Pub Date : 2024-05-01Epub Date: 2024-04-01DOI: 10.1007/s40273-024-01363-1
Jen-Yu Amy Chang, James B Chilcott, Nicholas R Latimer
With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.
{"title":"Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.","authors":"Jen-Yu Amy Chang, James B Chilcott, Nicholas R Latimer","doi":"10.1007/s40273-024-01363-1","DOIUrl":"10.1007/s40273-024-01363-1","url":null,"abstract":"<p><p>With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140336383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01Epub Date: 2024-02-01DOI: 10.1007/s40273-024-01358-y
Shih-Wen Lin, Sheila Shapouri, Hélène Parisé, Eric Bercaw, Mei Wu, Eunice Kim, Matthew Matasar
Objective: This study aimed to assess the budget impact of introducing fixed-duration mosunetuzumab as a treatment option for adult patients with relapsed or refractory follicular lymphoma after at least two prior systemic therapies and to estimate the total cumulative costs per patient in the USA.
Methods: A 3-year budget impact model was developed for a hypothetical 1-million-member cohort enrolled in a mixed commercial/Medicare health plan. Comparators were: axicabtagene ciloleucel, tisagenlecleucel, tazemetostat, rituximab plus lenalidomide, copanlisib, and older therapies (rituximab or obinutuzumab ± chemotherapy). Costs per patient comprised treatment-associated costs including the drug, its administration, adverse events, and routine care. Dosing and safety data were ascertained from respective package inserts and clinical trial data. Drug costs (March 2023) were estimated based on the average wholesale acquisition cost reported in AnalySource®, and all other costs were based on published sources and inflated to 2022 US dollars. Market shares were obtained from Genentech internal projections and expert opinion. Budget impact outcomes were presented on a per-member per-month basis.
Results: Compared with a scenario without mosunetuzumab, its introduction over 3 years resulted in a budget increase of $69,812 (1% increase) and an average per-member per-month budget impact of $0.0019. Among the newer therapies, mosunetuzumab had the second-lowest cumulative per patient cost (mosunetuzumab = $202,039; axicabtagene ciloleucel = $505,845; tisagenlecleucel = $476,293; rituximab plus lenalidomide = $263,520; tazemetostat = $250,665; copanlisib = $127,293) and drug costs, and its introduction only increased total drug costs by 0.1%. By year 3, the cumulative difference in the per patient cost with mosunetuzumab was -$303,805 versus axicabtagene ciloleucel, -$274,254 versus tisagenlecleucel, -$61,481 versus rituximab plus lenalidomide, -$48,625 versus tazemetostat, and $74,747 versus copanlisib. Older therapies were less costly with 3-year cumulative costs that ranged from $36,512 to $147,885.
Conclusions: Over 3 years, the estimated cumulative per patient cost of mosunetuzumab is lower than most available newer therapies, resulting in a small increase in the budget after its formulary adoption for the treatment of relapsed or refractory follicular lymphoma.
{"title":"Budget Impact of Introducing Fixed-Duration Mosunetuzumab for the Treatment of Relapsed or Refractory Follicular Lymphoma After Two or More Lines of Systemic Therapy in the USA.","authors":"Shih-Wen Lin, Sheila Shapouri, Hélène Parisé, Eric Bercaw, Mei Wu, Eunice Kim, Matthew Matasar","doi":"10.1007/s40273-024-01358-y","DOIUrl":"10.1007/s40273-024-01358-y","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to assess the budget impact of introducing fixed-duration mosunetuzumab as a treatment option for adult patients with relapsed or refractory follicular lymphoma after at least two prior systemic therapies and to estimate the total cumulative costs per patient in the USA.</p><p><strong>Methods: </strong>A 3-year budget impact model was developed for a hypothetical 1-million-member cohort enrolled in a mixed commercial/Medicare health plan. Comparators were: axicabtagene ciloleucel, tisagenlecleucel, tazemetostat, rituximab plus lenalidomide, copanlisib, and older therapies (rituximab or obinutuzumab ± chemotherapy). Costs per patient comprised treatment-associated costs including the drug, its administration, adverse events, and routine care. Dosing and safety data were ascertained from respective package inserts and clinical trial data. Drug costs (March 2023) were estimated based on the average wholesale acquisition cost reported in AnalySource<sup>®</sup>, and all other costs were based on published sources and inflated to 2022 US dollars. Market shares were obtained from Genentech internal projections and expert opinion. Budget impact outcomes were presented on a per-member per-month basis.</p><p><strong>Results: </strong>Compared with a scenario without mosunetuzumab, its introduction over 3 years resulted in a budget increase of $69,812 (1% increase) and an average per-member per-month budget impact of $0.0019. Among the newer therapies, mosunetuzumab had the second-lowest cumulative per patient cost (mosunetuzumab = $202,039; axicabtagene ciloleucel = $505,845; tisagenlecleucel = $476,293; rituximab plus lenalidomide = $263,520; tazemetostat = $250,665; copanlisib = $127,293) and drug costs, and its introduction only increased total drug costs by 0.1%. By year 3, the cumulative difference in the per patient cost with mosunetuzumab was -$303,805 versus axicabtagene ciloleucel, -$274,254 versus tisagenlecleucel, -$61,481 versus rituximab plus lenalidomide, -$48,625 versus tazemetostat, and $74,747 versus copanlisib. Older therapies were less costly with 3-year cumulative costs that ranged from $36,512 to $147,885.</p><p><strong>Conclusions: </strong>Over 3 years, the estimated cumulative per patient cost of mosunetuzumab is lower than most available newer therapies, resulting in a small increase in the budget after its formulary adoption for the treatment of relapsed or refractory follicular lymphoma.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11039538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139651373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-29DOI: 10.1007/s40273-024-01385-9
William L. Herring, Meghan E. Gallagher, Nirmish Shah, KC Morse, Deirdre Mladsi, Olivia M. Dong, Anjulika Chawla, Jennifer W. Leiding, Lixin Zhang, Clark Paramore, Biree Andemariam
Background and Objective
Gene therapies for sickle cell disease (SCD) may offer meaningful benefits for patients and society. This study evaluated the cost-effectiveness of lovotibeglogene autotemcel (lovo-cel), a one-time gene therapy administered via autologous hematopoietic stem cell transplantation, compared with common care for patients in the United States (US) with SCD aged ≥ 12 years with ≥ 4 vaso-occlusive events (VOEs) in the past 24 months.
Methods
We developed a patient-level simulation model accounting for lovo-cel and SCD-related events, complications, and mortality over a lifetime time horizon. The pivotal phase 1/2 HGB-206 clinical trial (NCT02140554) served as the basis for lovo-cel efficacy and safety. Cost, quality-of-life, and other clinical data were sourced from HGB-206 data and the literature. Analyses were conducted from US societal and third-party payer perspectives. Uncertainty was assessed through probabilistic sensitivity analysis and extensive scenario analyses.
Results
Patients treated with lovo-cel were predicted to survive 23.84 years longer on average (standard deviation [SD], 12.80) versus common care (life expectancy, 62.24 versus 38.40 years), with associated discounted patient quality-adjusted life-year (QALY) gains of 10.20 (SD, 4.10) and direct costs avoided of $1,329,201 (SD, $1,346,446) per patient. Predicted societal benefits included discounted caregiver QALY losses avoided of 1.19 (SD, 1.38) and indirect costs avoided of $540,416 (SD, $262,353) per patient. Including lovo-cel costs ($3,282,009 [SD, $29,690] per patient) resulted in incremental cost-effectiveness ratios of $191,519 and $124,051 per QALY gained from third-party payer and societal perspectives, respectively. In scenario analyses, the predicted cost-effectiveness of lovo-cel also was sensitive to baseline age and VOE frequency and to the proportion of patients achieving and maintaining complete resolution of VOEs.
Conclusions
Our analysis of lovo-cel gene therapy compared with common care for patients in the US with SCD with recurrent VOEs estimated meaningful improvements in survival, quality of life, and other clinical outcomes accompanied by increased overall costs for the health care system and for broader society. The predicted economic value of lovo-cel gene therapy was influenced by uncertainty in long-term clinical effects and by positive spillover effects on patient productivity and caregiver burden.
{"title":"Cost-Effectiveness of Lovotibeglogene Autotemcel (Lovo-Cel) Gene Therapy for Patients with Sickle Cell Disease and Recurrent Vaso-Occlusive Events in the United States","authors":"William L. Herring, Meghan E. Gallagher, Nirmish Shah, KC Morse, Deirdre Mladsi, Olivia M. Dong, Anjulika Chawla, Jennifer W. Leiding, Lixin Zhang, Clark Paramore, Biree Andemariam","doi":"10.1007/s40273-024-01385-9","DOIUrl":"https://doi.org/10.1007/s40273-024-01385-9","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background and Objective</h3><p>Gene therapies for sickle cell disease (SCD) may offer meaningful benefits for patients and society. This study evaluated the cost-effectiveness of lovotibeglogene autotemcel (lovo-cel), a one-time gene therapy administered via autologous hematopoietic stem cell transplantation, compared with common care for patients in the United States (US) with SCD aged ≥ 12 years with ≥ 4 vaso-occlusive events (VOEs) in the past 24 months.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We developed a patient-level simulation model accounting for lovo-cel and SCD-related events, complications, and mortality over a lifetime time horizon. The pivotal phase 1/2 HGB-206 clinical trial (NCT02140554) served as the basis for lovo-cel efficacy and safety. Cost, quality-of-life, and other clinical data were sourced from HGB-206 data and the literature. Analyses were conducted from US societal and third-party payer perspectives. Uncertainty was assessed through probabilistic sensitivity analysis and extensive scenario analyses.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Patients treated with lovo-cel were predicted to survive 23.84 years longer on average (standard deviation [SD], 12.80) versus common care (life expectancy, 62.24 versus 38.40 years), with associated discounted patient quality-adjusted life-year (QALY) gains of 10.20 (SD, 4.10) and direct costs avoided of $1,329,201 (SD, $1,346,446) per patient. Predicted societal benefits included discounted caregiver QALY losses avoided of 1.19 (SD, 1.38) and indirect costs avoided of $540,416 (SD, $262,353) per patient. Including lovo-cel costs ($3,282,009 [SD, $29,690] per patient) resulted in incremental cost-effectiveness ratios of $191,519 and $124,051 per QALY gained from third-party payer and societal perspectives, respectively. In scenario analyses, the predicted cost-effectiveness of lovo-cel also was sensitive to baseline age and VOE frequency and to the proportion of patients achieving and maintaining complete resolution of VOEs.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>Our analysis of lovo-cel gene therapy compared with common care for patients in the US with SCD with recurrent VOEs estimated meaningful improvements in survival, quality of life, and other clinical outcomes accompanied by increased overall costs for the health care system and for broader society. The predicted economic value of lovo-cel gene therapy was influenced by uncertainty in long-term clinical effects and by positive spillover effects on patient productivity and caregiver burden.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-27DOI: 10.1007/s40273-024-01377-9
Gemma E. Shields, Paul Clarkson, Ash Bullement, Warren Stevens, Mark Wilberforce, Tracey Farragher, Arpana Verma, Linda M. Davies
Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.
{"title":"Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature","authors":"Gemma E. Shields, Paul Clarkson, Ash Bullement, Warren Stevens, Mark Wilberforce, Tracey Farragher, Arpana Verma, Linda M. Davies","doi":"10.1007/s40273-024-01377-9","DOIUrl":"https://doi.org/10.1007/s40273-024-01377-9","url":null,"abstract":"<p>Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-14DOI: 10.1007/s40273-024-01370-2
Tobias Sydendal Grand, Shijie Ren, James Hall, Daniel Oudin Åström, Stephane Regnier, Praveen Thokala
Background and Objectives
There are significant challenges when obtaining clinical and economic evidence for health technology assessments of rare diseases. Many of them have been highlighted in previous systematic reviews but they have not been summarised in a comprehensive manner. For all stakeholders working with rare diseases, it is important to be aware and understand these issues. The objective of this review is to identify the main challenges for the economic evaluation of orphan drugs in rare diseases.
Methods
An umbrella review of systematic reviews of economic studies concerned with orphan and ultra-orphan drugs was conducted. Studies that were not systematic reviews, or on advanced therapeutic medicinal products, personalised medicines or other interventions that were not considered orphan drugs were excluded. The database searches included publications from 2010 to 2023, and were conducted in MEDLINE, EMBASE and the Cochrane library using filters for systematic reviews, and economic evaluations and models. These filters were combined with search terms for rare diseases and orphan drugs. A hand search supplemented the literature searches. The findings were reported by a compliant Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.
Results
Two hundred and eighty-two records were identified from the literature searches, of which 64 were duplicates, whereas five reviews were identified from the hand search. A total of 36 reviews were included after screening against inclusion/exclusion criteria, 35 from literature searches and one from hand searching. Of those studies 1, 27 and 8 were low, moderate and high quality, respectively. The reviews highlight the scarcity of evidence for health economic parameters, for example, clinical effectiveness, costs, quality of life and the natural history of disease. Health economic evaluations such as cost-effectiveness and budget-impact analyses were scarce, and generally low-to-moderate quality. The causes were limited health economic parameters, together with publications bias, especially for cost-effectiveness analyses.
Conclusions
The results highlighted issues around a considerable paucity of evidence for economic evaluations and few cost-effectiveness analyses, supporting the notion that a paucity of evidence makes economic evaluations of rare diseases more challenging compared with more prevalent diseases. Furthermore, we provide recommendations for more sustainable approaches in economic evaluations of rare diseases.
{"title":"Issues, Challenges and Opportunities for Economic Evaluations of Orphan Drugs in Rare Diseases: An Umbrella Review","authors":"Tobias Sydendal Grand, Shijie Ren, James Hall, Daniel Oudin Åström, Stephane Regnier, Praveen Thokala","doi":"10.1007/s40273-024-01370-2","DOIUrl":"https://doi.org/10.1007/s40273-024-01370-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background and Objectives</h3><p>There are significant challenges when obtaining clinical and economic evidence for health technology assessments of rare diseases. Many of them have been highlighted in previous systematic reviews but they have not been summarised in a comprehensive manner. For all stakeholders working with rare diseases, it is important to be aware and understand these issues. The objective of this review is to identify the main challenges for the economic evaluation of orphan drugs in rare diseases.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>An umbrella review of systematic reviews of economic studies concerned with orphan and ultra-orphan drugs was conducted. Studies that were not systematic reviews, or on advanced therapeutic medicinal products, personalised medicines or other interventions that were not considered orphan drugs were excluded. The database searches included publications from 2010 to 2023, and were conducted in MEDLINE, EMBASE and the Cochrane library using filters for systematic reviews, and economic evaluations and models. These filters were combined with search terms for rare diseases and orphan drugs. A hand search supplemented the literature searches. The findings were reported by a compliant Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Two hundred and eighty-two records were identified from the literature searches, of which 64 were duplicates, whereas five reviews were identified from the hand search. A total of 36 reviews were included after screening against inclusion/exclusion criteria, 35 from literature searches and one from hand searching. Of those studies 1, 27 and 8 were low, moderate and high quality, respectively. The reviews highlight the scarcity of evidence for health economic parameters, for example, clinical effectiveness, costs, quality of life and the natural history of disease. Health economic evaluations such as cost-effectiveness and budget-impact analyses were scarce, and generally low-to-moderate quality. The causes were limited health economic parameters, together with publications bias, especially for cost-effectiveness analyses.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>The results highlighted issues around a considerable paucity of evidence for economic evaluations and few cost-effectiveness analyses, supporting the notion that a paucity of evidence makes economic evaluations of rare diseases more challenging compared with more prevalent diseases. Furthermore, we provide recommendations for more sustainable approaches in economic evaluations of rare diseases.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1007/s40273-024-01376-w
Junfeng Wang, Xavier Pouwels, Bram Ramaekers, Geert Frederix, Chris van Lieshout, Rudolf Hoogenveen, Xinyu Li, G. Ardine de Wit, Manuela Joore, Hendrik Koffijberg, Anoukh van Giessen, Saskia Knies, Talitha Feenstra
Background
The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs.
Methods
We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings.
Results
In total, 54 respondents contributed to the panel results. The term ‘multi-use disease models’ was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders’ roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2).
Conclusions
MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.
{"title":"A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations","authors":"Junfeng Wang, Xavier Pouwels, Bram Ramaekers, Geert Frederix, Chris van Lieshout, Rudolf Hoogenveen, Xinyu Li, G. Ardine de Wit, Manuela Joore, Hendrik Koffijberg, Anoukh van Giessen, Saskia Knies, Talitha Feenstra","doi":"10.1007/s40273-024-01376-w","DOIUrl":"https://doi.org/10.1007/s40273-024-01376-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>In total, 54 respondents contributed to the panel results. The term ‘multi-use disease models’ was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with <i>comparing alternative policies</i> and <i>supporting resource allocation decisions</i> as the top 2), while 20 issues (with <i>model transparency</i> and <i>stakeholders’ roles</i> as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (<i>regular updates</i> and <i>revalidation after updates</i> were the top 2).</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1007/s40273-024-01374-y
Yanara Marks, Jeffrey S. Hoch, Anna Heath, Petros Pechlivanoglou
Background and Objective
Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators.
Methods
We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes.
Results
Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures.
Conclusions
There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.
背景与目标卫生技术评估 (HTA) 的决策模型大多使用商业软件提交给 HTA 机构,而商业软件具有已知的局限性。有人建议使用开源编程语言 R,因为它高效、透明、可重现,并能考虑复杂的分析。然而,它在 HTA 中的使用仍然有限。本定性研究旨在探讨 R 语言在 HTA 中应用缓慢的主要原因,并找出切实的促进因素。方法我们与来自政府机构、咨询公司、制药公司和学术界的 24 位主要利益相关者进行了两次半结构化焦点小组讨论。两次讨论历时 1.5 小时,对之前研究中发现的障碍进行了反思,并强调了其他障碍。对讨论进行了记录和半誊写,并将数据整理和归纳为关键主题。结果 人力资源限制被认为是一个关键障碍,包括缺乏培训、优先排序和协作,以及对变革的抵制。另一个主要障碍是 HTA 机构对 R 中的提交缺乏认可或明确的指导。与会者还强调,在接受的软件包和决策模型结构方面缺乏沟通,HTA 机构之间也缺乏关于标准决策模型结构的沟通。编制培训材料和举办有针对性的研讨会被认为是一个关键的短期促进因素。加强利益相关者的沟通和参与也可以通过确定需求和机会、鼓励 HTA 机构解决结构性障碍以及提高使用 R 的积极性来促进 R 的使用。
{"title":"Barriers and Facilitators of Using R for Decision Analytic Modeling in Health Technology Assessment: Focus Group Results","authors":"Yanara Marks, Jeffrey S. Hoch, Anna Heath, Petros Pechlivanoglou","doi":"10.1007/s40273-024-01374-y","DOIUrl":"https://doi.org/10.1007/s40273-024-01374-y","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background and Objective</h3><p>Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-07DOI: 10.1007/s40273-024-01372-0
Anna Heath, Gianluca Baio, Ioanna Manolopoulou, Nicky J. Welton
Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.
信息价值(VOI)分析计算的是通过获取更多信息以减少健康经济决策模型中的不确定性而产生的经济价值。VOI 被建议作为研究优先次序和试验设计的工具,因为它可以突出未来研究的经济价值途径。近期方法学上的进步使得在研究实践中使用 VOI 变得越来越可行;然而,VOI 方法与用于设计研究(如临床试验)的标准方法之间存在重大差异。我们的目的是强调基于自愿自主创新的研究设计方法与标准临床试验设计方法之间的主要区别,特别是考虑整个决策背景的重要性。我们举了两个假设的例子来说明,只有在以下情况下,VOI 方法才是准确的:(1) 在设计研究时,将所有可行的比较对象都纳入决策模型;(2) 在收集数据并提出最终治疗建议后,将所有比较对象都保留在决策模型中。使用 VOI 方法时,如果在研究的设计或分析阶段遗漏比较对象,可能会导致不正确的试验设计和/或治疗建议。总之,我们得出的结论是,忽略潜在的参照物而错误地指定健康经济模型会导致误导性的 VOI 结果,并可能浪费稀缺的研究资源。
{"title":"Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators","authors":"Anna Heath, Gianluca Baio, Ioanna Manolopoulou, Nicky J. Welton","doi":"10.1007/s40273-024-01372-0","DOIUrl":"https://doi.org/10.1007/s40273-024-01372-0","url":null,"abstract":"<p>Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-07DOI: 10.1007/s40273-024-01371-1
Anna Kenseth, Dominika Kantorova, Mikyung Kelly Seo, Eline Aas, John Cairns, David Kerr, Hanne Askautrud, Jørn Evert Jacobsen
Objectives
Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemotherapy. We aimed to investigate the cost-effectiveness of a recently developed deep learning-based prognostic method, Histotyping, from the perspective of the Norwegian healthcare system.
Methods
Two partitioned survival models were developed to assess the cost-effectiveness of Histotyping for two treatment cohorts: patients with CRC stage II and III. For each of the two cohorts, Histotyping was used for risk stratification to assign adjuvant chemotherapy and was compared with the standard of care (SOC) (adjuvant chemotherapy to all patients). Health outcomes measured in the model were quality-adjusted life years (QALYs) and life years (LYs) gained. Deterministic and probabilistic sensitivity analyses were performed to determine the impact of uncertainty. Scenario analyses were performed to assess the impact of the parameters with the greatest uncertainty.
Results
Risk-stratifying patients with CRC stage II and III using Histotyping was dominant (less costly and more effective) compared to SOC. In patients with CRC stage II, the net monetary benefit of Histotyping was 270,934 Norwegian kroners (NOK) (year of valuation is 2021), and the net health benefit of Histotyping was 0.99. In stage III, the net monetary benefit of Histotyping was 195,419 NOK, and the net health benefit of Histotyping was 0.71.
Conclusions
Risk-stratifying patients with CRC using Histotyping prior to the administration of adjuvant chemotherapy is likely to be a cost-effective strategy in Norway.
目的在选择治疗方法之前对 II 期和 III 期结直肠癌(CRC)患者进行准确的风险分层,可以将有限的医疗资源有效地分配给可能从辅助化疗中获益的患者。我们的目的是从挪威医疗保健系统的角度出发,调查最近开发的基于深度学习的预后方法--Histotyping的成本效益。方法开发了两个分区生存模型,以评估Histotyping在两个治疗队列(CRC II期和III期患者)中的成本效益。对于这两个组群中的每一个组群,Histotyping均用于风险分层,以分配辅助化疗,并与标准治疗(SOC)(对所有患者进行辅助化疗)进行比较。模型中衡量的健康结果是质量调整生命年(QALYs)和获得的生命年(LYs)。为确定不确定性的影响,进行了确定性和概率敏感性分析。结果与 SOC 相比,使用组织分型法对 II 期和 III 期 CRC 患者进行风险分层具有优势(成本更低、效果更好)。在CRC II期患者中,组织分型的净经济效益为270,934挪威克朗(估值年份为2021年),组织分型的净健康效益为0.99。在III期患者中,组织分型的净经济效益为195,419挪威克朗,组织分型的净健康效益为0.71。
{"title":"Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?","authors":"Anna Kenseth, Dominika Kantorova, Mikyung Kelly Seo, Eline Aas, John Cairns, David Kerr, Hanne Askautrud, Jørn Evert Jacobsen","doi":"10.1007/s40273-024-01371-1","DOIUrl":"https://doi.org/10.1007/s40273-024-01371-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objectives</h3><p>Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemotherapy. We aimed to investigate the cost-effectiveness of a recently developed deep learning-based prognostic method, Histotyping, from the perspective of the Norwegian healthcare system.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Two partitioned survival models were developed to assess the cost-effectiveness of Histotyping for two treatment cohorts: patients with CRC stage II and III. For each of the two cohorts, Histotyping was used for risk stratification to assign adjuvant chemotherapy and was compared with the standard of care (SOC) (adjuvant chemotherapy to all patients). Health outcomes measured in the model were quality-adjusted life years (QALYs) and life years (LYs) gained. Deterministic and probabilistic sensitivity analyses were performed to determine the impact of uncertainty. Scenario analyses were performed to assess the impact of the parameters with the greatest uncertainty.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Risk-stratifying patients with CRC stage II and III using Histotyping was dominant (less costly and more effective) compared to SOC. In patients with CRC stage II, the net monetary benefit of Histotyping was 270,934 Norwegian kroners (NOK) (year of valuation is 2021), and the net health benefit of Histotyping was 0.99. In stage III, the net monetary benefit of Histotyping was 195,419 NOK, and the net health benefit of Histotyping was 0.71.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p> Risk-stratifying patients with CRC using Histotyping prior to the administration of adjuvant chemotherapy is likely to be a cost-effective strategy in Norway.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.1007/s40273-024-01361-3
Abstract
Background
Assessing the cost-effectiveness of interventions targeting childhood excess weight requires estimates of the hazards of transitioning between weight status categories. Current estimates are based on studies characterized by insufficient sample sizes, a lack of national representativeness, and untested assumptions.
Objectives
We sought to (1) estimate transition probabilities and hazard ratios for transitioning between childhood weight status categories, (2) test the validity of the underlying assumption in the literature that transitions between childhood bodyweight categories are time-homogeneous, (3) account for complex sampling procedures when deriving nationally representative transition estimates, and (4) explore the impact of child, maternal, and sociodemographic characteristics.
Methods
We applied a multistate transition modeling approach accounting for complex survey design to UK Millennium Cohort Study (MCS) data to predict transition probabilities and hazard ratios for weight status movements for children aged 3–17. Surveys were conducted at ages 3 (wave 2 in 2004), 5 (wave 3 in 2006), 7 (wave 4 in 2008), 11 (wave 5 in 2012), 14 (wave 6 in 2015), and 17 (wave 7 in 2018) years. We derived datasets that included repeated body mass index measurements across waves after excluding multiple births and children with missing or implausible bodyweight records. To account for the stratified cluster sample design of the MCS, we incorporated survey weights and jackknife replicates of survey weights. Using a validation dataset from the MCS, we tested the validity of our models. Finally, we estimated the relationships between state transitions and child, maternal, and sociodemographic factors.
Results
The datasets for our primary analysis consisted of 10,399 children for waves 2–3, 10,729 for waves 3–4, 9685 for waves 4–5, 8593 for waves 5–6, and 7085 for waves 6–7. All datasets consisted of roughly equal splits of boys and girls. Under the assumption of time-heterogeneous transition rates (our base-case model), younger children (ages 3–5 and 5–7 years) had significantly higher annual transition probabilities of moving from healthy weight to overweight (0.033, 95% confidence interval [CI] 0.026–0.041, and 0.027, 95% CI 0.021–0.033, respectively) compared to older children (0.015, 95% CI 0.012–0.018, at ages 7–11; 0.018, 95% CI 0.013–0.023, at ages 11–14; and 0.018, 95% CI 0.013–0.025 at ages 14–17 years). However, the resolution of unhealthy weight was more strongly age-dependent than transitions from healthy weight to non-healthy weight states. Transition hazards differed by child, maternal, and sociodemographic factors.
Conclusions
Our models generated estimates of bodyweight status transitions in a representative UK childhood population. Compared to our scenario models (i.e., t
{"title":"Childhood Transitions Between Weight Status Categories: Evidence from the UK Millennium Cohort Study","authors":"","doi":"10.1007/s40273-024-01361-3","DOIUrl":"https://doi.org/10.1007/s40273-024-01361-3","url":null,"abstract":"<h3>Abstract</h3> <span> <h3>Background</h3> <p>Assessing the cost-effectiveness of interventions targeting childhood excess weight requires estimates of the hazards of transitioning between weight status categories. Current estimates are based on studies characterized by insufficient sample sizes, a lack of national representativeness, and untested assumptions.</p> </span> <span> <h3>Objectives</h3> <p>We sought to (1) estimate transition probabilities and hazard ratios for transitioning between childhood weight status categories, (2) test the validity of the underlying assumption in the literature that transitions between childhood bodyweight categories are time-homogeneous, (3) account for complex sampling procedures when deriving nationally representative transition estimates, and (4) explore the impact of child, maternal, and sociodemographic characteristics.</p> </span> <span> <h3>Methods</h3> <p>We applied a multistate transition modeling approach accounting for complex survey design to UK Millennium Cohort Study (MCS) data to predict transition probabilities and hazard ratios for weight status movements for children aged 3–17. Surveys were conducted at ages 3 (wave 2 in 2004), 5 (wave 3 in 2006), 7 (wave 4 in 2008), 11 (wave 5 in 2012), 14 (wave 6 in 2015), and 17 (wave 7 in 2018) years. We derived datasets that included repeated body mass index measurements across waves after excluding multiple births and children with missing or implausible bodyweight records. To account for the stratified cluster sample design of the MCS, we incorporated survey weights and jackknife replicates of survey weights. Using a validation dataset from the MCS, we tested the validity of our models. Finally, we estimated the relationships between state transitions and child, maternal, and sociodemographic factors.</p> </span> <span> <h3>Results</h3> <p>The datasets for our primary analysis consisted of 10,399 children for waves 2–3, 10,729 for waves 3–4, 9685 for waves 4–5, 8593 for waves 5–6, and 7085 for waves 6–7. All datasets consisted of roughly equal splits of boys and girls. Under the assumption of time-heterogeneous transition rates (our base-case model), younger children (ages 3–5 and 5–7 years) had significantly higher annual transition probabilities of moving from healthy weight to overweight (0.033, 95% confidence interval [CI] 0.026–0.041, and 0.027, 95% CI 0.021–0.033, respectively) compared to older children (0.015, 95% CI 0.012–0.018, at ages 7–11; 0.018, 95% CI 0.013–0.023, at ages 11–14; and 0.018, 95% CI 0.013–0.025 at ages 14–17 years). However, the resolution of unhealthy weight was more strongly age-dependent than transitions from healthy weight to non-healthy weight states. Transition hazards differed by child, maternal, and sociodemographic factors.</p> </span> <span> <h3>Conclusions</h3> <p>Our models generated estimates of bodyweight status transitions in a representative UK childhood population. Compared to our scenario models (i.e., t","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}