However, allowance in design for alternative non-equal correlation structures can and should be made when necessary. Statistical Data Analysis follows the End of Trial Declaration station and precedes the Clinical Trial Summary Report station. The analysis using summary statistics makes no such assumption. The power is then found using the area under the curve of the normal distribution to the left of Zp: Zp Q N - 3 - Zreq where Zreq is the quantile of the. We use the Fisher Z-transformation: Zr ½ log (1+r)/ (1-r). The examples support the value of the compound symmetry assumption as a realistic simplification in quantitative planning of repeated measures trials. The power calculation is done using an approximation by the normal distribution. Several examples from clinical trials are presented, and broad practical recommendations are made.
Quantitative consideration is also given to practical issues in the design of repeated measures studies: the merits of having more than one pre-treatment measurement are demonstrated, and methods for determining sample sizes in repeated measures designs are provided. Analysis of covariance is the method of choice and its superiority over analysis of post-treatment means or analysis of mean changes is quantified, as regards both reduced variance and avoidance of bias, using a simple model for the covariance structure between time points. Quite often the data for each patient may be effectively summarized by a pre-treatment mean and a post-treatment mean.
This is done to ensure that, the knowledge of the results does not impact. This paper explores the use of simple summary statistics for analysing repeated measurements in randomized clinical trials with two treatments. The type of statistical analysis to be used for a particular trial is pre-specified.