Preface to First Edition - lib

SUMMARY 77R> mh_test(anomalies)Asymptotic Marginal-Homogeneity Testdata: response bygroups (MD, RA)stratified by blockchi-squared = 21.2266, df = 3, p-value = 9.446e-05The p-value indicates a deviation from the null hypothesis. However, the levelsof the response are not treated as ordered. Similar to the analysis of thegastrointestinal damage data above, we can take this information into accountby the definition of an appropriate score. Here, the number of anomalies is anatural choice:R> mh_test(anomalies, scores = list(c(0, 1, 2, 3)))Asymptotic Marginal-Homogeneity Test for Ordered Datadata: response (ordered) bygroups (MD, RA)stratified by blockchi-squared = 21.0199, df = 1, p-value = 4.545e-06In our case, both versions coincide and one can conclude that the assessment ofthe number of anomalies differs between the medical doctor and the researchassistant.4.4 SummaryThe analysis of randomised experiments, for example the analysis of randomisedclinical trials such as the Misoprostol trial presented in this chapter,requires the application of conditional inferences procedures. In such experiments,the observations might not have been sampled from well-defined populationsbut are assigned to treatment groups, say, by a random procedurewhich is reiterated when randomisation tests are applied.ExercisesEx. 4.1 Although in the past Fisher’s test has been largely applied to sparse2 × 2 tables, it can also be applied to larger tables, especially when thereis concern about small values in some cells. Using the data displayed inTable 4.8 (taken from Mehta and Patel, 2003) which gives the distributionof the oral lesion site found in house-to-house surveys in three geographicregions of rural India, find the p-value from Fisher’s test and the correspondingp-value from applying the usual chi-square test to the data. What areyour conclusions?© 2010 by Taylor and Francis Group, LLC