{"title":"了解和计算治疗所需数量的指南。","authors":"Valentin Vancak, Yair Goldberg, Stephen Z Levine","doi":"10.1136/ebmental-2020-300232","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>We aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.</p><p><strong>Methods: </strong>The NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis <i>et al</i>'s NNT, Kraemer and Kupfer's NNT and the Furukawa and Leucht's NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.</p><p><strong>Results: </strong>The application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.</p><p><strong>Conclusion: </strong>This application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"131-136"},"PeriodicalIF":6.6000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231569/pdf/ebmental-2020-300232.pdf","citationCount":"10","resultStr":"{\"title\":\"Guidelines to understand and compute the number needed to treat.\",\"authors\":\"Valentin Vancak, Yair Goldberg, Stephen Z Levine\",\"doi\":\"10.1136/ebmental-2020-300232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>We aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.</p><p><strong>Methods: </strong>The NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis <i>et al</i>'s NNT, Kraemer and Kupfer's NNT and the Furukawa and Leucht's NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.</p><p><strong>Results: </strong>The application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.</p><p><strong>Conclusion: </strong>This application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.</p>\",\"PeriodicalId\":12233,\"journal\":{\"name\":\"Evidence Based Mental Health\",\"volume\":\"24 4\",\"pages\":\"131-136\"},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231569/pdf/ebmental-2020-300232.pdf\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Evidence Based Mental Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/ebmental-2020-300232\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evidence Based Mental Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/ebmental-2020-300232","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Guidelines to understand and compute the number needed to treat.
Objective: We aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.
Methods: The NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis et al's NNT, Kraemer and Kupfer's NNT and the Furukawa and Leucht's NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.
Results: The application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.
Conclusion: This application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.
期刊介绍：
Evidence-Based Mental Health alerts clinicians to important advances in treatment, diagnosis, aetiology, prognosis, continuing education, economic evaluation and qualitative research in mental health. Published by the British Psychological Society, the Royal College of Psychiatrists and the BMJ Publishing Group the journal surveys a wide range of international medical journals applying strict criteria for the quality and validity of research. Clinicians assess the relevance of the best studies and the key details of these essential studies are presented in a succinct, informative abstract with an expert commentary on its clinical application.Evidence-Based Mental Health is a multidisciplinary, quarterly publication.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
