PubMed · 6383510
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I K Fry. 1984. Who needs high technology?. https://doi.org/10.1259/0007-1285-57-681-765
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Sample size calculations are given for comparing two groups of subjects, typically referring to active and non-active intervention groups, on an ordinal outcome in experiments where the subjects are measured before and after intervention. These calculations apply to log-odds models with random intercepts, treatment, time and treatment-by-time interaction terms, the latter being the term of interest. The assumed forms of the odds ratios are flexible, allowing for proportional odds, adjacent categories, or other conditional models for ordinal responses. Simulations studies show that, for given sample sizes, the nominal and actual powers of the proposed test are similar.
We consider the problem of constructing a confidence interval for the intraclass correlation coefficient in an interrater reliability study when both raters and subjects are random effects in a two-way random effects model. A simulation study is conducted to investigate and compare the interval coverage per cents of three methods of approximation: (i) Satterthwaite's two moments, the standard approach; (ii) modified three moments, a newer approach; and (iii) modified large sample, the newest approach for this particular problem. One-sided lower and two-sided bounds are examined. For the two-moment approach, coverage of confidence intervals can be understated for not only one-sided bounds but also two-sided bounds. Coverage of confidence intervals for the modified large-sample approach is either correct or conservative and provides narrower two-sided widths than the modified three-moment approach, which can understate coverage on one-sided and two-sided bounds for two or three raters. The competing methods are illustrated with data from a reliability study of four raters evaluating the number of decayed, missing, and filled surfaces in the permanent teeth of ten subjects. Overall, the modified large-sample approach provides the most satisfactory performance of the three methods.
In clinical trials, one of the main questions that is being asked is how many additional observations, if any, are needed beyond those originally planned. In a two-treatment double-blind clinical experiment, one is interested in testing the null hypothesis of equality of the means against one-sided alternative when the common variance sigma2 is unknown. We wish to determine the required total sample size when the error probabilities alpha and beta are specified at a predetermined alternative. Shih provided a two-stage procedure which is an extension of Stein's one-sample procedure, assuming normal response. He estimates sigma2 by the method of maximum likelihood via the EM algorithm and carries out a simulation study in order to evaluate the effective level of significance and the power. The author proposed a closed-form estimator for sigma2 and showed analytically that the difference between the effective and nominal levels of significance is negligible and that the power exceeds 1-beta when the initial sample size is large. Here we consider responses from arbitrary distributions in which the mean and the variance are not functionally related and show that when the initial sample size is large, the conclusions drawn previously by the author still hold. The effective coverage probability of a fixed-width interval is also evaluated. Proofs of certain assertions are deferred to the Appendix.