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首页> 外文期刊>Journal of nonparametric statistics >Adjusting for baseline information in comparing the efficacy of treatments using bivariate varying-coefficient models
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Adjusting for baseline information in comparing the efficacy of treatments using bivariate varying-coefficient models

机译：使用双变量变系数模型调整基线信息以比较治疗效果

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In biomedical studies, patients' reaction to the treatment can be different depending on their health condition at baseline. In this paper, we develop a bivariate varying-coefficient regression model for longitudinal data with the baseline outcome. The proposed model enables the exploration of the dynamic trend of response variables over time and to provide an effective treatment based on an individual's baseline level of disease by allowing the coefficients to vary with time and baseline. The varying coefficients are modelled through basis function approximation and a set of basis functions is selected by the proposed criterion based on the empirical loglikelihood. After the proposed model is fitted to data, the hypothesis test is designed to evaluate the efficacy of treatments across baseline levels. Theoretical and empirical studies confirm that the proposed methods choose the most parsimonious model consistently and compare the treatment effects successfully across baseline levels. The entire procedure is illustrated with depression data analysis.
机译：在生物医学研究中，患者对治疗的反应可能会有所不同，具体取决于基线时的健康状况。在本文中，我们针对具有基线结果的纵向数据开发了双变量变系数回归模型。所提出的模型使探索随时间变化的响应变量的动态趋势成为可能，并通过允许系数随时间和基线变化而根据个体的基线疾病水平提供有效的治疗方法。通过基函数逼近对变化系数进行建模，并基于经验对数似然，通过提出的标准选择一组基函数。在将提出的模型拟合到数据之后，设计假设检验以评估跨基线水平的治疗效果。理论和经验研究证实，所提出的方法一致地选择了最简约的模型，并成功地比较了整个基线水平的治疗效果。整个过程通过抑郁数据分析进行说明。

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