Models for Reliability Studies In a typical interrater reliability study, each of a random sample of n targets is rated independently by k judges. Three different

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Some statistical techniques for analyzing the kinds of studies typically reported in Circulation Research are described. Particular emphasis is given to the comparison of means from more than two populations, the joint effect of several experimentally controlled variables, and the analysis of studies with repeated measurements on the same experimental units.

Background. We studied 715 patients 2 weeks after myocardial infarction to establish the associations between six frequency domain measures of heart period variability (HPV) and mortality during 4 years of follow-up.Methods and Results. Each measure of HPV had a significant and at least moderately strong univariate association with all-cause mortality, cardiac death, and arrhythmic death. Power in the lower-frequency bands -ultra low frequency (ULF) and very low frequency (VLF) powerhad stronger associations with all three mortality end points than power in the higherfrequency bands -low frequency (LF) and high frequency (HF) power. The 24-hour total power also had a significant and strong association with all three mortality end points. VLF power was the only variable that was more strongly associated with arrhythmic death than with cardiac

Two statistics, kappa and weighted kappa, are available for measuring agreement between two raters on a nominal scale. Formulas for the standard errors of these two statistics have been given in the literature, but they are in error. The errors seem to be in the direction of overestimation, so that the use of the incorrect formulas results in conservative significance tests and confidence intervals. Valid formulas for the approximate large-sample variances are given, and their calculation is illustrated using a numerical example.The statistics kappa (Cohen, 1960) and weighted kappa (Cohen, 1968) were introduced to provide coefficients of agreement between two raters for nominal scales. Kappa is appropriate when all disagreements may be considered equally serious, and weighted kappa is appropriate when the relative seriousness of the different possible disagreements can be specified.The papers describing these two statistics also present expressions for their standard errors. These expressions are incorrect, having been derived from the contradictory assumptions of fixed marginal totals and binomial variation of cell frequencies. Everitt (1968) derived the exact variances of weighted and unweighted kappa when the parameters are zero by assuming a generalized hypergeometric distribution. He found these expressions to be far too complicated for routine use, and offered, as alternatives, expressions derived by assuming binomial distributions. These alternative expressions are incorrect, essentially for the same reason as above.Assume that N subjects are distributed into k* cells by each of them being assigned to one of k categories by one rater and, independently, to one of the same k categories by a second