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首页> 外文学位 >Marginal modeling of longitudinal, binary response data: Semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design.
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Marginal modeling of longitudinal, binary response data: Semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design.

机译：纵向，二进制响应数据的边际建模：具有长响应序列的半参数和参数估计以及有效的取决于结果的采样设计。

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Longitudinal data analysis is fundamental to characterizing changes that occur over time. The research contained in this dissertation is focused on models and estimation procedures for marginal regression modeling long series of binary response data. There are three primary topics: (1) Semiparametric estimation, (2) models for parametric estimation and (3) study design. Semiparametric estimators in the presence time-varying covariates are examined. We study the bias-efficiency tradeoff with covariance weighted, Generalized Estimating Equations (Liang and Zeger, 1986) estimators of cross-sectional mean model parameters [e.g., parameters in E(Y ij|Xij)] when the true model is given by the full covariate conditional mean, E( Yij|Xi1, Xi2,..., Xini ). While Pepe and Anderson (1994) showed that biased estimates will likely result in this scenario unless a diagonal working covariance weighting scheme is used, many authors (e.g., Zhao et al., 1992; Fitzmaurice, 1995; Mancl and Leroux, 1996) have shown that covariance weighted estimates are far more efficient. We examine the tradeoff between validity and efficiency by exploring data features. Next, we study marginalized regression models (Heagerty, 1999). Marginalized models are a flexible class of models that permit likelihood-based estimation of marginal regression model parameters. We extend this class by proposing a model to accommodate response dependence structures for long response series (e.g., those that exhibit short-range serial and long-range response dependence). We describe a maximum likelihood estimation procedure and evaluate asymptotic properties under dependence model misspecification. We also describe a strategy by which inference can be made with Bayesian methods. Finally, we propose an outcome dependent sampling design for longitudinal binary data when exposure ascertainment costs are high and when a large percentage of subjects do not exhibit variation in their response series (e.g., symptoms at every timepoint or no symptoms at any timepoint). Our design measures exposure only on subjects who exhibit response variation. We modify the marginalized model likelihood and propose a maximum conditional likelihood estimation procedure that acknowledges the study design. We evaluate asymptotic properties of the estimators, including showing that in plausible scenarios, they can be highly efficient compared to maximum likelihood estimators based on the entire cohort.
机译：纵向数据分析是表征随时间变化的基础。本文的研究主要集中在对二元响应数据进行长边际回归建模的模型和估计程序上。主要有三个主题：（1）半参数估计，（2）参数估计模型和（3）研究设计。检验存在时变协变量中的半参数估计量。当真实模型由方程组给出真实模型时，我们使用协方差加权的广义估计方程（Liang和Zeger，1986）估计器来研究偏倚效率折衷，该估计方程用于估计截面均值模型参数[例如，E（Y ij | Xij）中的参数]。完全协变量条件均值E（Yij | Xi1，Xi2，...，Xini）。尽管Pepe和Anderson（1994）指出，除非使用对角工作协方差加权方案，否则在这种情况下估计可能会产生偏差，但许多作者（例如，Zhao等，1992； Fitzmaurice，1995； Mancl和Leroux，1996）表明协方差加权估计效率更高。我们通过探索数据特征来检验有效性和效率之间的权衡。接下来，我们研究边缘化回归模型（Heagerty，1999）。边际化模型是一类灵活的模型，允许基于可能性的边际回归模型参数估计。我们通过提出一个模型来适应长响应系列的响应依赖结构（例如，显示短距离序列和长距离响应依赖的结构）来扩展此类。我们描述了最大似然估计程序并评估依赖模型错误指定下的渐近性质。我们还描述了一种可以通过贝叶斯方法进行推理的策略。最后，当暴露确定成本高且大部分受试者的反应序列没有变化（例如，每个时间点出现症状或任何时间点都没有症状）时，我们为纵向二进制数据提出一种基于结果的抽样设计。我们的设计仅对表现出反应差异的受试者测量暴露量。我们修改了边缘化模型的似然性，并提出了承认研究设计的最大条件似然估计程序。我们评估了估计量的渐近性质，包括表明在合理的情况下，与基于整个队列的最大似然估计量相比，它们可能是高效的。

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