New Statistic (new + statistic)

Distribution by Scientific Domains

Selected Abstracts

Environmental power analysis , a new perspective

David R. Fox
Abstract Power analysis and sample-size determination are related tools that have recently gained popularity in the environmental sciences. Their indiscriminate application, however, can lead to wildly misleading results. This is particularly true in environmental monitoring and assessment, where the quality and nature of data is such that the implicit assumptions underpinning power and sample-size calculations are difficult to justify. When the assumptions are reasonably met these statistical techniques provide researchers with an important capability for the allocation of scarce and expensive resources to detect putative impact or change. Conventional analyses are predicated on a general linear model and normal distribution theory with statistical tests of environmental impact couched in terms of changes in a population mean. While these are ,optimal' statistical tests (uniformly most powerful), they nevertheless pose considerable practical difficulties for the researcher. Compounding this difficulty is the subsequent analysis of the data and the impost of a decision framework that commences with an assumption of ,no effect'. This assumption is only discarded when the sample data indicate demonstrable evidence to the contrary. The alternative (,green') view is that any anthropogenic activity has an impact on the environment and therefore a more realistic initial position is to assume that the environment is already impacted. In this article we examine these issues and provide a re-formulation of conventional mean-based hypotheses in terms of population percentiles. Prior information or belief concerning the probability of exceeding a criterion is incorporated into the power analysis using a Bayesian approach. Finally, a new statistic is introduced which attempts to balance the overall power regardless of the decision framework adopted. Copyright 2001 John Wiley & Sons, Ltd. [source]

An optimal dose-effect mode trend test for SNP genotype tables

Ryo Yamada
Abstract The genome-wide association studies have improved our understanding of the genetic basis of many complex traits. Two-by-three contingency tables are tested in these studies. The trend test for the additive mode is most often used, which is the test of 1 degree of freedom (df=1) and other tests, such as the genotype test (,2 (df=2)) and the ,2 (df=1) tests for the dominant and recessive modes are also used to increase the power for markers in the non-additive modes. However, any one of them or combination of them is not perfect. We describe the relations among the ,2 (df=2) test and ,2 (df=1) tests for the dominant and recessive modes and the trend test for the additive mode and propose a new statistic based on their relations that tests the hypothesis that the disease-susceptible allele has a dose-effect somewhere between the recessive and dominant modes, which corresponds to the optimal dose-effect for the observed data. Genet. Epidemiol. 2008. 2008 Wiley-Liss, Inc. [source]

Discovery and transmission of functional QTL in the pedigree of an elite soybean cultivar Suinong14

PLANT BREEDING, Issue 3 2010
J. Qin
With 3 figures and 5 tables Abstract In this study, we extended in silico mapping for single trait to analyse data from multiple environments by calculating intraclass correlations and to mapping pleiotropic QTL for multiple traits by defining new statistic to measure the correlation between multiple traits and the marker. Data sets include phenotypes of eight agronomic traits obtained from six different ecologic environments and years, and genotypic information from 477 polymorphic markers on 14 ancestral lines in the pedigree of ,Suinong14'. With in silico mapping, a total of 39 markers distributed on 14 linkage groups are detected as QTL responsible for eight agronomic traits and 10 QTL are identified as having pleiotropic effects. Tracing transmission of functional QTL in the pedigree indicated that certain QTL, such as Sat_036 on linkage group D1a, Satt182 on linkage group L, and Satt726 on linkage group B2 may be responsible for the contribution of exotic germplasm to the improved cultivars. [source]

Benchmarks and control charts for surgical site infections

T. L. Gustafson
Background Although benchmarks and control charts are basic quality improvement tools, few surgeons use them to monitor surgical site infection (SSI). Obstacles to widespread acceptance include: (1) small denominators, (2) complexities of adjusting for patient risk and (3) scepticism about their true purpose (cost cutting, surgical privilege determination or improving outcomes). Methods The application of benchmark charts (using US national SSI rates as limits) and control charts (using facility rates as limits) was studied in 51 hospitals submitting data to the AICE National Database Initiative. SSI rates were risk adjusted by calculating a new statistic, the standardized infection ratio (SIR), based on the risk index suggested by the Centers for Disease Control National Nosocomial Infection Surveillance Study. Fourteen different types of control chart were examined and 115 suspiciously high or low monthly rates were flagged. Participating hospital epidemiologists investigated and classified each flag as ,a real problem' (potentially preventable) or ,not a problem' (beyond the control of personnel at this facility). Results None of the standard, widely recommended, control charts studied showed practical value for identifying either preventable rate increases or outbreaks (clusters due to a single organism). On the other hand, several types of risk-adjusted control chart based on the SIR correctly identified most true opportunities for improvement. Sensitivity, specificity and receiver,operator characteristic (ROC) analysis revealed that the XmR chart of monthly SIRs would be useful in hospitals with smaller surgical volumes (ROC area = 0732, P = 0001). For larger hospitals, the most sensitive and robust SIR chart for real-time monitoring of surgical infections was the mXmR chart (ROC area = 0753, P = 00005). 2000 British Journal of Surgery Society Ltd [source]