{"title":"A practical guide to the appropriate analysis of eGFR data over time: A simulation study","authors":"Todd DeVries, Kevin J. Carroll, Sandra A. Lewis","doi":"10.1002/pst.2381","DOIUrl":null,"url":null,"abstract":"In several therapeutic areas, including chronic kidney disease (CKD) and immunoglobulin A nephropathy (IgAN), there is a growing interest in how best to analyze estimated glomerular filtration rate (eGFR) data over time in randomized clinical trials including how to best accommodate situations where the rate of change is not anticipated to be linear over time, often due to possible short term hemodynamic effects of certain classes of interventions. In such situations, concerns have been expressed by regulatory authorities that the common application of single slope analysis models may induce Type I error inflation. This article aims to offer practical advice and guidance, including SAS codes, on the statistical methodology to be employed in an eGFR rate of change analysis and offers guidance on trial design considerations for eGFR endpoints. A two‐slope statistical model for eGFR data over time is proposed allowing for an analysis to simultaneously evaluate short term acute effects and long term chronic effects. A simulation study was conducted under a range of credible null and alternative hypotheses to evaluate the performance of the two‐slope model in comparison to commonly used single slope random coefficients models as well as to non‐slope based analyses of change from baseline or time normalized area under the curve (TAUC). Importantly, and contrary to preexisting concerns, these simulations demonstrate the absence of alpha inflation associated with the use of single or two‐slope random coefficient models, even when such models are misspecified, and highlight that any concern regarding model misspecification relates to power and not to lack of Type I error control.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.2381","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
In several therapeutic areas, including chronic kidney disease (CKD) and immunoglobulin A nephropathy (IgAN), there is a growing interest in how best to analyze estimated glomerular filtration rate (eGFR) data over time in randomized clinical trials including how to best accommodate situations where the rate of change is not anticipated to be linear over time, often due to possible short term hemodynamic effects of certain classes of interventions. In such situations, concerns have been expressed by regulatory authorities that the common application of single slope analysis models may induce Type I error inflation. This article aims to offer practical advice and guidance, including SAS codes, on the statistical methodology to be employed in an eGFR rate of change analysis and offers guidance on trial design considerations for eGFR endpoints. A two‐slope statistical model for eGFR data over time is proposed allowing for an analysis to simultaneously evaluate short term acute effects and long term chronic effects. A simulation study was conducted under a range of credible null and alternative hypotheses to evaluate the performance of the two‐slope model in comparison to commonly used single slope random coefficients models as well as to non‐slope based analyses of change from baseline or time normalized area under the curve (TAUC). Importantly, and contrary to preexisting concerns, these simulations demonstrate the absence of alpha inflation associated with the use of single or two‐slope random coefficient models, even when such models are misspecified, and highlight that any concern regarding model misspecification relates to power and not to lack of Type I error control.
期刊介绍:
Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics.
The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.