Between-subject and within-subject statistical information in dental research

The evaluation of risk factors in dental research frequently uses observati ons at multiple sites in the same patient. For this reason, statistical met hods that accommodate correlated data are generally used to assess the sign ificance of the risk factors (e.g., generalized estimating equations, gener alized linear mixed models). In applications of these methods, it is typica lly assumed (implicitly, if not explicitly) that between-subject and within -subject comparisons will produce the same estimated effect of the risk fac tor. When between- and within-subject comparisons conflict, the statistical methods can give biased estimates or results that are difficult to interpr et. For illustration, we present two examples from periodontal disease stud ies in which different statistical methods give different estimates and sig nificance levels for a risk factor. Statistical analyses in dental research should assess whether different sources of information give similar conclu sions about risk factors or treatments.