Avoiding Blunders When Analyzing Correlated Data, Clustered Data, or Repeated Measures

Rheumatology research often involves correlated and clustered data. A common error when analyzing these data occurs when instead we treat these data as independent observations. This can lead to incorrect statistical inference. The data used are a subset of the 2017 study from Raheel et al consisting of 633 patients with rheumatoid arthritis (RA) between 1988 and 2007. RA flare and the number of swollen joints served as our binary and continuous outcomes, respectively. Generalized linear models (GLM) were fitted for each, while adjusting for rheumatoid factor (RF) positivity and sex. Additionally, a generalized linear mixed model with a random intercept and a generalized estimating equation were used to model RA flare and the number of swollen joints, respectively, to take additional correlation into account. The GLM's beta coefficients and their 95% confidence intervals (CIs) are then compared to their mixed-effects equivalents. The beta coefficients compared between methodologies are very similar. However, their standard errors increase when correlation is accounted for. As a result, if the additional correlations are not considered, the standard error can be underestimated. This results in an overestimated effect size, narrower CIs, increased type I error, and a smaller P value, thus potentially producing misleading results. It is important to model the additional correlation that occurs in correlated data.

Automated summary

Rheumatology research often involves correlated and clustered data. Treating these data as independent observations can lead to incorrect statistical inference. In this study, data from 633 rheumatoid arthritis patients were analyzed using generalized linear models, while adjusting for factors such as rheumatoid factor positivity and sex, as well as considering additional correlation using generalized linear mixed models and generalized estimating equations. The study found that when correlation was accounted for, standard errors increased, resulting in overestimated effect size, narrower confidence intervals, increased type I error, and potentially misleading results.