Background: Extensive left ventricular (LV) dilatation after myocardial infarction (MI) is associated with increased heart failure risk.
Aims: To investigate whether the power to demonstrate the relation between LV dilatation and heart failure depends on the method applied to predict LV dilatation after MI.
Methods: A random-effects model and ANOVA model for repeated measurements (MANOVA) were applied to predict LV volume index during 1 year for 298 post-MI patients. Spearman correlation coefficients (r) were calculated and Cox regression analysis was used to calculate risk ratio's (RR).
Results: LV volume indices were more accurately predicted by a random-effects model than by a MANOVA model (systolic/diastolic respectively r = 0.93/0.91 vs. r = 0.67/0.64). Furthermore, patients with high LV volume index as predicted by the random-effects model, had significantly increased heart failure risk (systolic RR 2.04 (95% CI: 1.31 to 3.17; P = 0.001), diastolic RR 1.80 (95% CI: 1.16 to 2.78; P = 0.007). Using the same data, MANOVA failed to demonstrate this relation significantly (systolic RR 1.77 (95% CI: 0.79 to 3.98; P = 0.16), diastolic RR 1.49 (95% CI: 0.68 to 3.30; P = 0.31).
Conclusion: When analyzing repeated measurement data, random-effect models are more powerful in detecting clinical relations than are MANOVA models, especially in the presence of missing values.