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Use of population data bases with double sampling for choice-based sample surveys and time-to-event data

使用人口数据库和双重抽样进行基于选择的抽样调查和事件时间数据

基本信息

批准号：
162449825
负责人：
Professorin Dr. Iris Pigeot
金额：
--
依托单位：
Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS GmbH
依托单位国家：
德国
项目类别：
Priority Programmes
财政年份：
2010
资助国家：
德国
起止时间：
2009-12-31 至 2011-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/162449825?language=en
关键词：
Use population data bases double

项目摘要

In many research applications based on administrative data bases, certain covariates and proxy measurements are available for all persons in a sampling frame. After sample selection, usually only the precise covariate information collected in the sample is used for data analysis. Ignoring the information in the sampling frame results in an efficiency loss in parameter estimation and has been acknowledged in epidemiology, where two-phase or double sampling methods have been developed to make better use of the combined data. As a consequence, the cost of covariate measurements in surveys can be reduced by double sampling. This project will apply the ideas of double sampling developed in epidemiology for the design and analysis of sample surveys where the sampling frame is based on a population data base. Likelihood oriented approaches and methods based on weighting schemes have been developed to combine the data sources and enhance efficiency. The actual performance of the diverse two-phase methods will be compared in choice-based samples and time-to-event analyses of authentic sociological and epidemiological data. New methods for stratification that build on available phase-one information will be investigated. The goal of the project is the development of empirically based recommendations how phase-one information from large administrative data bases can be efficiently used in the design of two-phase studies for choice-based samples and time-to-event data.

在许多基于行政数据库的研究应用中，某些协变量和替代测量值可用于抽样框架中的所有人。在样本选择之后，通常只有在样本中收集到的精确协变量信息才用于数据分析。忽略抽样框架中的信息会导致参数估计的效率损失，这在流行病学中得到了承认，在流行病学中，已经开发了两阶段或双重抽样方法，以更好地利用合并数据。因此，在调查中协变量测量的成本可以通过双重抽样来降低。本项目将应用流行病学中发展的双重抽样思想，设计和分析抽样调查，抽样框架以人口数据库为基础。已经开发了基于加权方案的面向可能性的途径和方法，以联合收割机组合数据源并提高效率。不同的两阶段方法的实际表现将在基于选择的样本和真实的社会学和流行病学数据的时间到事件的分析进行比较。将研究建立在现有第一阶段信息基础上的新的分层方法。该项目的目标是发展经验为基础的建议，如何从大型行政数据库的第一阶段的信息可以有效地用于设计的两个阶段的研究选择为基础的样本和时间的事件数据。