Complex sampling: Implications for data analysis

Investigators in dental public health often use strategies other than simple random sampling to identify potential subjects; however, their statistical analyses do not always fake into account the complex sampling mechanism. Often if is not clear whether a given strategy requires adjustment for stratification and/or cluster sampling of observations We propose that the need for such adjustment depends on the primary study objective. As a general rule, we recommend that if the study goal is to estimate the magnitude of either a population Value of interest (e.g., prevalence), or an established exposure-outcome association, adjustment of variances to reflect complex sampling is essential because obtaining appropriate variance estimates is a priority. However, if the study goal is to establish the presence of an association, especially in a preliminary investigation of novel conditions or understudied populations, obtaining appropriate variance estimates may not be of primary importance; hence, adjustment of variances for complex sampling is not always required, but often is recommended. This paper describes several types of complex sampling designs, methods of: adjusting for complex sampling strategies, examples illustrating the effect of adjustment, and alternative approaches for analysis of complex samples.