'Population' regression equations were more effective than 'individual' regression equations in allowing detection of differences between treatments, as judged by F values from ANOVA.
Do 'population' derived regression equations improve the detection of treatment differences compared to 'individual' equations for correcting systolic time intervals for heart rate in healthy young adults?
Population-derived regression equations are more effective than individual equations for correcting systolic time intervals for heart rate in clinical pharmacological studies.
We have examined the problem of how systolic time intervals (STI) should be corrected for heart rate in clinical pharmacological studies. 2. 'Individual' linear regression equations describing the relationship between STI and heart rate were derived for each of 43 healthy young adults (30 men and 13 women) by measuring STI at different heart rates produced by incremental doses of intravenous atropine. 'Population' equations for each sex were obtained by taking the mean of the 'individual' regression coefficients. 3. In order to assess which method more effectively reduced variability of the STI, 'individual' regression coefficients were derived for eight men who had previously participated in a placebo-controlled study which had used STI to test the cardiovascular effects of calcium antagonists alone and in combination with propranolol. 4. Within-subject variability in rate-corrected STI was similar after application of 'individual' and 'population' regression equations. Between-subject variability tended to be less after the use of 'population' equations. 5. 'Population' regression equations were more effective than 'individual' regression equations in allowing detection of differences between treatments, as judged by F values from ANOVA. 6. In clinical pharmacological studies including measurements of STI in healthy young subjects, 'individual' regression equations appear to have no advantage over 'population' equations derived in a group of subjects of similar age and sex.
Warrington et al. (Mon,) conducted a other in Healthy (n=43). 'Population' derived regression equations vs. 'Individual' derived regression equations was evaluated on Variability of the systolic time intervals and detection of differences between treatments. 'Population' regression equations were more effective than 'individual' regression equations in allowing detection of differences between treatments, as judged by F values from ANOVA.
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