Scholarlink

<p>In an observational study using data from Kaiser Permanente Northern California (KPNC), we evaluated the effectiveness of respiratory syncytial virus (RSV) immunoprophylaxis palivizumab, a monthly injection that eligible infants receive during the winter RSV season, on RSV-related morbidity, particularly recurrent bronchiolitis in infancy. Traditional methods such as extended Cox Proportional Hazards (CPH) models are commonly used to analyze these recurrent event data. We propose a new approach based on the nonhomogeneous Poisson process that explicitly models the impact of a past event episode and a time-varying treatment on the likelihood of future episodes of the event. Two models, the common-hazard and distinct-hazard models, are developed. These models and the extended CPH model are evaluated in simulation studies and applied to the KPNC dataset.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modeling Sequential Dependence in Recurrent Event Data

  • 期刊论文

  • 发表日期：

    2026年7月20日

查看全文
  • Xiang Huang,
  • Hui Nian,
  • Pingsheng Wu,
  • Sherian X. Li,
  • Gabriel J Escobar,
  • Eileen M Walsh,
  • Tina Hartert,
  • Chang Yu

摘要

In an observational study using data from Kaiser Permanente Northern California (KPNC), we evaluated the effectiveness of respiratory syncytial virus (RSV) immunoprophylaxis palivizumab, a monthly injection that eligible infants receive during the winter RSV season, on RSV-related morbidity, particularly recurrent bronchiolitis in infancy. Traditional methods such as extended Cox Proportional Hazards (CPH) models are commonly used to analyze these recurrent event data. We propose a new approach based on the nonhomogeneous Poisson process that explicitly models the impact of a past event episode and a time-varying treatment on the likelihood of future episodes of the event. Two models, the common-hazard and distinct-hazard models, are developed. These models and the extended CPH model are evaluated in simulation studies and applied to the KPNC dataset.