Modeling Individual Patient Count/Rate Data over Time with Applications to Cancer Pain Flares and Cancer Pain Medication Usage.

统计学期刊(英文) Pub Date : 2021-10-01 Epub Date: 2021-09-30 DOI:10.4236/ojs.2021.115038
George J Knafl, Salimah H Meghani
{"title":"Modeling Individual Patient Count/Rate Data over Time with Applications to Cancer Pain Flares and Cancer Pain Medication Usage.","authors":"George J Knafl,&nbsp;Salimah H Meghani","doi":"10.4236/ojs.2021.115038","DOIUrl":null,"url":null,"abstract":"<p><p>The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non-constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time, because in example analyses, it either generates better LCV scores or more parsimonious models and requires substantially less time.</p>","PeriodicalId":59624,"journal":{"name":"统计学期刊(英文)","volume":"11 5","pages":"633-654"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351387/pdf/","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"统计学期刊(英文)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/ojs.2021.115038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/9/30 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non-constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time, because in example analyses, it either generates better LCV scores or more parsimonious models and requires substantially less time.

Abstract Image

Abstract Image

Abstract Image

随着时间的推移,个体患者计数/率数据与癌症疼痛发作和癌症疼痛药物使用的应用建模。
本文的目的是研究在考虑时间相关性和非恒定分散的情况下,对个体患者计数/率数据进行建模的方法,同时需要合理的时间来搜索这些数据的替代模型。本文研究了扩展广义估计方程(GEE)建模的两种方法的公式。这些方法使用基于多元正态密度的似然函数。第一种方法扩充了标准GEE方程,使之包括估计色散参数的方程。第二种方法是基于由类似然函数对所有模型参数的偏导数确定的估计方程,从而扩展了线性混合建模。考虑了三种相关结构,包括独立的、可交换的和空间自回归的1阶相关。类似然函数用于制定类似然交叉验证(LCV)分数,用于评估模型。本文给出了将这两种建模方法应用于三个数据集的示例分析,这些数据集包括每个癌症患者每天的疼痛发作次数、每天服用的必要止痛药以及每天服用的每剂量的全天候止痛药。使用自适应回归建模方法将均值和离散度建模为可能的非线性时间函数,以搜索与LCV分数比较的替代模型。这些分析的结果表明,扩展线性混合建模更适合随着时间的推移对个体患者计数/率数据进行建模,因为在示例分析中,它要么生成更好的LCV分数,要么生成更简洁的模型,并且需要更少的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
571
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信