Misspecified autocorrelation structures in data analysis lead to inaccurate parameter estimates.
Researchers investigated using multivariate models to analyze longitudinal data sets with multiple response variables and covariates in medical studies. By examining correlations between response variables and autocorrelation over time, they found that accurately specifying the autocorrelation structure is crucial. Comparing simpler models with multivariate models, they discovered that incorrect specification of autocorrelation negatively impacts parameter estimates.