Pub Date : 2024-10-01Epub Date: 2024-09-03DOI: 10.1007/s40273-024-01431-6
Jemimah Ride, Ilias Goranitis, Yan Meng, Christine LaBond, Emily Lancsar
Background: Reporting standards of discrete choice experiments (DCEs) in health have not kept pace with the growth of this method, with multiple reviews calling for better reporting to improve transparency, assessment of validity and translation. A key missing piece has been the absence of a reporting checklist that details minimum standards of what should be reported, as exists for many other methods used in health economics.
Methods: This paper reports the development of a reporting checklist for DCEs in health, which involved a scoping review to identify potential items and a Delphi consensus study among 45 DCE experts internationally to select items and guide the wording and structure of the checklist. The Delphi study included a best-worst scaling study for prioritisation.
Conclusions: The final checklist is presented along with guidance on how to apply it. This checklist can be used by authors to ensure that sufficient detail of a DCE's methods are reported, providing reviewers and readers with the information they need to assess the quality of the study for themselves. Embedding this reporting checklist into standard practice for health DCEs offers an opportunity to improve consistency of reporting standards, thereby enabling transparency of review and facilitating comparison of studies and their translation into policy and practice.
{"title":"A Reporting Checklist for Discrete Choice Experiments in Health: The DIRECT Checklist.","authors":"Jemimah Ride, Ilias Goranitis, Yan Meng, Christine LaBond, Emily Lancsar","doi":"10.1007/s40273-024-01431-6","DOIUrl":"10.1007/s40273-024-01431-6","url":null,"abstract":"<p><strong>Background: </strong>Reporting standards of discrete choice experiments (DCEs) in health have not kept pace with the growth of this method, with multiple reviews calling for better reporting to improve transparency, assessment of validity and translation. A key missing piece has been the absence of a reporting checklist that details minimum standards of what should be reported, as exists for many other methods used in health economics.</p><p><strong>Methods: </strong>This paper reports the development of a reporting checklist for DCEs in health, which involved a scoping review to identify potential items and a Delphi consensus study among 45 DCE experts internationally to select items and guide the wording and structure of the checklist. The Delphi study included a best-worst scaling study for prioritisation.</p><p><strong>Conclusions: </strong>The final checklist is presented along with guidance on how to apply it. This checklist can be used by authors to ensure that sufficient detail of a DCE's methods are reported, providing reviewers and readers with the information they need to assess the quality of the study for themselves. Embedding this reporting checklist into standard practice for health DCEs offers an opportunity to improve consistency of reporting standards, thereby enabling transparency of review and facilitating comparison of studies and their translation into policy and practice.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126334","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 : 2024-10-01Epub Date: 2024-07-05DOI: 10.1007/s40273-024-01406-7
Nicholas R Latimer, Mark J Rutherford
There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.
由于新疗法的开发似乎为某些患者提供了治愈的可能,人们对使用治愈模型为健康技术评估(HTA)提供信息的兴趣与日俱增。然而,治愈模型通常不包括在提交给 HTA 机构的证据档案中,也很少被用来作为决策依据。这可能是由于人们对治愈模型的工作原理、假设条件以及可靠性缺乏了解。在本教程中,我们将解释为何以及何时固化模型可能对 HTA 有用,描述混合和非混合固化模型的主要特征,并提供 Stata 代码演示其在各种情况下的使用。我们强调了分析人员在拟合这些模型时以及评审人员和决策者在解释其预测时必须考虑的关键问题。我们特别指出,灵活的参数非混杂治愈模型尚未用于 HTA,但它们的优势使其非常适合于治愈假设有效但随访有限的 HTA 情况。
{"title":"Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know.","authors":"Nicholas R Latimer, Mark J Rutherford","doi":"10.1007/s40273-024-01406-7","DOIUrl":"10.1007/s40273-024-01406-7","url":null,"abstract":"<p><p>There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535058","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 : 2024-10-01Epub Date: 2024-07-31DOI: 10.1007/s40273-024-01416-5
Pamela Gongora-Salazar, Rafael Perera, Oliver Rivero-Arias, Apostolos Tsiachristas
Background: Health systems are moving towards value-based care, implementing new care models that allegedly aim beyond patient outcomes. Therefore, a policy and academic debate is underway regarding the definition of value in healthcare, the inclusion of costs in value metrics, and the importance of each value element. This study aimed to define healthcare value elements and assess their relative importance (RI) to the public in England.
Method: Using data from 26 semi-structured interviews and a literature review, and applying decision-theory axioms, we selected a comprehensive and applicable set of value-based elements. Their RI was determined using two discrete choice experiments (DCEs) based on Bayesian D-efficient DCE designs, with one DCE incorporating healthcare costs expressed as income tax rise. Respondent preferences were analysed using mixed logit models.
Results: Six value elements were identified: additional life-years, health-related quality of life, patient experience, target population size, equity, and cost. The DCE surveys were completed by 402 participants. All utility coefficients had the expected signs and were statistically significant (p < 0.05). Additional life-years (25.3%; 95% confidence interval [CI] 22.5-28.6%) and patient experience (25.2%; 95% CI 21.6-28.9%) received the highest RI, followed by target population size (22.4%; 95% CI 19.1-25.6%) and quality of life (17.6%; 95% CI 15.0-20.3%). Equity had the lowest RI (9.6%; 95% CI 6.4-12.1%), decreasing by 8.8 percentage points with cost inclusion. A similar reduction was observed in the RI of quality of life when cost was included.
Conclusion: The public prioritizes value elements not captured by conventional metrics, such as quality-adjusted life-years. Although cost inclusion did not alter the preference ranking, its inclusion in the value metric warrants careful consideration.
{"title":"Unravelling Elements of Value of Healthcare and Assessing their Importance Using Evidence from Two Discrete-Choice Experiments in England.","authors":"Pamela Gongora-Salazar, Rafael Perera, Oliver Rivero-Arias, Apostolos Tsiachristas","doi":"10.1007/s40273-024-01416-5","DOIUrl":"10.1007/s40273-024-01416-5","url":null,"abstract":"<p><strong>Background: </strong>Health systems are moving towards value-based care, implementing new care models that allegedly aim beyond patient outcomes. Therefore, a policy and academic debate is underway regarding the definition of value in healthcare, the inclusion of costs in value metrics, and the importance of each value element. This study aimed to define healthcare value elements and assess their relative importance (RI) to the public in England.</p><p><strong>Method: </strong>Using data from 26 semi-structured interviews and a literature review, and applying decision-theory axioms, we selected a comprehensive and applicable set of value-based elements. Their RI was determined using two discrete choice experiments (DCEs) based on Bayesian D-efficient DCE designs, with one DCE incorporating healthcare costs expressed as income tax rise. Respondent preferences were analysed using mixed logit models.</p><p><strong>Results: </strong>Six value elements were identified: additional life-years, health-related quality of life, patient experience, target population size, equity, and cost. The DCE surveys were completed by 402 participants. All utility coefficients had the expected signs and were statistically significant (p < 0.05). Additional life-years (25.3%; 95% confidence interval [CI] 22.5-28.6%) and patient experience (25.2%; 95% CI 21.6-28.9%) received the highest RI, followed by target population size (22.4%; 95% CI 19.1-25.6%) and quality of life (17.6%; 95% CI 15.0-20.3%). Equity had the lowest RI (9.6%; 95% CI 6.4-12.1%), decreasing by 8.8 percentage points with cost inclusion. A similar reduction was observed in the RI of quality of life when cost was included.</p><p><strong>Conclusion: </strong>The public prioritizes value elements not captured by conventional metrics, such as quality-adjusted life-years. Although cost inclusion did not alter the preference ranking, its inclusion in the value metric warrants careful consideration.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141860527","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 : 2024-09-26DOI: 10.1007/s40273-024-01438-z
Maddalena Centanni, Janine Nijhuis, Mats O Karlsson, Lena E Friberg
Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).
Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.
Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).
Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.
{"title":"Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy.","authors":"Maddalena Centanni, Janine Nijhuis, Mats O Karlsson, Lena E Friberg","doi":"10.1007/s40273-024-01438-z","DOIUrl":"https://doi.org/10.1007/s40273-024-01438-z","url":null,"abstract":"<p><strong>Background: </strong>Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).</p><p><strong>Methods: </strong>A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.</p><p><strong>Results: </strong>The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).</p><p><strong>Conclusions: </strong>Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142351615","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 : 2024-09-26DOI: 10.1007/s40273-024-01432-5
Mihir Gandhi, Ravindran Kanesvaran, Mohamad Farid Bin Harunal Rashid, Dawn Qingqing Chong, Wen-Yee Chay, Rachel Lee-Yin Tan, Richard Norman, Madeleine T King, Nan Luo
Objectives: The aim of the study was to develop and compare utility value sets for the EORTC QLU-C10D, a cancer-specific utility instrument based on the EORTC QLQ-C30, using the preferences of the general public and cancer patients in Singapore, and to assess their measurement properties.
Methods: A total of 600 individuals from the general public were recruited using a multi-stage random sampling, along with 626 cancer patients with clinically confirmed diagnoses from outpatient clinics of the largest tertiary cancer hospital. Each participant valued 16 pairs of EORTC QLU-C10D health states using a discrete choice experiment (DCE). Conditional logit models were used to analyze the DCE responses of the general public and cancer patients separately. Utility values were assessed for known-group validity and responsiveness in the cancer patients by comparing mean values across subgroups of patients and calculating standardized response means using longitudinal EORTC QLQ-C30 data, respectively.
Results: Physical functioning and pain had the most impact on utility for both cancer patients and general public groups. Worst health state utility values were -0.821 and -0.463 for the general public and cancer patients, respectively. Cancer patients' values were lower for mild-to-moderate health states but higher for moderately-to-highly impaired states compared with the general public's values. Both value sets discriminated between patients with differing characteristics and responded equally well to improved health status, but the cancer patients' value set was slightly more responsive to deteriorated health.
Conclusions: EORTC QLU-C10D value sets based on the preferences of the Singaporean general public and cancer patients exhibited differences in values but similar psychometric properties.
{"title":"Valuation of the EORTC Quality of Life Utility Core 10 Dimensions (QLU-C10D) in a Multi-ethnic Asian Setting: How Does Having Cancer Matter?","authors":"Mihir Gandhi, Ravindran Kanesvaran, Mohamad Farid Bin Harunal Rashid, Dawn Qingqing Chong, Wen-Yee Chay, Rachel Lee-Yin Tan, Richard Norman, Madeleine T King, Nan Luo","doi":"10.1007/s40273-024-01432-5","DOIUrl":"https://doi.org/10.1007/s40273-024-01432-5","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of the study was to develop and compare utility value sets for the EORTC QLU-C10D, a cancer-specific utility instrument based on the EORTC QLQ-C30, using the preferences of the general public and cancer patients in Singapore, and to assess their measurement properties.</p><p><strong>Methods: </strong>A total of 600 individuals from the general public were recruited using a multi-stage random sampling, along with 626 cancer patients with clinically confirmed diagnoses from outpatient clinics of the largest tertiary cancer hospital. Each participant valued 16 pairs of EORTC QLU-C10D health states using a discrete choice experiment (DCE). Conditional logit models were used to analyze the DCE responses of the general public and cancer patients separately. Utility values were assessed for known-group validity and responsiveness in the cancer patients by comparing mean values across subgroups of patients and calculating standardized response means using longitudinal EORTC QLQ-C30 data, respectively.</p><p><strong>Results: </strong>Physical functioning and pain had the most impact on utility for both cancer patients and general public groups. Worst health state utility values were -0.821 and -0.463 for the general public and cancer patients, respectively. Cancer patients' values were lower for mild-to-moderate health states but higher for moderately-to-highly impaired states compared with the general public's values. Both value sets discriminated between patients with differing characteristics and responded equally well to improved health status, but the cancer patients' value set was slightly more responsive to deteriorated health.</p><p><strong>Conclusions: </strong>EORTC QLU-C10D value sets based on the preferences of the Singaporean general public and cancer patients exhibited differences in values but similar psychometric properties.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142351616","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 : 2024-09-20DOI: 10.1007/s40273-024-01429-0
Nicholas R Latimer, Kurt Taylor, Anthony J Hatswell, Sophia Ho, Gabriel Okorogheye, Clara Chen, Inkyu Kim, John Borrill, David Bertwistle
Background and objective: Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation.
Methods: We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut.
Results: The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria.
Conclusions: The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals.
{"title":"An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?","authors":"Nicholas R Latimer, Kurt Taylor, Anthony J Hatswell, Sophia Ho, Gabriel Okorogheye, Clara Chen, Inkyu Kim, John Borrill, David Bertwistle","doi":"10.1007/s40273-024-01429-0","DOIUrl":"https://doi.org/10.1007/s40273-024-01429-0","url":null,"abstract":"<p><strong>Background and objective: </strong>Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation.</p><p><strong>Methods: </strong>We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut.</p><p><strong>Results: </strong>The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria.</p><p><strong>Conclusions: </strong>The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142292932","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 : 2024-09-09DOI: 10.1007/s40273-024-01418-3
Yankier Pijeira Perez, Dyfrig A Hughes
Background: The National Institute for Health and Care Excellence (NICE) may approve health technologies on condition of more evidence generated only in research (OiR) or only with research (OwR). NICE specifies the information needed to comply with its request, although it may not necessarily guarantee good quality and timely evidence for re-appraisal, before reaching a final decision.
Aim: This study aimed to critically appraise the methods, quality and risk of bias of evidence generated in response to NICE OiR and OwR technology appraisal (TA) and highly specialised technologies (HSTs) recommendations.
Methods: NICE TAs (between March 2000 and September 2020) and HST evaluations (to October 2023) of medicines were reviewed. Conditional recommendations were analysed to identify the evidence requested by NICE for re-appraisal. The new evidence was analysed for compliance with NICE's request and assessed using the Cochrane Collaboration's tools for risk of bias in randomised trials and the ROBINS-I tool for non-randomised evidence.
Results: NICE made 54 conditional recommendations from TAs (13 OiR and 41 OwR) and five conditional recommendations for HSTs (all OwR). Of these, 16 TAs presented additional evidence for re-appraisal (9 OiR [69%] and 7 OwR [17%]) and three HSTs (3 OwR [60%]). Two of the nine re-appraised TAs with OiR recommendation and four of the seven OwR complied fully with NICE's request for further evidence, while all three from the HSTs complied. The majority of re-appraised TAs and HSTs included evidence that was deemed to be at serious, high, moderate or unclear risk of bias. Among the 26 randomised controlled trials from TAs assessed, eight were categorised as having low risk of bias in all domains and ten had at least one domain as a high risk of bias. Reporting was unclear for the remainder. Twenty-two non-randomised studies, primarily single-arm studies, were susceptible to biases mostly due to the selection of participants and to confounding. Two HSTs provided evidence from randomised controlled trials which were classified as unclear or high risk of bias. All non-randomised evidence from HSTs were categorised as moderate or serious risk of bias.
Conclusions: There is widespread non-compliance with agreed data requests and important variation in the quality of evidence submitted in response to NICE conditional approval recommendations. Quality standards ought to be stipulated in respect to evidence contributing to re-appraisals following NICE conditional approval recommendations.
{"title":"Evidence Following Conditional NICE Technology Appraisal Recommendations: A Critical Analysis of Methods, Quality and Risk of Bias.","authors":"Yankier Pijeira Perez, Dyfrig A Hughes","doi":"10.1007/s40273-024-01418-3","DOIUrl":"https://doi.org/10.1007/s40273-024-01418-3","url":null,"abstract":"<p><strong>Background: </strong>The National Institute for Health and Care Excellence (NICE) may approve health technologies on condition of more evidence generated only in research (OiR) or only with research (OwR). NICE specifies the information needed to comply with its request, although it may not necessarily guarantee good quality and timely evidence for re-appraisal, before reaching a final decision.</p><p><strong>Aim: </strong>This study aimed to critically appraise the methods, quality and risk of bias of evidence generated in response to NICE OiR and OwR technology appraisal (TA) and highly specialised technologies (HSTs) recommendations.</p><p><strong>Methods: </strong>NICE TAs (between March 2000 and September 2020) and HST evaluations (to October 2023) of medicines were reviewed. Conditional recommendations were analysed to identify the evidence requested by NICE for re-appraisal. The new evidence was analysed for compliance with NICE's request and assessed using the Cochrane Collaboration's tools for risk of bias in randomised trials and the ROBINS-I tool for non-randomised evidence.</p><p><strong>Results: </strong>NICE made 54 conditional recommendations from TAs (13 OiR and 41 OwR) and five conditional recommendations for HSTs (all OwR). Of these, 16 TAs presented additional evidence for re-appraisal (9 OiR [69%] and 7 OwR [17%]) and three HSTs (3 OwR [60%]). Two of the nine re-appraised TAs with OiR recommendation and four of the seven OwR complied fully with NICE's request for further evidence, while all three from the HSTs complied. The majority of re-appraised TAs and HSTs included evidence that was deemed to be at serious, high, moderate or unclear risk of bias. Among the 26 randomised controlled trials from TAs assessed, eight were categorised as having low risk of bias in all domains and ten had at least one domain as a high risk of bias. Reporting was unclear for the remainder. Twenty-two non-randomised studies, primarily single-arm studies, were susceptible to biases mostly due to the selection of participants and to confounding. Two HSTs provided evidence from randomised controlled trials which were classified as unclear or high risk of bias. All non-randomised evidence from HSTs were categorised as moderate or serious risk of bias.</p><p><strong>Conclusions: </strong>There is widespread non-compliance with agreed data requests and important variation in the quality of evidence submitted in response to NICE conditional approval recommendations. Quality standards ought to be stipulated in respect to evidence contributing to re-appraisals following NICE conditional approval recommendations.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154772","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 : 2024-09-07DOI: 10.1007/s40273-024-01430-7
Amy Gye, Richard De Abreu Lourenco, Stephen Goodall
Objective: Chimeric antigen-receptor T-cell therapy (CAR-T) is characterised by early phase data at the time of registration, high upfront cost and a complex manufacturing and administration process compared with standard therapies. Our objective was to compare the performance of different models to assess the cost effectiveness of CAR-T using a state-transition model (STM), partitioned survival model (PSM) and discrete event simulation (DES).
Methods: Individual data for tisagenlecleucel for the treatment of young patients with acute lymphoblastic leukaemia (ALL) were used to populate the models. Costs and benefits were measured over a lifetime to generate a cost per quality-adjusted life-year (QALY). Model performance was compared quantitatively on the outcomes generated and a checklist developed summarising the components captured by each model type relevant to assessing cost effectiveness of CAR-T.
Results: Models generated similar results with base-case analyses ranging from an incremental cost per QALY of $96,074-$99,625. DES was the only model to specifically capture CAR-T wait time, demonstrating a substantial loss of benefit of CAR-T with increased wait time.
Conclusion: Although model type did not meaningfully impact base-case results, the ability to incorporate an outcome-based payment arrangement (OBA) and wait time are important elements to consider when selecting a model for CAR-T. DES provided greater flexibility compared with STM and PSM approaches to deal with the complex manufacturing and administration process that can lead to extended wait times and substantially reduce the benefit of CAR-T. This is an important consideration when selecting a model type for CAR-T, so major drivers of uncertainty are considered in funding decisions.
{"title":"Different Models, Same Results: Considerations When Choosing Between Approaches to Model Cost Effectiveness of Chimeric-Antigen Receptor T-Cell Therapy Versus Standard of Care.","authors":"Amy Gye, Richard De Abreu Lourenco, Stephen Goodall","doi":"10.1007/s40273-024-01430-7","DOIUrl":"https://doi.org/10.1007/s40273-024-01430-7","url":null,"abstract":"<p><strong>Objective: </strong>Chimeric antigen-receptor T-cell therapy (CAR-T) is characterised by early phase data at the time of registration, high upfront cost and a complex manufacturing and administration process compared with standard therapies. Our objective was to compare the performance of different models to assess the cost effectiveness of CAR-T using a state-transition model (STM), partitioned survival model (PSM) and discrete event simulation (DES).</p><p><strong>Methods: </strong>Individual data for tisagenlecleucel for the treatment of young patients with acute lymphoblastic leukaemia (ALL) were used to populate the models. Costs and benefits were measured over a lifetime to generate a cost per quality-adjusted life-year (QALY). Model performance was compared quantitatively on the outcomes generated and a checklist developed summarising the components captured by each model type relevant to assessing cost effectiveness of CAR-T.</p><p><strong>Results: </strong>Models generated similar results with base-case analyses ranging from an incremental cost per QALY of $96,074-$99,625. DES was the only model to specifically capture CAR-T wait time, demonstrating a substantial loss of benefit of CAR-T with increased wait time.</p><p><strong>Conclusion: </strong>Although model type did not meaningfully impact base-case results, the ability to incorporate an outcome-based payment arrangement (OBA) and wait time are important elements to consider when selecting a model for CAR-T. DES provided greater flexibility compared with STM and PSM approaches to deal with the complex manufacturing and administration process that can lead to extended wait times and substantially reduce the benefit of CAR-T. This is an important consideration when selecting a model type for CAR-T, so major drivers of uncertainty are considered in funding decisions.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146027","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 : 2024-09-02DOI: 10.1007/s40273-024-01424-5
Meiyu Wu, Jing Ma, Sini Li, Shuxia Qin, Chongqing Tan, Ouyang Xie, Andong Li, Aaron G Lim, Xiaomin Wan
Background and objective: China has the highest number of hepatitis C virus (HCV) infections in the world. However, it is unclear what levels of screening and treatment are needed to achieve the WHO 2030 hepatitis C elimination targets. We aimed to evaluate the impact of scaling up interventions on the hepatitis C epidemic and determine how and at what cost these elimination targets could be achieved for the whole population in China.
Methods: We developed a compartmental model incorporating HCV transmission, disease progression, and care cascade for the whole population in China, calibrated with data on demographics, injecting drug use, HCV prevalence, and treatments. Five different scenarios were evaluated for effects and costs for 2022-2030. All costs were converted to 2021 US dollar (USD) and discounted at an annual rate of 5%. One-way sensitivity analyses were conducted to assess the robustness of the model.
Results: Under the status quo scenario, the incidence of hepatitis C is projected to increase from 60.39 (57.60-63.45) per 100,000 person-years in 2022 to 68.72 (65.3-73.97) per 100,000 person-years in 2030, and 2.52 million (1.94-3.07 million) infected patients are projected to die between 2022 and 2030, of which 0.76 (0.61-1.08) million will die due to hepatitis C. By increasing primary screening to 10%, conducting regular rescreening (annually for PWID and every 5 years for the general population) and treating 90% of patients diagnosed, the incidence would be reduced by 88.15% (86.61-89.45%) and hepatitis C-related mortality by 60.5% (52.62-65.54%) by 2030, compared with 2015 levels. This strategy would cost USD 52.78 (USD 43.93-58.53) billion.
Conclusions: Without changes in HCV prevention and control policy, the disease burden of HCV in China will increase dramatically. To achieve the hepatitis C elimination targets, China needs to sufficiently scale up screening and treatment.
{"title":"Effects and Costs of Hepatitis C Virus Elimination for the Whole Population in China: A Modelling Study.","authors":"Meiyu Wu, Jing Ma, Sini Li, Shuxia Qin, Chongqing Tan, Ouyang Xie, Andong Li, Aaron G Lim, Xiaomin Wan","doi":"10.1007/s40273-024-01424-5","DOIUrl":"https://doi.org/10.1007/s40273-024-01424-5","url":null,"abstract":"<p><strong>Background and objective: </strong>China has the highest number of hepatitis C virus (HCV) infections in the world. However, it is unclear what levels of screening and treatment are needed to achieve the WHO 2030 hepatitis C elimination targets. We aimed to evaluate the impact of scaling up interventions on the hepatitis C epidemic and determine how and at what cost these elimination targets could be achieved for the whole population in China.</p><p><strong>Methods: </strong>We developed a compartmental model incorporating HCV transmission, disease progression, and care cascade for the whole population in China, calibrated with data on demographics, injecting drug use, HCV prevalence, and treatments. Five different scenarios were evaluated for effects and costs for 2022-2030. All costs were converted to 2021 US dollar (USD) and discounted at an annual rate of 5%. One-way sensitivity analyses were conducted to assess the robustness of the model.</p><p><strong>Results: </strong>Under the status quo scenario, the incidence of hepatitis C is projected to increase from 60.39 (57.60-63.45) per 100,000 person-years in 2022 to 68.72 (65.3-73.97) per 100,000 person-years in 2030, and 2.52 million (1.94-3.07 million) infected patients are projected to die between 2022 and 2030, of which 0.76 (0.61-1.08) million will die due to hepatitis C. By increasing primary screening to 10%, conducting regular rescreening (annually for PWID and every 5 years for the general population) and treating 90% of patients diagnosed, the incidence would be reduced by 88.15% (86.61-89.45%) and hepatitis C-related mortality by 60.5% (52.62-65.54%) by 2030, compared with 2015 levels. This strategy would cost USD 52.78 (USD 43.93-58.53) billion.</p><p><strong>Conclusions: </strong>Without changes in HCV prevention and control policy, the disease burden of HCV in China will increase dramatically. To achieve the hepatitis C elimination targets, China needs to sufficiently scale up screening and treatment.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142110705","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 : 2024-09-01Epub Date: 2024-07-02DOI: 10.1007/s40273-024-01409-4
Andrew J Sutton, Daniel S Lupu, Stephen P Bergin, Thomas L Holland, Staci A McAdams, Sanjeet S Dadwal, Khoi Nguyen, Frederick S Nolte, Gabriel Tremblay, Bradley A Perkins
Introduction: Immunocompromised host pneumonia (ICHP) is an important cause of morbidity and mortality, yet usual care (UC) diagnostic tests often fail to identify an infectious etiology. A US-based, multicenter study (PICKUP) among ICHP patients with hematological malignancies, including hematological cell transplant recipients, showed that plasma microbial cell-free DNA (mcfDNA) sequencing provided significant additive diagnostic value.
Aim: The objective of this study was to perform a cost-effectiveness analysis (CEA) of adding mcfDNA sequencing to UC diagnostic testing for hospitalized ICHP patients.
Methods: A semi-Markov model was utilized from the US third-party payer's perspective such that only direct costs were included, using a lifetime time horizon with discount rates of 3% for costs and benefits. Three comparators were considered: (1) All UC, which included non-invasive (NI) and invasive testing and early bronchoscopy; (2) All UC & mcfDNA; and (3) NI UC & mcfDNA & conditional UC Bronch (later bronchoscopy if the initial tests are negative). The model considered whether a probable causative infectious etiology was identified and if the patient received appropriate antimicrobial treatment through expert adjudication, and if the patient died in-hospital. The primary endpoints were total costs, life-years (LYs), equal value life-years (evLYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio per QALY. Extensive scenario and probabilistic sensitivity analyses (PSA) were conducted.
Results: At a price of $2000 (2023 USD) for the plasma mcfDNA, All UC & mcfDNA was more costly ($165,247 vs $153,642) but more effective (13.39 vs 12.47 LYs gained; 10.20 vs 9.42 evLYs gained; 10.11 vs 9.42 QALYs gained) compared to All UC alone, giving a cost/QALY of $16,761. NI UC & mcfDNA & conditional UC Bronch was also more costly ($162,655 vs $153,642) and more effective (13.19 vs 12.47 LYs gained; 9.96 vs 9.42 evLYs gained; 9.96 vs 9.42 QALYs gained) compared to All UC alone, with a cost/QALY of $16,729. The PSA showed that above a willingness-to-pay threshold of $50,000/QALY, All UC & mcfDNA was the preferred scenario on cost-effectiveness grounds (as it provides the most QALYs gained). Further scenario analyses found that All UC & mcfDNA always improved patient outcomes but was not cost saving, even when the price of mcfDNA was set to $0.
Conclusions: Based on the evidence available at the time of this analysis, this CEA suggests that mcfDNA may be cost-effective when added to All UC, as well as in a scenario using conditional bronchoscopy when NI testing fails to identify a probable infectious etiology for ICHP. Adding mcfDNA testing to UC diagnostic testing should allow more patients to receive appropriate therapy earlier and improve patient outcomes.
导言:免疫受损宿主肺炎(ICHP)是导致发病和死亡的重要原因,但常规护理(UC)诊断测试往往无法确定感染性病因。一项针对血液恶性肿瘤 ICHP 患者(包括血细胞移植受者)的美国多中心研究(PICKUP)显示,血浆微生物无细胞 DNA(mcfDNA)测序具有显著的附加诊断价值。目的:本研究的目的是对住院 ICHP 患者的 UC 诊断测试中增加 mcfDNA 测序进行成本效益分析(CEA):从美国第三方支付机构的角度出发,采用半马尔可夫模型,只包括直接成本,使用终生时间跨度,成本和收益的贴现率均为 3%。该模型考虑了三个比较对象:(1)所有 UC,包括非侵入性(NI)和侵入性检测以及早期支气管镜检查;(2)所有 UC 和 mcfDNA;以及(3)NI UC 和 mcfDNA 以及有条件的 UC Bronch(如果初始检测结果为阴性,则随后进行支气管镜检查)。该模型考虑了是否确定了可能的致病感染病因,患者是否通过专家裁定接受了适当的抗菌治疗,以及患者是否在院内死亡。主要终点是总成本、生命年(LYs)、等值生命年(evLYs)、质量调整生命年(QALYs)和每 QALY 的增量成本效益比。进行了广泛的情景分析和概率敏感性分析(PSA):血浆 mcfDNA 的价格为 2000 美元(2023 年),与单独使用 All UC 相比,All UC & mcfDNA 的成本更高(165247 美元 vs 153642 美元),但效果更好(13.39 LYs gained vs 12.47 LYs;10.20 evLYs gained vs 9.42 evLYs;10.11 QALYs gained vs 9.42 QALYs),成本/QALY 为 16761 美元。NI UC、mcfDNA 和有条件 UC 支气管治疗的成本(162,655 美元 vs 153,642 美元)和疗效(13.19 LYs vs 12.47 LYs gained;9.96 vs 9.42 evLYs gained;9.96 vs 9.42 QALYs gained)也高于单独治疗所有 UC,成本/QALY 为 16,729 美元。PSA 显示,在 50,000 美元/QALY 的支付意愿阈值之上,从成本效益的角度来看,All UC & mcfDNA 是首选方案(因为它能提供最多的 QALYs 收益)。进一步的方案分析发现,即使 mcfDNA 的价格设定为 0.00 美元,All UC & mcfDNA 始终能改善患者的治疗效果,但并不能节约成本:根据本次分析时可用的证据,本 CEA 表明,如果将 mcfDNA 添加到 All UC 中,以及在 NI 检测未能确定 ICHP 的可能感染病因时使用条件支气管镜检查,则 mcfDNA 可能具有成本效益。在 UC 诊断检测中加入 mcfDNA 检测,应能让更多患者更早地接受适当的治疗,并改善患者的预后。
{"title":"Cost-Effectiveness of Plasma Microbial Cell-Free DNA Sequencing When Added to Usual Care Diagnostic Testing for Immunocompromised Host Pneumonia.","authors":"Andrew J Sutton, Daniel S Lupu, Stephen P Bergin, Thomas L Holland, Staci A McAdams, Sanjeet S Dadwal, Khoi Nguyen, Frederick S Nolte, Gabriel Tremblay, Bradley A Perkins","doi":"10.1007/s40273-024-01409-4","DOIUrl":"10.1007/s40273-024-01409-4","url":null,"abstract":"<p><strong>Introduction: </strong>Immunocompromised host pneumonia (ICHP) is an important cause of morbidity and mortality, yet usual care (UC) diagnostic tests often fail to identify an infectious etiology. A US-based, multicenter study (PICKUP) among ICHP patients with hematological malignancies, including hematological cell transplant recipients, showed that plasma microbial cell-free DNA (mcfDNA) sequencing provided significant additive diagnostic value.</p><p><strong>Aim: </strong>The objective of this study was to perform a cost-effectiveness analysis (CEA) of adding mcfDNA sequencing to UC diagnostic testing for hospitalized ICHP patients.</p><p><strong>Methods: </strong>A semi-Markov model was utilized from the US third-party payer's perspective such that only direct costs were included, using a lifetime time horizon with discount rates of 3% for costs and benefits. Three comparators were considered: (1) All UC, which included non-invasive (NI) and invasive testing and early bronchoscopy; (2) All UC & mcfDNA; and (3) NI UC & mcfDNA & conditional UC Bronch (later bronchoscopy if the initial tests are negative). The model considered whether a probable causative infectious etiology was identified and if the patient received appropriate antimicrobial treatment through expert adjudication, and if the patient died in-hospital. The primary endpoints were total costs, life-years (LYs), equal value life-years (evLYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio per QALY. Extensive scenario and probabilistic sensitivity analyses (PSA) were conducted.</p><p><strong>Results: </strong>At a price of $2000 (2023 USD) for the plasma mcfDNA, All UC & mcfDNA was more costly ($165,247 vs $153,642) but more effective (13.39 vs 12.47 LYs gained; 10.20 vs 9.42 evLYs gained; 10.11 vs 9.42 QALYs gained) compared to All UC alone, giving a cost/QALY of $16,761. NI UC & mcfDNA & conditional UC Bronch was also more costly ($162,655 vs $153,642) and more effective (13.19 vs 12.47 LYs gained; 9.96 vs 9.42 evLYs gained; 9.96 vs 9.42 QALYs gained) compared to All UC alone, with a cost/QALY of $16,729. The PSA showed that above a willingness-to-pay threshold of $50,000/QALY, All UC & mcfDNA was the preferred scenario on cost-effectiveness grounds (as it provides the most QALYs gained). Further scenario analyses found that All UC & mcfDNA always improved patient outcomes but was not cost saving, even when the price of mcfDNA was set to $0.</p><p><strong>Conclusions: </strong>Based on the evidence available at the time of this analysis, this CEA suggests that mcfDNA may be cost-effective when added to All UC, as well as in a scenario using conditional bronchoscopy when NI testing fails to identify a probable infectious etiology for ICHP. Adding mcfDNA testing to UC diagnostic testing should allow more patients to receive appropriate therapy earlier and improve patient outcomes.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141492977","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}