A Note on Ordinal Modeling of Smoking Rate Data.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Donald Hedeker, Robin J Mermelstein, Juned Siddique
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引用次数: 0

Abstract

Introduction: This paper discusses statistical models for ordinal data that may be more appropriate for smoking rate outcomes than are models that assume continuous measurement and normality. Smoking rate outcomes often have distributions that make them inappropriate for many popular statistical models that assume normality, and are more appropriately considered ordinal outcomes. This article describes how the ordinal logistic regression model can be used to obtain estimates of means, and comparisons of means, for smoking rate outcomes.

Methods: Analyses of the daily smoking rate of a sample of 383 subjects are presented using linear regression and ordinal logistic regression. From the latter, we derive regression estimates (intercepts and slopes) in terms of the mean response without having to assume any distributional form for the smoking rate outcome variable. Regressors considered are the subject's gender and their level of dependency as assessed by the nicotine dependence symptom scale (NDSS).

Results: Estimated regression coefficients were similar, but the linear regression model indicated a significant gender effect, such that females had a higher smoking rate than males. Though similar, this effect was not quite significant (at the 0.05 level) in the ordinal model. The effect of dependency was significant in both models, with more dependent smokers having a higher smoking rate.

Conclusions: Results and conclusions can depend on the assumptions of a statistical model. Methods relaxing the assumption of normality are useful to examine how robust effects are to this common assumption.

Implications: Modeling of smoking rate outcomes can be performed without having to rely on methods that assume a normal distribution. The ordinal model can provide estimates relating to mean differences in smoking rate for the effects of regressors.

关于吸烟率数据的序数模型的说明。
引言:本文讨论了有序数据的统计模型,这些模型可能比假设连续测量和正态性的模型更适合于吸烟率结果。吸烟率结果的分布往往不适合许多假设正态性的流行统计模型,而更适合将其视为有序结果。本文描述了如何使用有序逻辑回归模型来获得吸烟率结果的均值估计和均值比较。方法:采用线性回归和有序逻辑回归对383名调查对象的日吸烟率进行分析。从后者,我们根据平均响应得出回归估计(截距和斜率),而不必假设吸烟率结果变量的任何分布形式。考虑的回归因素是受试者的性别和尼古丁依赖症状量表(NDSS)评估的依赖程度。结果:估计的回归系数相似,但线性回归模型显示了显著的性别效应,女性吸烟率高于男性。虽然相似,但在有序模型中,这种效应并不十分显著(在0.05水平上)。在两个模型中,依赖的影响都是显著的,依赖的吸烟者越多,吸烟率就越高。结论:结果和结论可能取决于统计模型的假设。放松正态性假设的方法对于检验这种共同假设的稳健程度是有用的。启示:对吸烟率结果的建模可以不依赖于假设正态分布的方法。序数模型可以为回归量的影响提供与吸烟率平均差异有关的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nicotine & Tobacco Research
Nicotine & Tobacco Research 医学-公共卫生、环境卫生与职业卫生
CiteScore
8.10
自引率
10.60%
发文量
268
审稿时长
3-8 weeks
期刊介绍: Nicotine & Tobacco Research is one of the world''s few peer-reviewed journals devoted exclusively to the study of nicotine and tobacco. It aims to provide a forum for empirical findings, critical reviews, and conceptual papers on the many aspects of nicotine and tobacco, including research from the biobehavioral, neurobiological, molecular biologic, epidemiological, prevention, and treatment arenas. Along with manuscripts from each of the areas mentioned above, the editors encourage submissions that are integrative in nature and that cross traditional disciplinary boundaries. The journal is sponsored by the Society for Research on Nicotine and Tobacco (SRNT). It publishes twelve times a year.
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