LaVange L M, Stearns S C, Lafata J E, Koch G G, Shah B V
Quintiles, Inc., Research Triangle Park, NC 27709-3979, USA.
Stat Methods Med Res. 1996 Sep;5(3):311-29. doi: 10.1177/096228029600500306.
Large-scale health surveys provide a wealth of information for addressing problems in health sciences research. Designed for multiple purposes, these surveys frequently have large sample sizes and extensive measurements of demographic and socioeconomic characteristics, risk factors, disease outcomes and health care service use and costs. Complex features of the sampling design typically employed to select the survey sample, coupled with the vast amount of information available from the survey database, underlie issues that must be addressed during data processing and analysis. Numerous articles in the literature have focused on the debate of whether or not, and how, to control for features of the sample design during data analysis. Traditional statistical methods for simple random samples and the software that accompanies them have historically not had the capacity to account for the survey design. Recent advancements in statistical methodology for survey data analysis have greatly expanded the analytical tools available to the survey analyst. Commercial software packages that incorporate these methods offer the analyst convenient ways for applying such tools to large survey databases in an easy and efficient manner. We present an overview of analysis strategies for survey data and illustrate their application via the SUDAAN software system. Examples for analyses are provided through data from two large US health surveys, the National Health Interview Survey and the Longitudinal Study of Aging. Questions of both a cross-sectional and longitudinal nature are addressed. The examples involve logistic regression, time-to-event analysis, and repeated measures analysis.
大规模健康调查为解决健康科学研究中的问题提供了丰富的信息。这些调查旨在实现多种目的，通常具有较大的样本量，并广泛测量人口统计学和社会经济特征、风险因素、疾病结局以及医疗服务的使用情况和成本。用于选择调查样本的抽样设计的复杂特征，再加上从调查数据库中获得的大量信息，构成了数据处理和分析过程中必须解决的问题。文献中的众多文章都聚焦于在数据分析过程中是否以及如何控制样本设计特征的争论。传统的简单随机样本统计方法及其配套软件历来都无法考虑调查设计。调查数据分析统计方法的最新进展极大地扩展了可供调查分析师使用的分析工具。包含这些方法的商业软件包为分析师提供了便捷的方式，以便轻松、高效地将此类工具应用于大型调查数据库。我们概述了调查数据分析的策略，并通过SUDAAN软件系统说明其应用。通过来自两项大型美国健康调查（国家健康访谈调查和老龄化纵向研究）的数据提供分析示例。解决了横断面和纵向性质的问题。示例涉及逻辑回归、事件发生时间分析和重复测量分析。
