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|Title:||Wilson confidence intervals for the two-sample log-odds-ratio in stratified 2 × 2 contingency tables|
|Source:||Brown, B.M., Suesse, T., Yap, V.B. (2012). Wilson confidence intervals for the two-sample log-odds-ratio in stratified 2 × 2 contingency tables. Communications in Statistics - Theory and Methods 41 (18) : 3355-3370. ScholarBank@NUS Repository. https://doi.org/10.1080/03610926.2011.560779|
|Abstract:||Large-sample Wilson-type confidence intervals (CIs) are derived for a parameter of interest in many clinical trials situations: the log-odds-ratio, in a two-sample experiment comparing binomial success proportions, say between cases and controls. The methods cover several scenarios: (i) results embedded in a single 2 × 2 contingency table; (ii) a series of K 2 × 2 tables with common parameter; or (iii) K tables, where the parameter may change across tables under the influence of a covariate. The calculations of the Wilson CI require only simple numerical assistance, and for example are easily carried out using Excel. The main competitor, the exact CI, has two disadvantages: It requires burdensome search algorithms for the multi-table case and results in strong over-coverage associated with long confidence intervals. All the application cases are illustrated through a wellknown example. A simulation study then investigates how the Wilson CI performs among several competing methods. The Wilson interval is shortest, except for very large odds ratios, while maintaining coverage similar to Wald-type intervals. An alternative to the Wald CI is the Agresti-Coull CI, calculated from the Wilson and Wald CIs, which has same length as the Wald CI but improved coverage. Copyright © Taylor & Francis Group, LLC.|
|Source Title:||Communications in Statistics - Theory and Methods|
|Appears in Collections:||Staff Publications|
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