Optimal design for epidemiological studies subject to designed missingness
Optimal design for epidemiological studies subject to designed missingness
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DOI：
10.1007/s10985-007-9068-7
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发表时间：
2007-12-01
影响因子：
1.3
通讯作者：
Strauss, Warren
中科院分区：
文献类型：
--
作者：
Morara, Michele;Ryan, Louise;Strauss, Warren
在大型流行病学研究中，预算或后勤限制通常会使研究调查人员无法衡量所有研究对象的所有暴露、协变量和结果。我们开发了一个灵活的理论框架，其中包括一些熟悉的设计，如病例对照和队列研究，以及多阶段抽样设计。我们的框架还允许设计缺失，并包括结果相关设计的选项。我们的公式是基于最大似然的，并将众所周知的关于缺失数据的推断结果推广到多阶段设置。应用了各种技术来简化这些设计的海森矩阵的计算，从而促进了实现各种设计的高效软件工具的开发。
In large epidemiological studies, budgetary or logistical constraints will typically preclude study investigators from measuring all exposures, covariates and outcomes of interest on all study subjects. We develop a flexible theoretical framework that incorporates a number of familiar designs such as case control and cohort studies, as well as multistage sampling designs. Our framework also allows for designed missingness and includes the option for outcome dependent designs. Our formulation is based on maximum likelihood and generalizes well known results for inference with missing data to the multistage setting. A variety of techniques are applied to streamline the computation of the Hessian matrix for these designs, facilitating the development of an efficient software tool to implement a wide variety of designs.