Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
Stat Med. 2010 Aug 15;29(18):1919-31. doi: 10.1002/sim.3953.
For longitudinal data where the response and time-dependent predictors within each individual are measured at distinct time points, traditional longitudinal models such as generalized linear mixed effects models or marginal models cannot be directly applied. Instead, some preprocessing such as smoothing is required to temporally align the response and predictors. We propose a binning method, which results in equally spaced bins of time. After incorporating binning, traditional models can be applied. The proposed binning approach was applied on a longitudinal hemodialysis study to look for possible contemporaneous and lagged effects between occurrences of a health event (i.e. infection) and levels of a protein marker of inflammation (i.e. C-reactive protein). Both Poisson mixed effects models and zero-inflated Poisson (ZIP) mixed effects models were applied to the subsequent data, and some important biological findings about contemporaneous and lagged associations were uncovered. In addition, a simulation study was conducted to investigate various properties of the binning approach.
对于每个个体中的响应和时变预测因子在不同时间点测量的纵向数据，传统的纵向模型（如广义线性混合效应模型或边际模型）不能直接应用。相反，需要进行一些预处理，例如平滑处理，以在时间上对齐响应和预测因子。我们提出了一种分箱方法，该方法会产生时间上等间隔的分箱。在包含分箱后，可以应用传统模型。所提出的分箱方法应用于一项纵向血液透析研究中，以寻找健康事件（即感染）发生和炎症蛋白标志物（即 C 反应蛋白）水平之间可能存在的同期和滞后效应。随后对泊松混合效应模型和零膨胀泊松（ZIP）混合效应模型进行了应用，并揭示了一些关于同期和滞后关联的重要生物学发现。此外，还进行了一项模拟研究，以研究分箱方法的各种特性。
