Power multiple testing

In summary, GC adjusts for population stratification without the assumption or estimation of parameters such as the number of subpopulations involved in the study. It provides control of false-positive results caused by population structure as well as by multiple testing. One possible drawback of this method is that the correction of the test statistic is constant across the genome. As a result, GC may have less power in certain situations. 

Benjamini, Y., and Hochberg, Y. (1995) Controlling false discovery rate a practical and powerful approach to multiple testing. J R Statist Soc Br. 57, 289-300. 

Also, the analysis plan should identify the statistical methods that will be used and how hypotheses will be tested (e.g., a p value cutoff or a confidence interval for the difference that excludes 0). And the plan should prespecify whether interim analyses are planned, indicate how issues of multiple testing will be addressed, and predefine any subgroup analyses that will be conducted. Finally, the analysis plan should include the results of power and sample size calculations. 

Y. Benjamini and Y. Hochberg, Controlhng the false discovery rate A practical and powerful approach to multiple testing. StatSoc SerB (Methodological) 57 289-300... 

Common to both, linkage scans and candidate gene studies are the problems of multiple testing. When many hypotheses are tested at once, the power of a given study to detect true results decreases, while the risk for false-positive results increases. This is especially true for low-penetrance gene variants. Sample sizes which are large enough to balance this effect are usually difficult to obtain there-... 

In practical terms, this means that if we perform multiple tests and make multiple inferences, each one at a reasonably low error probability, the likelihood that some of these inferences will be erroneous could be appreciable. To correct for this, one must conduct each individual test at a decreased significance level, with the result that either the power of the tests will be reduced as well, or the sample size must be increased to accommodate the desired power. This could make the trial prohibitively expensive. Statisticians sometimes refer to the need to adjust the significance level so that the experimentwise error rate is controlled, as the statistical penalty for multiplicity. 

Distinguishing between irreversible and reversible multiple reactions and power-law test. 

Benjamini, Y. Hochberg, Y. (1995). Controlling the False Discovery Rate A practical and Powerful Approach to Multiple Testing. Journal of The Royal Statistical Society, Vol. Ser B 57, pp. 289-300. 

The Nyquist stability criterion is similar to the Bode criterion in that it determines closed-loop stability from the open-loop frequency response characteristics. Both criteria provide convenient measures of relative stability, the gain and phase margins, which will be introduced in Section J.4. As the name implies, the Nyquist stability criterion is based on the Nyquist plot for GqiXs), a polar plot of its frequency response characteristics (see Chapter 14). The Nyquist stability criterion does not have the same restrictions as the Bode stability criterion, because it is applicable to open-loop unstable systems and to systems with multiple values of co or cOg. The Nyquist stability criterion is the most powerful stability test that is available for linear systems described by transfer function models. 

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