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Innovative strategies using SUDAAN for analysis of health surveys with complex samples

Quintiles, Inc., Research Triangle Park, North Carolina, USA

Sally C Steams

Department of Health Policy and Administration, University of North Carolina, Chapel Hill, North Carolina, USA

Jennifer E Lafata

Center for Clinical Effectiveness, Henry Ford Health System, Detroit, Michigan, USA

Gary G Koch

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA

Babubhai V Shah

Research Triangle Institute, Research Triangle Park, North Carolina, USA

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.

Statistical Methods in Medical Research, Vol. 5, No. 3, 311-329 (1996)
DOI: 10.1177/096228029600500306


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