配对计数回归模型的医生咨询和非处方药摄入的数量。

IF 1.6 3区 医学 Q3 HEALTH CARE SCIENCES & SERVICES
Jussiane Nader Gonçalves, Wagner Barreto-Souza, Hernando Ombao
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引用次数: 0

摘要

在各种实际情况下,配对计数变量是相关的,因此需要联合估计方法。在本文中,我们引入了一类灵活的二元混合泊松回归模型,该模型为不可观测异质性的指数族(EF)分布分量。所提出的二元混合泊松模型处理了典型的计数数据的过色散现象,并且在相关结构方面具有灵活性。因此,与最广泛使用的模型相比,这种新型模型具有明显的优势,因为它捕获计数数据中的正相关性和负相关性。在二元混合泊松模型下,通过极大似然法对参数进行推理。给出了评价参数估计器有限样本性能的蒙特卡罗方法。此外,我们采用似然比统计来检验某些相关源的显著性,并通过模拟研究评估其性能。此外,采用模拟包络进行残差分析,并采用随机概率积分变换进行校正模型控制,解决了模型的充分性问题。提出的双变量混合泊松模型被考虑用于分析来自澳大利亚健康调查的医疗数据集,我们的目的是研究与医生咨询次数和非处方药摄入量之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Paired count regressions for modeling the number of doctor consultations and non-prescribed drugs intake.

There are sundry practical situations in which paired count variables are correlated, thus requiring a joint estimation method. In this article, we introduce a flexible class of bivariate mixed Poisson regression models, which settle into an exponential-family (EF) distributed component for unobserved heterogeneity. The proposed bivariate mixed Poisson models deal with the phenomenon of overdispersion, typical of count data, and have flexibility in terms of the correlation structure. Thus, this novel class of models has a distinct advantage over the most widely used models because it captures both positive and negative correlations in the count data. Under the bivariate mixed Poisson model, inference of the parameters is conducted through the maximum likelihood method. Monte Carlo studies on assessing the finite-sample performance of the estimators of the parameters are presented. Furthermore, we employ a likelihood ratio statistic for testing the significance of certain sources of correlation and evaluate its performance via simulation studies. Moreover, model adequacy is addressed by using simulated envelopes for residual analysis, and also a randomized probability integral transformation for calibration model control. The proposed bivariate mixed Poisson model is considered for analyzing a healthcare dataset from the Australian Health Survey, where our aim is to study the association between the number of consultations with a doctor and the number of non-prescribed drug intake.

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来源期刊
Statistical Methods in Medical Research
Statistical Methods in Medical Research 医学-数学与计算生物学
CiteScore
4.10
自引率
4.30%
发文量
127
审稿时长
>12 weeks
期刊介绍: Statistical Methods in Medical Research is a peer reviewed scholarly journal and is the leading vehicle for articles in all the main areas of medical statistics and an essential reference for all medical statisticians. This unique journal is devoted solely to statistics and medicine and aims to keep professionals abreast of the many powerful statistical techniques now available to the medical profession. This journal is a member of the Committee on Publication Ethics (COPE)
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