Statistical Inference in Database Exploitation: Sample Size and Confounding Factors.

Abstract

Database (DB) exploitation is an essential tool in medical and epidemiological research, enabling the extraction of hidden insights from large volumes of information through statistical analysis. This work provides a methodological reflection on two critical aspects of such studies, namely sample size and confounder detection via multivariate analysis. When working with databases, researchers must perform statistical inference based exclusively on the available data and therefore often ask whether the fixed sample size is sufficient to detect relationships of interest. In most cases, no threshold value separates valid sizes from invalid ones, as statistical power increases gradually and depends on multiple parameters, with no fixed cutoff points. Even when the results are inconclusive (high p-values), possibly because of limited sample size, their publication is essential to feed future meta-analyses that may provide more solid conclusions. DB analysis requires discriminating between general and circumstantial associations. Although the relationships revealed by univariate analysis maintain their descriptive value, the accuracy of their interpretation can be increased by identifying possible confounding factors.