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程序中，对抽样框架中的所有人员都可以使用某些协变量和代理测量。选择样品后，通常仅将样品中收集的精确协变量信息用于数据分析。忽略采样框架中的信息会导致参数估计的效率损失，并且在流行病学中已得到认可，在流行病学中，已经开发了两相或双抽样方法来更好地利用合并的数据。结果，可以通过双重采样来降低调查中协变量测量的成本。该项目将应用流行病学中开发的双重抽样的想法，以设计和分析采样框架基于人口数据库的样本调查。已经开发了基于加权方案的可能性方法和方法，以结合数据源并提高效率。将在基于选择的样本和真实社会学和流行病学数据的事实分析中比较各种两相方法的实际性能。将研究基于可用相位信息的新方法的新方法。该项目的目的是开发基于经验的建议，如何有效地使用来自大型行政数据库的相位一信息，用于设计基于选择的样本和活动时间数据的两相研究。