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Decreased heart rate variability has been associated with an adverse prognosis in patients after myocardial infarction. Studies carried out in the population at large show contradictory results. The authors examined the association between heart rate variability on a standard 10-second electrocardiogram and cardiac and all-cause mortality in the Rotterdam Study, a population-based cohort study of men and women aged > or =55 years, using data collected between 1990 and 1996 (mean follow-up = 4 years). Heart rate variability, taken as the standard deviation of normal R-R intervals (SDNN), was computed by means of the Modular ECG Analysis System. After exclusion of subjects with arrhythmia and those with fewer than six normal R-R intervals, the study population consisted of 2,088 men and 3,184 women. Cox's proportional hazards model was used to examine the age- and sex-adjusted risk for cardiac, noncardiac, and total mortality in relation to quartiles of SDNN, using the third quartile of SDNN as the reference category. Subjects in the lowest quartile of SDNN relative to those in the third quartile had an 80 percent age- and sex-adjusted increased risk for cardiac mortality (hazard ratio = 1.8; 95% confidence interval: 1.0, 3.2). Interestingly, for subjects in the highest quartile of SDNN, an even more pronounced risk for cardiac mortality was present (hazard ratio = 2.3; 95% confidence interval: 1.3, 4.0). Additional adjustment for possible confounders did not materially change the risk estimates. The authors conclude that heart rate variability measured on the standard 10-second electrocardiogram can be used to identify older men and women with an increased risk for cardiac mortality. In the elderly, increased heart rate variability is an even stronger indicator of cardiac mortality than decreased heart rate variability. Further studies are needed to confirm these findings and to elucidate their physiologic meaning.

A relationship between baseline risk and treatment effect is increasingly investigated as a possible explanation of between‐study heterogeneity in clinical trial meta‐analysis. An approach that is still often applied in the medical literature is to plot the estimated treatment effects against the estimated measures of risk in the control groups (as a measure of baseline risk), and to compute the ordinary weighted least squares regression line. However, it has been pointed out by several authors that this approach can be seriously flawed. The main problem is that the observed treatment effect and baseline risk measures should be viewed as estimates rather than the true values. In recent years several methods have been proposed in the statistical literature to potentially deal with the measurement errors in the estimates. In this article we propose a vague priors Bayesian solution to the problem which can be carried out using the ‘Bayesian inference using Gibbs sampling’ (BUGS) implementation of Markov chain Monte Carlo numerical integration techniques. Different from other proposed methods, it uses the exact rather than an approximate likelihood, while it can handle many different treatment effect measures and baseline risk measures. The method differs from a recently proposed Bayesian method in that it explicitly models the distribution of the underlying baseline risks. We apply the method to three meta‐analyses published in the medical literature and compare the results with the outcomes of the other recently proposed methods. In particular we compare our approach to McIntosh's method, for which we show how it can be carried out using standard statistical software. We conclude that our proposed method offers a very general and flexible solution to the problem, which can be carried out relatively easily with existing Bayesian analysis software. A confidence band for the underlying relationship between true effect measure and baseline risk and a confidence interval for the value of the baseline risk measure for which there is no treatment effect are easily obtained by‐products of our approach. Copyright © 2000 John Wiley & Sons, Ltd.

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