000221748 001__ 221748
000221748 005__ 20181203024348.0
000221748 0247_ $$2doi$$a10.1002/bimj.201500115
000221748 022__ $$a0323-3847
000221748 02470 $$2ISI$$a000379929300010
000221748 037__ $$aARTICLE
000221748 245__ $$aInference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies
000221748 260__ $$bWiley-Blackwell$$c2016$$aHoboken
000221748 269__ $$a2016
000221748 300__ $$a19
000221748 336__ $$aJournal Articles
000221748 520__ $$aWe study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log-odds and arcsine transformations of the estimated probability (p) over cap, both for single-group studies and in combining results from several groups or studies in meta-analysis. Our simulations confirm that these biases are linear in rho, for small values of rho, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta-analysis and result in abysmal coverage of the combined effect for large K. We also propose bias-correction for the arcsine transformation. Our simulations demonstrate that this bias-correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta-analyses of prevalence.
000221748 6531_ $$aIntracluster correlation
000221748 6531_ $$aMeta-analysis
000221748 6531_ $$aOverdispersion
000221748 6531_ $$aRandom effects
000221748 6531_ $$aTransformation bias
000221748 700__ $$uUniv East Anglia, Sch Comp Sci, Norwich NR4 7TJ, Norfolk, England$$aBakbergenuly, Ilyas
000221748 700__ $$uUniv East Anglia, Sch Comp Sci, Norwich NR4 7TJ, Norfolk, England$$aKulinskaya, Elena
000221748 700__ $$g105911$$aMorgenthaler, Stephan$$0241889
000221748 773__ $$j58$$tBiometrical Journal$$k4$$q896-914
000221748 909C0 $$xU10127$$0252209$$pSTAP
000221748 909CO $$pSB$$particle$$ooai:infoscience.tind.io:221748
000221748 917Z8 $$x105911
000221748 937__ $$aEPFL-ARTICLE-221748
000221748 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000221748 980__ $$aARTICLE