{"title":"在开发和选择基于线性回归的映射算法时，使用临床预测模型的样本量计算框架。","authors":"Yasuhiro Hagiwara","doi":"10.1177/0272989X231188134","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To propose using a framework for calculating the sample size for clinical prediction models when developing and selecting mapping algorithms from a health-related quality-of-life (HRQOL) measure onto the score of a preference-based measure (PBM) using linear regression.</p><p><strong>Methods: </strong>The framework was summarized for health economics researchers. Mapping studies that mapped the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the EQ-5D-3L index using linear regression were evaluated in terms of sample size. The required sample size for each study was calculated using 4 criteria: global shrinkage factor ≥ 0.9, difference between the apparent and adjusted <i>R</i><sup>2</sup> ≤ 0.05, multiplicative margin of error in the estimated residual standard deviation ≤ 1.1, and absolute margin of error in the estimated model intercept ≤ 0.025.</p><p><strong>Results: </strong>Ten mapping studies were identified. The information required to calculate the sample size was successfully extracted from previous mapping studies. Four of 10 mapping studies did not have sufficient sample sizes.</p><p><strong>Limitations: </strong>Further extension of this framework to other regression approaches used in mapping studies is necessary.</p><p><strong>Conclusions: </strong>The sample size should be considered when developing and selecting a mapping algorithm based on linear regression.</p><p><strong>Highlights: </strong>No recommendation or guidance is available for the sample size to develop and select a mapping algorithm from a health-related quality-of-life measure onto the score of a preference-based measure.This research proposes using a framework for calculating the sample size for clinical prediction models in sample size consideration for mapping algorithms using linear regression.A survey showed that the information required to calculate the sample size could be successfully extracted from previous mapping studies and that 4 of 10 mapping studies did not have sufficient sample sizes.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using a Sample Size Calculation Framework for Clinical Prediction Models When Developing and Selecting Mapping Algorithms Based on Linear Regression.\",\"authors\":\"Yasuhiro Hagiwara\",\"doi\":\"10.1177/0272989X231188134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To propose using a framework for calculating the sample size for clinical prediction models when developing and selecting mapping algorithms from a health-related quality-of-life (HRQOL) measure onto the score of a preference-based measure (PBM) using linear regression.</p><p><strong>Methods: </strong>The framework was summarized for health economics researchers. Mapping studies that mapped the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the EQ-5D-3L index using linear regression were evaluated in terms of sample size. The required sample size for each study was calculated using 4 criteria: global shrinkage factor ≥ 0.9, difference between the apparent and adjusted <i>R</i><sup>2</sup> ≤ 0.05, multiplicative margin of error in the estimated residual standard deviation ≤ 1.1, and absolute margin of error in the estimated model intercept ≤ 0.025.</p><p><strong>Results: </strong>Ten mapping studies were identified. The information required to calculate the sample size was successfully extracted from previous mapping studies. Four of 10 mapping studies did not have sufficient sample sizes.</p><p><strong>Limitations: </strong>Further extension of this framework to other regression approaches used in mapping studies is necessary.</p><p><strong>Conclusions: </strong>The sample size should be considered when developing and selecting a mapping algorithm based on linear regression.</p><p><strong>Highlights: </strong>No recommendation or guidance is available for the sample size to develop and select a mapping algorithm from a health-related quality-of-life measure onto the score of a preference-based measure.This research proposes using a framework for calculating the sample size for clinical prediction models in sample size consideration for mapping algorithms using linear regression.A survey showed that the information required to calculate the sample size could be successfully extracted from previous mapping studies and that 4 of 10 mapping studies did not have sufficient sample sizes.</p>\",\"PeriodicalId\":49839,\"journal\":{\"name\":\"Medical Decision Making\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Decision Making\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/0272989X231188134\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/7/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/0272989X231188134","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/7/20 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Using a Sample Size Calculation Framework for Clinical Prediction Models When Developing and Selecting Mapping Algorithms Based on Linear Regression.
Purpose: To propose using a framework for calculating the sample size for clinical prediction models when developing and selecting mapping algorithms from a health-related quality-of-life (HRQOL) measure onto the score of a preference-based measure (PBM) using linear regression.
Methods: The framework was summarized for health economics researchers. Mapping studies that mapped the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the EQ-5D-3L index using linear regression were evaluated in terms of sample size. The required sample size for each study was calculated using 4 criteria: global shrinkage factor ≥ 0.9, difference between the apparent and adjusted R2 ≤ 0.05, multiplicative margin of error in the estimated residual standard deviation ≤ 1.1, and absolute margin of error in the estimated model intercept ≤ 0.025.
Results: Ten mapping studies were identified. The information required to calculate the sample size was successfully extracted from previous mapping studies. Four of 10 mapping studies did not have sufficient sample sizes.
Limitations: Further extension of this framework to other regression approaches used in mapping studies is necessary.
Conclusions: The sample size should be considered when developing and selecting a mapping algorithm based on linear regression.
Highlights: No recommendation or guidance is available for the sample size to develop and select a mapping algorithm from a health-related quality-of-life measure onto the score of a preference-based measure.This research proposes using a framework for calculating the sample size for clinical prediction models in sample size consideration for mapping algorithms using linear regression.A survey showed that the information required to calculate the sample size could be successfully extracted from previous mapping studies and that 4 of 10 mapping studies did not have sufficient sample sizes.
期刊介绍：
Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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