![]() However, there are many settings in which these simple solutions are unsatisfactory. 4 There are also several stand-alone packages that simplify the calculation of power, for example, PASS (NCSS Inc., Kaysville, Utah). 2, 3 Major statistical packages such as SAS (SAS Institute, Cary NC) contain routines for power calculation, and both functions and packages for power calculation are available for the free and open-source R environment. 1 Power for logistic regression can use iterative techniques or relatively simple formulae. For instance, the power for an ordinary least squares regression is described in basic textbooks. In many settings, the question of how to calculate power is reasonably well addressed by closed-form equations or easily tractable mathematical methods. It is often ethically unjustifiable to randomize more subjects than are required to yield sufficient power, and it is a waste of resources to invest time or money in studies that have little chance of rejecting the null or when power is far greater than necessary. “Statistical power” is defined as “the probability of rejecting the null hypothesis, given that some particular alternative hypothesis (“the alternative”) is true.” Power is particularly important from the perspectives of ethics and of allocating scarce resources. The method can easily be used when preliminary data are available, as is likely to be the case when research is performed in health delivery systems or other settings where electronic medical records can be obtained. We also demonstrate power calculations for correlated censored survival outcomes in a cluster-randomized trial setting, for which we are unaware of an analytic alternative. We show with a simulation study that bootstrap power calculation can replicate analytic power in cases where analytic power can be accurately calculated. ![]() We are not aware of bootstrap power calculation being previously proposed or explored for cluster-randomized trials, but it can also be applied for other study designs. In contrast, bootstrap power calculation requires initial data that resemble data that are to be used in the planned study. So, for example, for cluster-randomized trials, power calculations need not depend on intracluster correlation coefficient estimates from outside studies. Notably, it is simple to achieve great fidelity to the proposed data analysis method and there is no requirement for parameter estimates, or estimates of their variability, from outside settings. This method of calculation has several important strengths. It provides a relatively simple solution to power calculation that is likely to be more accurate than analytic solutions or simulation-based calculations, in the sense that the bootstrap approach does not rely on the assumptions inherent in analytic calculations. Bootstrap power calculation is a natural application of resampling methods.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |