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European Journal of Heart Failure 2002 4(3):277-282; doi:10.1016/S1388-9842(02)00003-X
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© 2002 European Society of Cardiology

Approaches to statistical analysis of repeated echocardiographic measurements after myocardial infarction and its relation to heart failure: Application of a random-effects model

Pieter-Jan de Kama, Adriaan A. Voorsb,c, Jan Brouwerd, Martin St John Suttone and Wiek H. van Gilstc,*

a Trial Co-ordination Center University Hospital Groningen, Groningen, The Netherlands
b Department of Cardiology St. Anthonius Hospital, Nieuwegein, The Netherlands
c Department of Clinical Pharmacology University of Groningen, Groningen, The Netherlands
d Department of Cardiology University Hospital Groningen, Groningen, The Netherlands
e Cardiovascular Division University of Pennsylvania Medical Center, Pennsylvania, USA

* Corresponding author. Tel.: +31-50-3632811. E-mail address: p_j_de_kam{at}hotmail.com


  Abstract

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.

Key Words: Heart failure • Random-effects model • Repeated measurements • Left ventricular dilatation • Myocardial infarction

Received March 1, 2001; Revised July 4, 2001; Accepted December 14, 2001


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