Affiliations: [a] Institute of Agricultural, Rural and Environmental Health, Saskatoon, Canada | [b] Community Health and Epidemiology, Saskatoon, Canada | [c] College of Nursing, University of Saskatchewan, Saskatoon, Canada
Abstract: Survey data analysis using complex sampling designs ought to account for clustering, stratification and unequal probability of selection. Design-based and model-based methods are two commonly used routes taken to account for such survey designs. Several studies of cross-sectional survey designs have shown that these two approaches provide similar results when the model fits the data well. The present paper aims at comparing these two approaches for longitudinal survey design using the National Population Health Survey (NPHS) dataset. A marginal modeling approach proposed by Rao and modified bootstrap method for longitudinal data were used by way of a design-based method. The Generalized Estimating Equation (GEE) method, proposed by Liang and Zeger was used as a typical model-based approach. The parameter estimates obtained using the design-based and model-based methods were similar. However, the standard errors and the 95% confidence interval were different. Rao's method produced the most conservative standard errors. In conclusion, design-based methods should be preferred over model-based methods, as this method provides reliable results.
Keywords: Multistage sampling, GEE, survey GEE, taylor linearization, bootstrap, NPHS
