{"title":"评价：使用后验混合模型量化系统评价证据不确定性的软件","authors":"Conrad Kabali","doi":"10.1111/jep.70272","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Rationale</h3>\n \n <p>Systematic reviews are essential for evidence-based healthcare decision-making. While it is relatively straightforward to quantitatively assess random errors in systematic reviews, as these are typically reported in primary studies, the assessment of biases often remains narrative. Primary studies seldom provide quantitative estimates of biases and their uncertainties, resulting in systematic reviews rarely including such measurements. Additionally, evidence appraisers often face time constraints and technical challenges that prevent them from conducting quantitative bias assessments themselves. Given that multiple biases and random errors collectively skew the point estimate from the truth, it is important to incorporate comprehensive quantitative methods of uncertainty in systematic reviews. These methods should integrate random errors and biases into a unified measure of uncertainty and be easily accessible to evidence appraisers, preferably through user-friendly software.</p>\n </section>\n \n <section>\n \n <h3> Aims and Objectives</h3>\n \n <p>To address this need, we propose a posterior mixture model and introduce <span>AppRaise</span>, a free, web-based interactive software designed to implement this approach.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We showcase its application through a health technology assessment (HTA) report on the effectiveness of continuous glucose monitoring in reducing A1c levels among individuals with type 1 diabetes.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Applying the <span>AppRaise</span> software to the HTA report revealed a high level of certainty (86% probability) that continuous glucose monitoring would, on average, result in a reduction in A1c levels compared with self-monitoring of blood glucose among Ontarians with type 1 diabetes. These findings were similar to other quantitative bias-adjusted approaches in systematic reviews.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p><span>AppRaise</span> can be utilized as a standalone tool or as a complement to validate the quality of evidence assessed using qualitative-based scoring methods. This approach is also useful for assessing the sensitivity of parameter estimates to potential biases introduced by primary studies.</p>\n </section>\n </div>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":"31 6","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AppRaise: Software for Quantifying Evidence Uncertainty in Systematic Reviews Using a Posterior Mixture Model\",\"authors\":\"Conrad Kabali\",\"doi\":\"10.1111/jep.70272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Rationale</h3>\\n \\n <p>Systematic reviews are essential for evidence-based healthcare decision-making. While it is relatively straightforward to quantitatively assess random errors in systematic reviews, as these are typically reported in primary studies, the assessment of biases often remains narrative. Primary studies seldom provide quantitative estimates of biases and their uncertainties, resulting in systematic reviews rarely including such measurements. Additionally, evidence appraisers often face time constraints and technical challenges that prevent them from conducting quantitative bias assessments themselves. Given that multiple biases and random errors collectively skew the point estimate from the truth, it is important to incorporate comprehensive quantitative methods of uncertainty in systematic reviews. These methods should integrate random errors and biases into a unified measure of uncertainty and be easily accessible to evidence appraisers, preferably through user-friendly software.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Aims and Objectives</h3>\\n \\n <p>To address this need, we propose a posterior mixture model and introduce <span>AppRaise</span>, a free, web-based interactive software designed to implement this approach.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We showcase its application through a health technology assessment (HTA) report on the effectiveness of continuous glucose monitoring in reducing A1c levels among individuals with type 1 diabetes.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Applying the <span>AppRaise</span> software to the HTA report revealed a high level of certainty (86% probability) that continuous glucose monitoring would, on average, result in a reduction in A1c levels compared with self-monitoring of blood glucose among Ontarians with type 1 diabetes. These findings were similar to other quantitative bias-adjusted approaches in systematic reviews.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p><span>AppRaise</span> can be utilized as a standalone tool or as a complement to validate the quality of evidence assessed using qualitative-based scoring methods. This approach is also useful for assessing the sensitivity of parameter estimates to potential biases introduced by primary studies.</p>\\n </section>\\n </div>\",\"PeriodicalId\":15997,\"journal\":{\"name\":\"Journal of evaluation in clinical practice\",\"volume\":\"31 6\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of evaluation in clinical practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jep.70272\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of evaluation in clinical practice","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jep.70272","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
AppRaise: Software for Quantifying Evidence Uncertainty in Systematic Reviews Using a Posterior Mixture Model
Rationale
Systematic reviews are essential for evidence-based healthcare decision-making. While it is relatively straightforward to quantitatively assess random errors in systematic reviews, as these are typically reported in primary studies, the assessment of biases often remains narrative. Primary studies seldom provide quantitative estimates of biases and their uncertainties, resulting in systematic reviews rarely including such measurements. Additionally, evidence appraisers often face time constraints and technical challenges that prevent them from conducting quantitative bias assessments themselves. Given that multiple biases and random errors collectively skew the point estimate from the truth, it is important to incorporate comprehensive quantitative methods of uncertainty in systematic reviews. These methods should integrate random errors and biases into a unified measure of uncertainty and be easily accessible to evidence appraisers, preferably through user-friendly software.
Aims and Objectives
To address this need, we propose a posterior mixture model and introduce AppRaise, a free, web-based interactive software designed to implement this approach.
Methods
We showcase its application through a health technology assessment (HTA) report on the effectiveness of continuous glucose monitoring in reducing A1c levels among individuals with type 1 diabetes.
Results
Applying the AppRaise software to the HTA report revealed a high level of certainty (86% probability) that continuous glucose monitoring would, on average, result in a reduction in A1c levels compared with self-monitoring of blood glucose among Ontarians with type 1 diabetes. These findings were similar to other quantitative bias-adjusted approaches in systematic reviews.
Conclusion
AppRaise can be utilized as a standalone tool or as a complement to validate the quality of evidence assessed using qualitative-based scoring methods. This approach is also useful for assessing the sensitivity of parameter estimates to potential biases introduced by primary studies.
期刊介绍：
The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.
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