Adjusted analysis multiplicity

Subgroups in a meta-analysis can be of direct interest or useful in exploring the consistency of findings. Adjustments for multiplicity from examining subgroups in meta-analysis are usually not feasible because of the limited information from the available data. Patterns and consistency of results may... 

We used t-tests with a Bonferroni adjustment for multiple comparisons when appropriate. When assumptions for parametric analysis were not met, we used Kruskal-Wallis (H) analysis of variance on rank, followed by planned pairwise comparisons of interest using Dunn s (Q) method. 

Automatic setup for repetitive signal analysis. For complex multichannel configurations that are used often, front-panelstorage/recaUfeatures can save dozens of manual selections and adjustments. When multiple memory locations are available, multiple front-panel setups can be stored to save even more time. 

The criterion for a negative TQT study specifically is that the UB of the 90 % Cl of the AAQTc estimate is below 10 ms, which applies to all post-dosing timepoints. Since the analysis uses a non-inferiority approach, there is no need for adjustment for multiplicity in this part of the analysis (Stockbridge et al. 2012 Tsong et al. 2008, 2010 Zhang and Machado 2008). Figure 1 shows two examples of clearly negative TQT studies (Tyl et al. 2012 Iwamoto et al. 2008). 

A study of 398 male and 133 female civil servants in London, England, measured blood pressure, PbB, and serum creatinine concentration the study found no correlation between blood pressure and PbB after adjustment for significant covariates, including sex, age, cigarette smoking, alcohol intake, and body mass index in a stepwise multiple regression analysis (Staessen et al. 1990). 

Because PB-PK models are based on physiological and anatomical measurements and all mammals are inherently similar, they provide a rational basis for relating data obtained from animals to humans. Estimates of predicted disposition patterns for test substances in humans may be obtained by adjusting biochemical parameters in models validated for animals adjustments are based on experimental results of animal and human in vitro tests and by substituting appropriate human tissue sizes and blood flows. Development of these models requires special software capable of simultaneously solving multiple (often very complex) differential equations, some of which were mentioned in this chapter. Several detailed descriptions of data analysis have been reported. 

This method can be considered a calibration transfer method that involves a simple instrument-specific postprocessing of the calibration model outputs [108,113]. It requires the analysis of a subset of the calibration standards on the master and all of the slave instmments. A multivariate calibration model built using the data from the complete calibration set obtained from the master instrument is then applied to the data of the subset of samples obtained on the slave instruments. Optimal multiplicative and offset adjustments for each instrument are then calculated using linear regression of the predicted y values obtained from the slave instrument spectra versus the known y values. 

Is one specific treatment comparison important . .. any aspects of multiplicity. .. should be identified in the protocol adjustment should always be considered.. .. an explanation of why adjustment is not thought necessary should be set out in the analysis plan. (ICH E9, Section 5.6)... 

The second approach leaves the data fixed, and introduces a random element into the selection of the splitting variable. So if, for example, predictors x29, x47, and x82 were the only significant splitters and had multiplicity-adjusted p values of say 2 x 10-7, 5 x 10-8, and 3 x 10-3, the conventional greedy algorithm would pick x47 as the splitting variable as it was the most significant. The RRP procedure would pick one of these three at random. Repeating the analysis with fresh random choices would then lead to a forest of trees different random choices will create different trees. 

When multiplicity is present, the usual frequentist approach to the analysis of clinical trial data may necessitate an adjustment to the type I error. Multiplicity may arise for, example. 

Using several different statistical methods, for example, an unpaired t-test, an analysis adjusted for centre effects, ANCOVA adjusting for centre and including baseline risk as a covariate, etc., and choosing that method which produces the smallest p-value is another form of multiplicity and is inappropriate. 

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