Abstract
Abstract A radial plot is a graphical display for comparing estimates that have differing precisions. It is a scatter plot of standardized estimates against reciprocals of standard errors, possibly with respect to a transformed scale, designed so that the original estimates can be compared and interpreted. The estimates may be means, regression coefficients, proportions, rates, odds ratios, random effects, or indeed any parameter estimates that merit comparison between individuals or groups. This article illustrates some uses of radial plots by discussing a variety of data examples taken from the literature. The statistical application areas include interlaboratory trials, point process event rates, empirical Bayes estimation, modeling of counting data, analysis of overdispersed and underdispersed binomial and Poisson data, mixture modeling and meta-analysis.
Keywords
Affiliated Institutions
Related Publications
Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis
Statistical heterogeneity and small-study effects are 2 major issues affecting the validity of meta-analysis. In this article, we introduce the concept of a limit meta-analysis,...
Explaining heterogeneity in meta-analysis: a comparison of methods
Exploring the possible reasons for heterogeneity between studies is an important aspect of conducting a meta-analysis. This paper compares a number of methods which can be used ...
A robust <i>P</i>‐value for treatment effect in meta‐analysis with publication bias
Abstract Publication bias is a major and intractable problem in meta‐analysis. There have been several attempts in the literature to adapt methods to allow for such bias, but th...
Analysis of Longitudinal Data
1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7....
Inference from Iterative Simulation Using Multiple Sequences
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, ho...
Publication Info
- Year
- 1994
- Type
- article
- Volume
- 89
- Issue
- 428
- Pages
- 1232-1242
- Citations
- 75
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1080/01621459.1994.10476864