Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
John Wiley & Sons, Apr 15, 2008 - Mathematics - 282 pages
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence.
The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.
This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.
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ˆκ 2008 John Wiley Applications approximately normal arcsin assumed asymptotic binomial Calibrating and Combining calibration scale Chapter Cochran’s Q combined evidence Combining Statistical Evidence confidence interval coverage probabilities defined degrees of freedom effect size empirical coverage Equation estimated Evidence Elena Kulinskaya evidence for heterogeneity example expected evidence Figure given Guide to Calibrating Key Inferential Function measure of evidence Meta Analysis methods nominal 95 noncentral chi-squared distribution noncentral t-distribution noncentrality parameter normal distribution null hypothesis observations obtain one-sided alternative p-value plot Poisson publication bias quantile random effects model Regression relative risk sample mean sample sizes Section standard deviation standard error standardized effect Statistical Evidence Elena Staudte Stephan Morgenthaler t-statistic t-test Table test statistic Transformation to evidence transformed effects two-sample unknown values variable variance stabilizing transformation weights Welch