Statistical Methods for Recurrent Event Data in RSV Immunoprophylaxis Studies

 描述（由申请人提供）：慢性疾病复发和缓解随着时间的推移。随着电子医疗记录的广泛使用，我们能够在同一患者中捕获随着时间的推移多次发生的疾病。因此，在评估对疾病加重或复发的治疗效果时，考虑病史是合乎逻辑的。目前的建议将开发基于非齐次泊松过程（罗斯，1996年，第78-81页）和依赖二进制指标的总和，以实现这一目标的方法。对于遵循“随时间推移的累积风险”机制的事件，我们将使用非齐次泊松过程明确建模当前事件发作和随时间变化的呼吸道合胞病毒（RSV）免疫预防状态对下一次事件发作机会的影响。目前的复发事件生存分析和传统的Poisson回归都无法做到这一点。对于随着时间的推移观察到的真正的二进制指标的经常性事件，我们将经常性事件重新格式化为依赖二进制指标变量的时间序列，并对其总和进行建模。我们将开发一类新的广义线性模型，使用我们开发的分布（Yu和Zelterman，2002 a和2002 b）为因变量，因变量二进制指标的总和。这些新的广义线性模型通过允许我们显式地对事件指标之间的顺序相关性进行建模，补充了广义估计方程（GEE）方法。所有开发的模型将通过全面的模拟研究与传统的复发事件生存模型或GEE方法进行比较。这些方法将进一步应用于母研究，即预防RSV：对发病率和哮喘的影响研究（5 R 01 HS 018454），以更好地评价RSV免疫预防对6岁时RSV相关发病率和儿童哮喘的影响。

 DESCRIPTION (provided by applicant): Chronic diseases relapse and remit over time. With the widespread use of electronic medical records, we are able to capture multiple occurrences of the disease over time in the same patient. It is therefore logical to account for disease histor when assessing the treatment effect on disease exacerbation or recurrence. The current proposal will develop methods based on a non-homogeneous Poisson process (Ross, 1996, pp 78-81) and on sums of dependent binary indicators to achieve this goal. For events that follow the "cumulative hazard over time" mechanism, we will use a non-homogeneous Poisson process to explicitly model the impact of the current episode of the event and time-varying respiratory syncytial virus (RSV) immunoprophylaxis status on the chance of the next episode of the event. Neither the current recurrent event survival analysis nor the traditional Poisson regression would enable us to do so. For recurrent events that are really binary indicators observed over time, we reformat the recurrent events as a time series of dependent binary indicator variables and model their sum. We will develop a new class of generalized linear models using distributions we developed (Yu and Zelterman, 2002a and 2002b) for the dependent variable, sums of dependent binary indicators. These new generalized linear models compliment the generalized estimating equation (GEE) method by allowing us to explicitly model sequential dependence among the event indicators. All developed models will be compared with traditional recurrent event survival models or the GEE method through comprehensive simulation studies. These methods will be further applied to the parent study, the Prevention of RSV: Impact on Morbidity and Asthma study (5 R01 HS 018454), to better evaluate the effect of RSV immunoprophylaxis on RSV related morbidity and childhood asthma by age 6 years.