{"title":"Event Charts for the Analysis of Adverse Events in Longitudinal Studies: An Example from a Smoking Cessation Pharmacotherapy Trial","authors":"J. Dubin, S. O'Malley","doi":"10.2174/1874297101003010034","DOIUrl":null,"url":null,"abstract":"Purpose: To illustrate the use of one particular graphical method, the event chart (Lee et al., 2000), for the display of adverse events (AE's), along with other important considerations such as time on treatment/intervention, severity of AE's, treatment assignment, gender, etc., in longitudinal studies. These graphs can also include other key information such as efficacy measures and time-dependent covariates of interest. Methods: Emphasizing an application of a dose-ranging smoking cessation trial of naltrexone, we use event charts to convey a few potentially interesting findings from the complex data from this trial, with particular attention paid to the analysis of the safety (AE) data from the subset of individuals who dropped out before the end of the treatment phase of the study. Results: The event charts conveyed some interesting findings regarding relationships between gender, AE's and dropout time, as well as between treatment group, AE's and dropout time, and between AE burden and dropout time. Conclusion: Event charts can be one of the helpful exploratory tools in investigating the pattern of adverse events and their possible association with covariates and time on treatment/intervention in longitudinal studies. Findings from the event chart analysis of AE's could potentially lead to more formal statistical analysis and modeling. Software for generating these event charts is available in R and S-Plus.","PeriodicalId":87834,"journal":{"name":"The open epidemiology journal","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2010-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The open epidemiology journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1874297101003010034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Purpose: To illustrate the use of one particular graphical method, the event chart (Lee et al., 2000), for the display of adverse events (AE's), along with other important considerations such as time on treatment/intervention, severity of AE's, treatment assignment, gender, etc., in longitudinal studies. These graphs can also include other key information such as efficacy measures and time-dependent covariates of interest. Methods: Emphasizing an application of a dose-ranging smoking cessation trial of naltrexone, we use event charts to convey a few potentially interesting findings from the complex data from this trial, with particular attention paid to the analysis of the safety (AE) data from the subset of individuals who dropped out before the end of the treatment phase of the study. Results: The event charts conveyed some interesting findings regarding relationships between gender, AE's and dropout time, as well as between treatment group, AE's and dropout time, and between AE burden and dropout time. Conclusion: Event charts can be one of the helpful exploratory tools in investigating the pattern of adverse events and their possible association with covariates and time on treatment/intervention in longitudinal studies. Findings from the event chart analysis of AE's could potentially lead to more formal statistical analysis and modeling. Software for generating these event charts is available in R and S-Plus.
目的:为了说明在纵向研究中使用一种特殊的图形方法,即事件图(Lee et al., 2000)来显示不良事件(AE),以及其他重要的考虑因素,如治疗/干预时间、AE的严重程度、治疗分配、性别等。这些图表还可以包括其他关键信息,如功效测量和感兴趣的时间相关协变量。方法:强调纳曲酮剂量范围戒烟试验的应用,我们使用事件图表从该试验的复杂数据中传达一些潜在的有趣发现,特别注意分析在研究治疗阶段结束前退出的个体子集的安全性(AE)数据。结果:事件图在性别、AE与辍学时间、治疗组、AE与辍学时间、AE负担与辍学时间的关系上有一些有趣的发现。结论:在纵向研究中,事件图可以作为一种有用的探索性工具,用于调查不良事件的模式及其与协变量和治疗/干预时间的可能关联。声发射事件图分析的结果可能会导致更正式的统计分析和建模。生成这些事件图表的软件在R和S-Plus中可用。