![statistical calculations for trials statistical calculations for trials](https://reliawiki.org/images/2/2e/Expected_Failure_Plot_Median_Rank.png)
![statistical calculations for trials statistical calculations for trials](https://i.ytimg.com/vi/sCbslydajOM/maxresdefault.jpg)
Several clinical trial examples such as dose-response. These design effects will enable appropriate sample size calculations to be performed for future randomised trials including both independent and paired data. The application of statistical methods depends on study designs, data type and investigation purpose. The derived design effects are validated through simulation and example calculations are presented to illustrate their use in sample size planning. The design effect is shown to depend on the intracluster correlation coefficient, proportion of observations belonging to a pair, working correlation structure, type of outcome and method of randomisation. In a systematic review of recent reports (20132018) of standard crossover BE trials 7 it was found that out of 48 reports that described the details of the sample size calculation, 12 (25) trials planned with an expected T/R-ratio of 1.0, hence they assumed the maximum power (e.g. Continuous and binary outcomes are considered, along with three different methods of randomisation: cluster randomisation, individual randomisation and randomisation to opposite treatment groups. We derive design effects algebraically assuming clustering because of paired data will be taken into account in the analysis using generalised estimating equations with either an independence or exchangeable working correlation structure. Randomised trials including a mixture of independent and paired data arise in many areas of health research, yet methods for determining the sample size for such trials are lacking. A method of estimation based on a power calculation based on the width of the confidence interval is described here and shown to have additional advantages of simplicity and transparency, enabling a more informed debate about the proposed size of trials.