Statistical Methods for Recurrent Event Data in RSV Immunoprophylaxis Studies

 描述(申请人提供)：慢性病随着时间的推移复发和缓解。随着电子病历的广泛使用，我们能够捕获同一患者在一段时间内多次发生的疾病。因此，在评估疾病恶化或复发的治疗效果时，考虑疾病历史是合乎逻辑的。目前的提案将发展基于非齐次泊松过程(Ross，1996，第78-81页)和相关二元指标总和的方法，以实现这一目标。对于遵循“随时间累积风险”机制的事件，我们将使用非齐次泊松过程来显式模拟事件的当前发作和时变的呼吸道合胞病毒(RSV)免疫预防状态对事件下一次发作几率的影响。目前的经常性事件生存分析和传统的泊松回归都不能使我们做到这一点。对于实际是随时间观察到的二元指标的重复事件，我们将其重新格式化为相关的二元指标变量的时间序列，并对其总和进行建模。我们将使用我们为因变量开发的分布(Yu和Zelterman，2002a和2002b)来开发一类新的广义线性模型，即因变量二元指标的和。这些新的广义线性模型补充了广义估计方程(GEE)方法，使我们能够显式地对事件指标之间的顺序相关性进行建模。所有模型都将通过全面的仿真研究与传统的重复事件生存模型或GEE方法进行比较。这些方法将进一步应用于母体研究-呼吸道合胞病毒的预防：对发病率和哮喘的影响研究(5R01 HS 018454)，以更好地评估呼吸道合胞病毒免疫预防对6岁前呼吸道合胞病毒相关发病率和儿童哮喘的效果。

 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.