PubMed Health⌕ Search

PubMed · 16033877

Performance of floating absolute risks.

Abstract

A recent investigation of hormone replacement therapy and breast cancer risk used a method called "floating absolute risks" (FARs) to compute confidence intervals for relative hazards. This method has been used in other medical studies and has received controversy. This controversy stems from the correct implementation of this method. However, there has been no direct comparison of the FAR method, as it is sometimes incorrectly applied and reported, with the conventional approach for computing confidence intervals from proportional hazards regression. In this paper, the author reports simulation results comparing these two methods and demonstrates that the FAR method, when applied incorrectly, can produce confidence intervals that are substantially too narrow.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Patrick G Arbogast. 2005-07-20. Performance of floating absolute risks.. https://doi.org/10.1093/aje%2Fkwi221

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Inferences on standardized mean difference: the generalized variable approach.

The standardized mean difference has been widely used as the most common index of effect magnitude in many applied fields. In this paper, we propose a novel approach using the concept of generalized variable for the confidence interval estimation and hypothesis testing of standardized mean difference. Furthermore, we extend this approach to compare standardized mean differences between two studies or between two strata. Simulation results demonstrate that the proposed approach can provide confidence intervals with excellent coverage properties and can perform hypothesis testing with satisfactory type-I error control.

Confidence Intervals↗

Interval estimation for rank correlation coefficients based on the probit transformation with extension to measurement error correction of correlated ranked data.

The Spearman (rho(s)) and Kendall (tau) rank correlation coefficient are routinely used as measures of association between non-normally distributed random variables. However, confidence limits for rho(s) are only available under the assumption of bivariate normality and for tau under the assumption of asymptotic normality of tau. In this paper, we introduce another approach for obtaining confidence limits for rho(s) or tau based on the arcsin transformation of sample probit score correlations. This approach is shown to be applicable for an arbitrary bivariate distribution. The arcsin-based estimators for rho(s) and tau (denoted by rho(s,a), tau(a)) are shown to have asymptotic relative efficiency (ARE) of 9/pi2 compared with the usual estimators rho(s) and tau when rho(s) and tau are, respectively, 0. In some nutritional applications, the Spearman rank correlation between nutrient intake as assessed by a reference instrument versus nutrient intake as assessed by a surrogate instrument is used as a measure of validity of the surrogate instrument. However, if only a single replicate (or a few replicates) are available for the reference instrument, then the estimated Spearman rank correlation will be downwardly biased due to measurement error. In this paper, we use the probit transformation as a tool for specifying an ANOVA-type model for replicate ranked data resulting in a point and interval estimate of a measurement error corrected rank correlation. This extends previous work by Rosner and Willett for obtaining point and interval estimates of measurement error corrected Pearson correlations.

Confidence Intervals↗

New confidence intervals for the difference between two sensitivities at a fixed level of specificity.

For two continuous-scale diagnostic tests, it is of interest to compare their sensitivities at a predetermined level of specificity. In this paper, we propose three new intervals for the difference between two sensitivities at a fixed level of specificity. These intervals are easy to compute. We also conduct simulation studies to compare the relative performance of the new intervals with the existing normal- approximation-based interval proposed by Wieand et al. Our simulation results show that the newly proposed intervals perform better than the existing normal-approximation-based interval in terms of coverage accuracy and interval length.

Confidence Intervals↗