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At least 127 records · Page 7Linked to original sources
PubMed2005

Weighing the results of differing 'low dose' studies of the mouse prostate by Nagel, Cagen, and Ashby: quantification of experimental power and statistical results.

Differing experimental findings with respect to "low dose" responses in the mouse prostate after in utero exposure have generated considerable controversy. An analysis of such controversies requires a broad strength and weight of the evidence approach. For example, a National Toxicology Program review panel acquired the raw data from nearly 50 studies and then statistically reanalyzed these data in a common and comparable approach. However, the statistical power of the various studies was not calculated and the quantitative p values were not reported in this reanalysis. Such calculations and values address vital strength- and weight-of-the-evidence questions: (1) how sensitive were the various studies to detect changes in prostate weight, particularly the negative replicate studies and (2) what were the p values; were negative studies robust or only marginal in their inability to find an effect? We first examined the statistical power of the studies to detect a positive effect on prostate weight. Preliminary calculations indicated that the two subsequent replicating studies were indeed more sensitive to changes in prostate weight in comparison to the original study, having reasonable power to detect an effect at only 50% of the response reported in the original study. Additional calculations were performed using the raw data available from one negative replicating study and the methods recommended by the statistics subpanel of the original review. This analysis used Dunnett's multiple comparison procedure for groups with p<0.05 to infer statistical significance, employed an analysis-of-covariance model with body weight as a covariate, and addressed litter as a nested random effect. The quantitated p values for this replicated study, comparing the two Bisphenol A treatment groups (2 and 20 microg/kg/day) to the control, were 0.821 and 0.972, respectively. This indicates this study was indeed robust in finding no treatment-related effect. Thus, the weight and strength of the evidence, based on sensitivity and quantitative p value, was that it is highly unlikely for this negative replicating study to have missed a true effect. In the future, we recommend a similar use of statistical power analysis for those designing experimental studies and for those conducting weight-of-the-evidence reviews, and we also recommend the clear quantitation and reporting of p values to support the review's interpretation and conclusions.

Algorithms↗
PubMed2005

Statistical power for analyses of changes in randomized controlled trials.

Randomized controlled trials (RCTs) are widely recommended as the most useful study design to generate reliable evidence and guidance to daily practices in medicine and dentistry. However, it is not well-known in dental research that different statistical methods of data analysis can yield substantial differences in study power. In this study, computer simulations are used to explore how using different univariate and multivariate statistical methods of analyzing change in continuous outcome variables affects study power, and the sample size required for RCTs. Results show that, in general, analysis of covariance (ANCOVA) yields greater power than other statistical methods in testing the superiority of one treatment over another, or in testing the equivalence between two treatments. Therefore, ANCOVA should be used in preference to change score or percentage change score to reduce type II error rates.

Analysis of Variance↗
PubMed2006

LRTae: improving statistical power for genetic association with case/control data when phenotype and/or genotype misclassification errors are present.

BACKGROUND: In the field of statistical genetics, phenotype and genotype misclassification errors can substantially reduce power to detect association with genetic case/control studies. Misclassification also can bias population frequency parameters such as genotype, haplotype, or multi-locus genotype frequencies. These problems are of particular concern in case/control designs because, short of repeated sampling, there is no way to detect misclassification errors. We developed a double-sampling procedure for case/control genetic association using a likelihood ratio test framework. Different approaches have been proposed to deal with misclassification errors. We have chosen the likelihood framework because of the ease with which misclassification probabilities may be incorporated into in the statistical framework and hypothesis testing. The statistic is called the Likelihood Ratio Test allowing for errors (LRTae) and is freely available via software download. RESULTS: We applied our procedure to 10,000 replicates of simulated case/control data in which we introduced phenotype misclassification errors. The phenotype considered is Ankylosing Spondylitis (AS). The LRTae method power was always greater than LRTstd power for the significance levels considered (5%, 1%, 0.1%, 0.01%). Power gains for the LRTae method over the LRTstd method increased as the significance level became more stringent. Multi-locus genotype frequency estimates using LRTae method were more accurate than estimates using LRTstd method. CONCLUSION: The LRTae method can be applied to single-locus genotypes, multi-locus genotypes, or multi-locus haplotypes in a case/control framework and can be more powerful to detect association in case/control studies when both genotype and/or phenotype errors are present. Furthermore, the LRTae method provides asymptotically unbiased estimates of case and control genotype frequencies, as well as estimates of phenotype and/or genotype misclassification rates.

Case-Control Studies↗
PubMed2004

How many pigs? Statistical power considerations in swine nutrition experiments.

Replication refers to the assignment of more than one experimental unit to the same treatment. Each replication of a treatment is an independent observation; thus, each replication involves a different experimental unit. In swine nutrition research, the experimental unit may be an individual animal, as in sow reproduction experiments, or a group of animals, as in growing-finishing pig experiments. In either case, calculation of the number of replicates needed to give an accurate and reliable outcome is an important step in deriving an experimental protocol. Although investigators often seem to choose replication arbitrarily on the basis of cost or availability of animals, housing considerations, convenience, or tradition, the question of how many pigs (i.e., how much replication is necessary) is a statistical one that has a statistical answer. A power analysis, performed while designing an experiment, will provide an investigator with an estimate of the number of replicates needed for an experiment of known power and sensitivity. This a priori, or prospective, power analysis ensures that an investigator does not waste time and resources carrying out an experiment that has little chance of finding a significant effect, if one exists. It also makes sure resources are not wasted by including more experimental units than are necessary to detect an effect. A retrospective, or a posteriori, power analysis may also be conducted. If no significant effects are found in an experiment, an investigator can assess the observed power of the experiment, or may determine the size of treatment effect that could have been detected using the standard deviation and number of replicates in the experiment. The latter may be useful in explaining results. However, the former may be misleading because a high P-value will invariably result in a low observed power, and little new information will be gained from the post hoc power analysis. In most cases, the time for making power calculations is before, not after, an experiment is conducted.

Animal Nutritional Physiological Phenomena↗
PubMed2006

Estimation efficiency and statistical power in arterial spin labeling fMRI.

Arterial spin labeling (ASL) data are typically differenced, sometimes after interpolation, as part of preprocessing before statistical analysis in fMRI. While this process can reduce the number of time points by half, it simplifies the subsequent signal and noise models (i.e., smoothed box-car predictors and white noise). In this paper, we argue that ASL data are best viewed in the same data analytic framework as BOLD fMRI data, in that all scans are modeled and colored noise is accommodated. The data are not differenced, but the control/label effect is implicitly built into the model. While the models using differenced data may seem easier to implement, we show that differencing models fit with ordinary least squares either produce biased estimates of the standard errors or suffer from a loss in efficiency. The main disadvantage to our approach is that non-white noise must be modeled in order to yield accurate standard errors, however, this is a standard problem that has been solved for BOLD data, and the very same software can be used to account for such autocorrelated noise.

Algorithms↗
PubMed2001

Comparison of statistical power between 2 * 2 allele frequency and allele positivity tables in case-control studies of complex disease genes.

In case-control studies of complex disease genes, allele frequencies or allele positivities at candidate loci or markers are compared between cases and controls. Although 2 x 2 contingency tables based on allele frequency and allele positivity are generally used to perform simple statistical tests (e.g. a comparison of two proportions and a chi2 test), little is known about the difference in power between the two tables. In this study, we investigated the number of subjects required to obtain a power of 1-beta with a significance level of alpha for the allele frequency and allele positivity tables. A large difference in the required number of subjects was found between the two tables. Allele positivity tables were suitable for the detection of susceptibility alleles showing a dominant mode of inheritance (MOI). On the other hand, allele frequency tables were suitable for the identification of susceptibility alleles showing a recessive MOI or a multiplicative MOI. In the case of an additive MOI, a suitable table was determined by combining the frequency of the susceptibility allele and the penetrance. These results imply that there are cases in which true association is detected based on one contingency table and is not detected based on another. A simulation analysis revealed that the type I error rate was not much inflated under the null hypothesis of no association, even when a statistical test was performed twice using both allele frequency and allele positivity tables. In contrast, under the alternative hypothesis, the loss of power was marked when a test was performed once using an unsuitable table. In conclusion, statistical tests should be performed using both tables, without adjustment of multiplicity, in case-control studies of complex disease genes when the study objective is exploratory.

Alleles↗