weights were applied in the first two years of the model.</p><p>Whilst studies have demonstrated a correlation between UPDRS Parts I and II and both the EQ-5D index and the PDQ-39 [<xref rid="pone.0159340.ref078" ref-type="bibr">78</xref>,<xref rid="pone.0159340.ref079" ref-type="bibr">79</xref>], existing algorithms do not fully capture this relationship [<xref rid="pone.0159340.ref080" ref-type="bibr">80</xref>]. For this reason we developed a new algorithm to apply after the first two years of the model for both treatment groups. An iterative process was used to identify a statistical model which could accurately predict the EQ-5D index from the explanatory variables available from the EARLYSTIM study (including UPDRS domain scores, and patient gender and age). Several model types were explored, including linear regression, beta regression, and finite mixtures of linear and beta regression with a range of link functions. A comparison of model fit was made using mean error, mean absolute error, the Bayesian information criterion and the Akaike information criterion. A beta regression approach with a log link function was considered the most appropriate, as it resulted in small errors, covered the full range of utility values possible with the EQ-5D and did not produce illogical results (e.g. worse UPDRS scores leading to higher utilities). The function is given below:
<disp-formula id="pone.0159340.e001"><alternatives><graphic xlink:href="pone.0159340.e001.jpg" id="pone.0159340.e001g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M1"><mml:mi>E</mml:mi><mml:mi>Q</mml:mi><mml:mo>−</mml:mo><mml:mn>5</mml:mn><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn>1.59</mml:mn><mml:mo>*</mml:mo><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0.01721</mml:mn><mml:mo>*</mml:mo><mml:mi>M</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mo>+</mml:mo><mml:mn>0.001448</mml:mn><mml:mo>*</mml:mo><mml:mi>A</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mo>−</mml:mo><mml:mn>0.0198</mml:mn><mml:mo>*</mml:mo><mml:mi>U</mml:mi><mml:mi>P</mml:mi><mml:mi>D</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>I</mml:mi><mml:mo>−</mml:mo><mml:mn>0.00049</mml:mn><mml:mo>*</mml:mo><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>U</mml:mi><mml:mi>P</mml:mi><mml:mi>D</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>I</mml:mi><mml:mi>I</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>−</mml:mo><mml:mn>0.0178</mml:mn><mml:mo>*</mml:mo><mml:mi>U</mml:mi><mml:mi>P</mml:mi><mml:mi>D</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>I</mml:mi><mml:mi>V</mml:mi><mml:mo>−</mml:mo><mml:mn>0.2468</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msup><mml:mo>−</mml:mo><mml:mn>0.594</mml:mn></mml:math></alternatives></disp-formula>Where ‘Male’ is set to one for males and zero for females.</p><p>The algorithm was subsequently applied to patients’ changing UPDRS scores over time in the model to predict variation in health-related quality of life for the remainder of the model.</p></sec><sec id="sec013"><title>Cost and resource use data</title><p>The cost analysis was undertaken from a UK National Health Service perspective. The main cost groups included were: pre-surgery hospitalisation; device acquisition and implantation; drug acquisition (BMT); adverse event management (treatment-specific and generic events); general follow-up; treatment withdrawal; and device replacements. Hospital-related costs were based upon Payment by Results tariffs [<xref rid="pone.0159340.ref081" ref-type="bibr">81</xref>], drug costs were taken from the British National Formulary [<xref rid="pone.0159340.ref082" ref-type="bibr">82</xref>], and GP and nurse follow-up visits were taken from the Personal Social Sciences Research Unit [<xref rid="pone.0159340.ref083" ref-type="bibr">83</xref>].</p><p>For DBS, two pre-operative assessments were required to undertake tests on the patient and prepare them for the implantation procedure. A separate hospitalisation was then included at which the device, leads and extensions were implanted, with the costs of the full DBS system applied at this point (i.e. device, leads, extensions and patient programmer) [<xref rid="pone.0159340.ref084" ref-type="bibr">84</xref>]. The cost of a battery replacement was assumed to include the cost of a new device, plus a hospitalisation for the procedure, with a mean battery lifetime of 4.5 years used [<xref rid="pone.0159340.ref085" ref-type="bibr">85</xref>]. The costs of AE management were based on the frequency of serious AEs observed in the EARLYSTIM trial, with separate costs applied to surgery-related events, device-related events and other events observed in the study [<xref rid="pone.0159340.ref081" ref-type="bibr">81</xref>,<xref rid="pone.0159340.ref086" ref-type="bibr">86</xref>]. In some cases, more than one plausible payment tariff was identified, and in such instances the mean of the available tariffs was applied.</p><p>Given the range of drug options available for PD management and the lack of standardised drug protocols, we calculated drug use across treatment options for BMT using an analysis of data from the UK Clinical Practice Research Datalink (CPRD) [<xref rid="pone.0159340.ref087" ref-type="bibr">87</xref>]. Data were extracted on a total of 297 patients (270 on BMT and 27 on DBS) for the period April 1 2003 to March 31 2012, and included information on a total of 305 different PD drug formulations administered during this period. Dosing information for each patient was combined with drug unit costs from the British National Formulary [<xref rid="pone.0159340.ref082" ref-type="bibr">82</xref>], and the number of patients receiving each drug (from the CPRD) to calculate mean daily drug costs for each treatment group. The calculated drug cost per day for each treatment option was £4.16 (BMT) and £2.28 (DBS). These costs were assumed to be constant over time.</p><p>The costs of general follow-up were accounted for in each treatment group via regular neurology outpatient appointments. For patients receiving DBS, four visits were assumed in the first year of treatment to account for device programming and drug dose adjustments, with two visits assumed per year thereafter. Patients on BMT were assumed to require two visits per year for the duration of the model, except in the case of patients withdrawing from DBS, who were assumed to require four such visits in the first year after withdrawal. Home visits by a PD nurse were applied in both treatment groups with the same frequency as for the neurology outpatient visits.</p><p>PD-related falls were assumed to require hospitalisation in 50.9% of cases [<xref rid="pone.0159340.ref062" ref-type="bibr">62</xref>,<xref rid="pone.0159340.ref064" ref-type="bibr">64</xref>,<xref rid="pone.0159340.ref072" ref-type="bibr">72</xref>]. The unit costs of follow-up and falls were based on national tariffs and social service cost estimates [<xref rid="pone.0159340.ref081" ref-type="bibr">81</xref>,<xref rid="pone.0159340.ref083" ref-type="bibr">83</xref>]. Finally, the costs of any additional hospitalisations were included for each treatment option. The CPRD dataset of PD patients (364 patients, described above) was used to estimate the mean number of inpatient days per patient per year [<xref rid="pone.0159340.ref087" ref-type="bibr">87</xref>], which were adjusted for hospitalisations related to treatment and adverse events described above, and then multiplied by a cost per hospital day. The mean number of hospital days per patient was estimated as 5.97 (BMT) and 2.63 (DBS). According to expert clinical advice, these hospitalisations can be assumed to be mainly due to PD-related co-morbidities.</p><p>A full list of the unit costs used in the model is given in <xref ref-type="supplementary-material" rid="pone.0159340.s003">S1 Table</xref> in the Supporting Material.</p></sec><sec id="sec014"><title>Data Analyses</title><p>A deterministic analysis was firstly undertaken, using the mean value of each parameter to calculate the total costs, life-years and QALYs over a 15-year horizon. To allow comparison of the cost-effectiveness of the two interventions, the incremental cost-effectiveness ratio (ICER) was calculated using the following formula (‘A’ and ‘B’ refer to the two interventions being compared):
<disp-formula id="pone.0159340.e002"><alternatives><graphic xlink:href="pone.0159340.e002.jpg" id="pone.0159340.e002g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M2"><mml:mi>I</mml:mi><mml:mi>C</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>Q</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mi>Y</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>Q</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mi>Y</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:math></alternatives></disp-formula></p><p>To explore the effect of individual parameter uncertainty upon the cost-effectiveness results, extensive one-way sensitivity analyses were also undertaken, varying each parameter in turn within plausible ranges. Probabilistic sensitivity analysis (PSA) was performed to explore the joint effect of the uncertainty in all input parameter values. This involved assigning a statistical distribution to each parameter to reflect the uncertainty in its mean value. A range of distributions was used (including normal, beta, gamma and lognormal) according to the parameter type and any necessary restrictions on possible sampled values. One value was sampled from each parameter’s distribution and the model results re-calculated using these values; this process was repeated 10,000 times to provide a range of costs and QALYs for each intervention.</p></sec></sec><sec sec-type="results" id="sec015"><title>Results</title><sec id="sec016"><title>Deterministic analysis</title><p><xref ref-type="table" rid="pone.0159340.t001">Table 1</xref> shows the discounted results of the deterministic analysis, based on a 15-year time horizon.</p><table-wrap id="pone.0159340.t001" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.t001</object-id><label>Table 1</label><caption><title>Deterministic model results (discounted).</title></caption><alternatives><graphic id="pone.0159340.t001g" xlink:href="pone.0159340.t001"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col></colgroup><thead><tr><th align="left" rowspan="1" colspan="1">Treatment</th><th align="left" rowspan="1" colspan="1">Mean cost per patient</th><th align="left" rowspan="1" colspan="1">Mean QALYs gained per patient</th><th align="left" rowspan="1" colspan="1">ICER (cost per QALY gained)</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">BMT</td><td align="left" rowspan="1" colspan="1">£46,278</td><td align="left" rowspan="1" colspan="1">5.35</td><td align="left" rowspan="1" colspan="1">-</td></tr><tr><td align="left" rowspan="1" colspan="1">DBS</td><td align="left" rowspan="1" colspan="1">£73,077</td><td align="left" rowspan="1" colspan="1">6.69</td><td align="left" rowspan="1" colspan="1">£19,887</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="t001fn001"><p>QALY = quality-adjusted life-year; ICER = incremental cost-effectiveness ratio</p></fn></table-wrap-foot></table-wrap><p>DBS was predicted to lead to improved QALY outcomes and increased costs compared with BMT, leading to an ICER of £19,887 per QALY gained. In the DBS group, two-thirds of the total costs were related to device acquisition, implantation and replacement (see <xref ref-type="fig" rid="pone.0159340.g002">Fig 2</xref>).</p><fig id="pone.0159340.g002" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g002</object-id><label>Fig 2</label><caption><title>Cost breakdown by treatment.</title></caption><graphic xlink:href="pone.0159340.g002"></graphic></fig><p>Figs <xref ref-type="fig" rid="pone.0159340.g003">3</xref> and <xref ref-type="fig" rid="pone.0159340.g004">4</xref> show the cumulative costs and QALYs over the 15-year period for each intervention.</p><fig id="pone.0159340.g003" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g003</object-id><label>Fig 3</label><caption><title>Cumulative discounted costs over 15 years.</title></caption><graphic xlink:href="pone.0159340.g003"></graphic></fig><fig id="pone.0159340.g004" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g004</object-id><label>Fig 4</label><caption><title>Cumulative discounted QALYs over 15 years.</title></caption><graphic xlink:href="pone.0159340.g004"></graphic></fig><p>These results show a steady increase in the total costs and QALYs over time for each intervention. The timing of DBS battery replacements can be seen in <xref ref-type="fig" rid="pone.0159340.g003">Fig 3</xref> at regular intervals.</p></sec><sec id="sec017"><title>One-way sensitivity analysis</title><p><xref ref-type="fig" rid="pone.0159340.g005">Fig 5</xref> shows the tornado diagram for the comparison of DBS versus BMT. The variables whose uncertainty was most influential are shown towards the top of the diagram.</p><fig id="pone.0159340.g005" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g005</object-id><label>Fig 5</label><caption><title>Tornado diagram of one-way sensitivity analysis for DBS versus BMT (plausible parameter ranges).</title></caption><graphic xlink:href="pone.0159340.g005"></graphic></fig><p>The key parameters identified by the one-way sensitivity analysis were: the model time horizon; the cost of the DBS system; the duration of the effect of DBS upon the UPDRS Part IV score (when this effect was assumed to last for 2 years, a QALY gain of 1.02 was observed for DBS patients, compared with 1.35 in the base-case); and the DBS battery longevity. In no scenario, however, was the computed ICER for the comparison of DBS versus BMT in excess of £30,000 per QALY gained.</p></sec><sec id="sec018"><title>Probabilistic sensitivity analysis</title><p>The results of the probabilistic sensitivity analysis are shown in Figs <xref ref-type="fig" rid="pone.0159340.g006">6</xref> and <xref ref-type="fig" rid="pone.0159340.g007">7</xref>, with the total costs and QALYs of each intervention presented. In <xref ref-type="fig" rid="pone.0159340.g006">Fig 6</xref>, each point represents one set of results generated from the sampled input parameter values for each treatment option. <xref ref-type="fig" rid="pone.0159340.g007">Fig 7</xref> is the cost-effectiveness acceptability curve (CEAC), showing the probability that each treatment option is cost-effective across a range of willingness-to-pay thresholds.</p><fig id="pone.0159340.g006" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g006</object-id><label>Fig 6</label><caption><title>Scatter plot of probabilistic results (15-year horizon).</title><p>The red dot represents the deterministic result.</p></caption><graphic xlink:href="pone.0159340.g006"></graphic></fig><fig id="pone.0159340.g007" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0159340.g007</object-id><label>Fig 7</label><caption><title>Cost-effectiveness acceptability curve.</title></caption><graphic xlink:href="pone.0159340.g007"></graphic></fig><p>The scatter plot in <xref ref-type="fig" rid="pone.0159340.g006">Fig 6</xref> shows that, although BMT is less costly than DBS overall, it generates fewer QALYs. Each cloud of points shows the considerable variability in the total QALYs gained for each intervention, reflecting the uncertainty in the long-term disease outcomes and their impact upon health-related quality of life. At a threshold of £20,000 per QALY gained, the probability of DBS being the most cost-effective intervention was 51%, rising to 99% at a £30,000 per QALY threshold (see <xref ref-type="fig" rid="pone.0159340.g007">Fig 7</xref>).</p></sec></sec><sec sec-type="conclusions" id="sec019"><title>Discussion</title><p>This analysis was undertaken to determine the cost-effectiveness of DBS versus alternative treatment options in PD patients from early onset of motor complications on over 15 years, building on existing cost effectiveness evidence of DBS treatment in patients with more advanced disease. This analysis suggests that DBS is a cost-effective intervention with an ICER of £19,887 per QALY gained when compared with BMT and below the UK maximum willingness-to-pay threshold of £30,000 per QALY gained [<xref rid="pone.0159340.ref088" ref-type="bibr">88</xref>].</p><p>This is the first analysis using the UPDRS scale to model long-term progression based on a PD population with early onset of motor complications. It supports the majority of the existing economic evidence in more advanced populations that DBS is cost-effective compared with BMT[<xref rid="pone.0159340.ref032" ref-type="bibr">32</xref>–<xref rid="pone.0159340.ref034" ref-type="bibr">34</xref>,<xref rid="pone.0159340.ref089" ref-type="bibr">89</xref>]. The recent analysis of DBS from a Swedish societal perspective showed DBS to be cost-saving compared to BMT [<xref rid="pone.0159340.ref090" ref-type="bibr">90</xref>]. Two published analyses have looked at DBS in advanced PD from a UK health service perspective. Eggington et al found acceptable ICERs in their modelled analyses using clinical data from the Deuschl RCT [<xref rid="pone.0159340.ref029" ref-type="bibr">29</xref>,<xref rid="pone.0159340.ref033" ref-type="bibr">33</xref>]. In contrast, the analysis based on the PD SURG study found less favourable results for DBS [<xref rid="pone.0159340.ref089" ref-type="bibr">89</xref>]. The PDSURG-based analysis used a micro-costing approach to estimate costs related to DBS and BMT, and its consequences over time. However, given that the clinical study was conducted 10–16 years ago, some obsolete practices may be reflected. Outdated practices may affect costs and effects, as patient selection has improved as a result of almost 20 years of experience with DBS, and targeting is more advanced, following the availability of better imaging techniques.</p><p>QALY differences between DBS and BMT were very small, non-significant, and possibly indicative of the disease-specific quality of life benefits (PDQ-39), which were smaller than in other RCTs (13% in PD SURG versus 24% and 26% in EARLYSTIM and Deuschl (2006), respectively [<xref rid="pone.0159340.ref029" ref-type="bibr">29</xref>,<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>,<xref rid="pone.0159340.ref089" ref-type="bibr">89</xref>]. The sensitivity of the PD SURG results to changes in QALYs was demonstrated by one-way sensitivity analyses showing ICERs below willingness-to-pay thresholds when 30% higher QALY benefits were assumed for DBS. These aspects may explain differences in cost-effectiveness results between existing studies and the PD SURG analysis.</p><p>Our results are in accord with the comparative previous analyses by Dams et al [<xref rid="pone.0159340.ref032" ref-type="bibr">32</xref>] who find DBS to be highly cost-effective in a sensitivity analysis where they considered an “early” population. However, this previous analysis used an early PD population at the start of the model (in terms of the distribution of patients between Hoehn and Yahr stages), applying all other parameter values as per an advanced PD population.</p><p>The modelling approach undertaken here has strengths. We developed an economic model using UPDRS scores to represent disease progression, and created a mapping algorithm to provide a link between these scores and patients’ health-related quality of life. The UPDRS-based model reflects the multi-faceted aspects of Parkinson’s disease, and allowed the use and synthesis of clinical data from multiple studies reporting UPDRS scores as endpoints. We used two-year data from the EARLYSTIM trial to inform the initial model inputs [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>], and supplemented this with data identified via a systematic review to project long-term outcomes for each treatment option. Device costs made up the majority of the costs in the DBS treatment group; in the BMT group, drug therapy and management of co-morbidities were the main cost drivers. The ICER remained relatively stable when tested in sensitivity analyses; only in a scenario in which the time horizon was limited to five years was an ICER greater than £30,000 per QALY gained observed. Uncertainty in long-term outcomes for each treatment was fully explored and did not lead to substantial changes in the cost-effectiveness results. Probabilistic sensitivity analysis indicated a high probability of DBS being cost-effective, with much of the variability in QALY gains due to the uncertainty in long-term disease progression rates and the long-term relationship between UPDRS scores and quality of life.</p><p>We recognise that our analysis has limitations. The time horizon of 15 years was chosen as the base case in order to capture modelling of long-term health and economic outcomes of a population with early complications at the start of the model and which develop into more advanced disease at later model stages. A lifetime horizon was not considered robust enough given the limitations in the data. The patient data used came from different sources, the longest of which provided 5 year follow-up information after DBS. Thus, heterogeneity of the data sources and the lack of actual patient data beyond 5 years are limitations.</p><p>As with all health economic modelling exercises, the analysis required a number of assumptions to be made, in particular with regard to long-term UPDRS outcomes for different treatments. Wherever assumptions were needed, they were conservative, for example assuming identical long-term UPDRS progression rates for DBS and BMT patients, with the exception of UPDRS IV, in which a longer-term benefit for DBS was applied. Further long-term outcomes data are needed to fully validate these assumptions. The use of a new mapping algorithm to derive EQ-5D utilities from UPDRS scores also introduced uncertainty, as its applicability to other datasets has not been evaluated. Nevertheless, the algorithm had high internal validity and builds on existing algorithms by capturing the relationship between a broader range of UPDRS domain scores and quality of life.</p><p>Alternative therapies are available for the management of PD with motor complications. In particular, sub-cutaneous apomorphine infusion (CSAI) and continuous duodenal levodopa carbidopa infusion (CDLCI) have been the focus of studies in patients with advanced PD [<xref rid="pone.0159340.ref091" ref-type="bibr">91</xref>–<xref rid="pone.0159340.ref103" ref-type="bibr">103</xref>]. However, evidence regarding the efficacy of their use among patients with early motor symptoms is lacking, and for this reason we excluded these treatment options from this analysis. Existing economic evidence indicates that CDLCI and CSAI are more costly than both DBS and BMT, [<xref rid="pone.0159340.ref104" ref-type="bibr">104</xref>–<xref rid="pone.0159340.ref106" ref-type="bibr">106</xref>] suggesting that DBS would be a more cost-effective alternative to both of these treatments. With availability of more robust comparative clinical data, a formal economic evaluation is needed comparing all treatment options in patients with early complications.</p><p>Clinicians are faced with having to make treatment decisions for patients presenting with motor complications on a continuum of care. In particular, strict inclusion criteria apply for patients to undergo DBS, and most available data refer to DBS of the subthalamic nucleus. Many patients in whom this treatment is contraindicated may benefit from CDLCI which is typically a treatment of advanced and very advanced PD.</p><p>In conclusion, we modelled cost-effectiveness of DBS in PD patients with early motor complications over a time horizon of 15 years building on evidence from patients with advanced disease. Using a novel mapping algorithm to link disease progression with health-related quality of life, and two-year follow-up data from the EARLYSTIM trial [<xref rid="pone.0159340.ref036" ref-type="bibr">36</xref>], our analysis concludes that the incremental cost-effectiveness ratio of DBS compared to BMT is acceptable based on current thresholds. Our findings are line with the evidence for cost-effectiveness of DBS in advanced PD and provide support from an economical standpoint for the extension of existing policy recommendations to make DBS available to patients with PD with early motor complications.</p></sec><sec sec-type="supplementary-material" id="sec020"><title>Supporting Information</title><supplementary-material content-type="local-data" id="pone.0159340.s001"><label>S1 PRISMA Checklist</label><caption><title>PRISMA 2009 Checklist–Reports of 4 systematic reviews informing DBS CE model.</title><p>(PDF)</p></caption><media xlink:href="pone.0159340.s001.pdf"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pone.0159340.s002"><label>S1 Appendix</label><caption><title>Appendix–Systematic review and data analyses.</title><p>(PDF)</p></caption><media xlink:href="pone.0159340.s002.pdf"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pone.0159340.s003"><label>S1 Table</label><caption><title>Parameter input values, sources and distributions for probabilistic sensitivity analysis.</title><p>(DOCX)</p></caption><media xlink:href="pone.0159340.s003.docx"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack><p>Richard Dodel commented on a previous version of this manuscript. 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