Measurement Error Case Series Models With Application to Infection-Cardiovascular Risk in Older Patients on Dialysis
考虑测量误差的病例序列模型：老年透析患者感染与心血管风险AI 解读 ↓
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考虑测量误差的病例序列模型：老年透析患者感染与心血管风险
- 研究问题
- 当住院记录只能粗略标记感染起始时间时，如何修正时变暴露时点的测量误差，并评估感染后心血管事件的相对发生率？
- 方法与思路
- 作者提出测量误差病例序列模型，分析忽略暴露起始时间误差造成的偏差，构造偏差校正估计，并以美国肾病住院资料展示应用。
- 材料陈述的贡献
- 摘要称，方法刻画并校正暴露时点误差的估计偏差；应用结果提示，感染起始时间误差会明显削弱感染后三十天的心血管发生率估计。
- 阅读关注点
- 应核对感染时点误差的分布与信息来源、偏差校正所需条件，以及病例序列控制混杂的范围和三十天风险窗口的设定依据。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明，不能推断刊本变化；摘要未给出时点误差模型、完整混杂控制条件或效应数值，因果解释需进一步核对。
原文摘要
Infection and cardiovascular disease are leading causes of hospitalization and death in older patients on dialysis. Our recent work found an increase in the relative incidence of cardiovascular outcomes during the ~ 30 days after infection-related hospitalizations using the case series model, which adjusts for measured and unmeasured baseline confounders. However, a major challenge in modeling/assessing the infection-cardiovascular risk hypothesis is that the exact time of infection, or more generally "exposure," onsets cannot be ascertained based on hospitalization data. Only imprecise markers of the timing of infection onsets are available. Although there is a large literature on measurement error in the predictors in regression modeling, to date there is no work on measurement error on the timing of a time-varying exposure to our knowledge. Thus, we propose a new method, the measurement error case series (MECS) models, to account for measurement error in time-varying exposure onsets. We characterized the general nature of bias resulting from estimation that ignores measurement error and proposed a bias-corrected estimation for the MECS models. We examined in detail the accuracy of the proposed method to estimate the relative incidence. Hospitalization data from United States Renal Data System, which captures nearly all (> 99%) patients with end-stage renal disease in the U.S. over time, is used to illustrate the proposed method. The results suggest that the estimate of the cardiovascular incidence following the 30 days after infections, a period where acute effects of infection on vascular endothelium may be most pronounced, is substantially attenuated in the presence of infection onset measurement error.