Clinical trials in acute myocardial infarction: should we adjust for baseline characteristics?

BACKGROUND: Clinical trials concerning acute myocardial infarction often evaluate short-term death. Several baseline characteristics are predictors of death, most notably age. Adjustment for one or more predictors in a multivariable analysis may be considered to correct the estimate of the treatment effect for any imbalance that by chance may have occurred between the randomized groups. Moreover, adjustment results in a stratified estimate of the effect of treatment. METHODS AND RESULTS: The effects of adjustment (correction for imbalance and stratification) were studied with logistic regression analysis in the Global Use of Strategies to Open Occluded Coronary Arteries (GUSTO)-I trial. The primary end point was 30-day death, which occurred in 6.3% of 10,348 patients randomly assigned to tissue plasminogen activator and 7.3% of 20,162 patients randomly assigned to streptokinase thrombolytic therapy. This is equivalent to an unadjusted odds ratio of 0.853. No significant imbalance had occurred for any of 17 baseline characteristics considered, including well-known demographic, presenting, and history characteristics. Adjusted for age, the odds ratio was 0.829, which is an 18% increase in estimated effect on the logistic scale. When adjusted for 17 characteristics, the odds ratio was 0.820, an increase of 25%. The increase in effect estimate was largely explained by the stratification effect and only partly by imbalance of predictors. CONCLUSIONS: Adjustment for predictive baseline characteristics, even when largely balanced, may lead to clearly different estimates of the treatment effect on mortality rates. Adjustment for important predictors such as age is recommended in clinical trials studying patients with acute myocardial infarction.

Duke Scholars

Author Kerry L. Lee Biostatistics & Bioinformatics, Division of Biostatistics