Performance recovery from exercise: A review from a statistical point of view

Background. Research into methods for recovery from fatigue due to exercise is a popular topic among sport medicine, kinesiology and physical therapy. However, both the quantity and quality of studies and a clear solution of recovery are lacking. An analysis of the statistical methods in the existing literature of performance recovery can enhance the quality of research and provide some guidance for future studies. Methods: A literature review was performed using SCOPUS, SPORTDiscus, MEDLINE, CINAHL, Cochrane Library and Science Citation Index Expanded databases to extract the studies related to performance recovery from exercise of human beings. Original studies and their statistical analysis for recovery methods including Active Recovery, Cryotherapy/Contrast Therapy, Massage Therapy, Diet/Ergogenics, and Rehydration were examined. Results: The review produces a Research Design and Statistical Method Analysis Summary. Conclusion: Research design and statistical methods can be improved by using the guideline from the Research Design and Statistical Method Analysis Summary. This summary table lists the potential issues and suggested solutions, such as, sample size calculation, sports specific and research design issues consideration, population and measure markers selection, statistical methods for different analytical requirements, equality of variance and normality of data, post hoc analyses and effect size calculation.^

A descriptive and exploratory analysis of occupational injuries at a chemical manufacturing facility

A retrospective study of 1353 occupational injuries occurring at a chemical manufacturing facility in Houston, Texas from January, 1982 through May, 1988 was performed to investigate the etiology of the occupational injury process. Injury incidence rates were calculated for various sub-populations of workers to determine differences in the risk of injury for various groups. Linear modeling techniques were used to determine the association between certain collected independent variables and severity of an injury event. Finally, two sub-groups of the worker population, shiftworkers and injury recidivists, were examined. An injury recidivist as defined is any worker experiencing one or more injury per year. Overall, female shiftworkers evidenced the highest average injury incidence rate compared to all other worker groups analyzed. Although the female shiftworkers were younger and less experienced, the etiology of their increased risk of injury remains unclear, although the rigors of performing shiftwork itself or ergonomic factors are suspect. In general, females were injured more frequently than males, but they did not incur more severe injuries. For all workers, many injuries were caused by erroneous or foregone training, and risk taking behaviors. Injuries of these types are avoidable. The distribution of injuries by severity level was bimodal; either injuries were of minor or major severity with only a small number of cases falling in between. Of the variables collected, only the type of injury incurred and the worker's titlecode were statistically significantly associated with injury severity. Shiftworkers did not sustain more severe injuries than other worker groups. Injury to shiftworkers varied as a 24-hour pattern; the greatest number occurred between 1200-1230 hours, (p = 0.002) by Cosinor analysis. Recidivists made up 3.3% of the population (23 males and 10 females), yet suffered 17.8% of the injuries. Although past research suggests that injury recidivism is a random statistical event, analysis of the data by logistic regression implicates gender, area worked, age and job titlecode as being statistically significantly related to injury recidivism at this facility. ^

A computerized statistical framework for coalescent analysis

Coalescent theory represents the most significant progress in theoretical population genetics in the past three decades. The coalescent theory states that all genes or alleles in a given population are ultimately inherited from a single ancestor shared by all members of the population, known as the most recent common ancestor. It is now widely recognized as a cornerstone for rigorous statistical analyses of molecular data from population [1]. The scientists have developed a large number of coalescent models and methods[2,3,4,5,6], which are not only applied in coalescent analysis and process, but also in today’s population genetics and genome studies, even public health. The thesis aims at completing a statistical framework based on computers for coalescent analysis. This framework provides a large number of coalescent models and statistic methods to assist students and researchers in coalescent analysis, whose results are presented in various formats as texts, graphics and printed pages. In particular, it also supports to create new coalescent models and statistical methods. ^

A comparison of two statistical methods for analysis of day of the week effect on births from 2005-2007 in Harris County of Texas

The pattern of the births during the week has been reported by many studies. The births occurred in weekends are found consistently less then births occurred in weekdays. This study employed two statistical methods, two-way ANOVA and two-way Friedman's test to analyse the daily variations in amount of births of 222,735 births from 2005-2007 in Harris County, Texas. The two methods were compared on their assumptions, procedures and results. Both of the tests showed a significant result which indicated that the births through the week are not uniformly distributed. The result of multiple comparison demonstrated the births occurring on weekends were significantly different than the births occurring on weekdays with least amount on Sundays.^

Comparison of two statistical methods for rare variant association analysis

Studies have shown that rare genetic variants have stronger effects in predisposing common diseases, and several statistical methods have been developed for association studies involving rare variants. In order to better understand how these statistical methods perform, we seek to compare two recently developed rare variant statistical methods (VT and C-alpha) on 10,000 simulated re-sequencing data sets with disease status and the corresponding 10,000 simulated null data sets. The SLC1A1 gene has been suggested to be associated with diastolic blood pressure (DBP) in previous studies. In the current study, we applied VT and C-alpha methods to the empirical re-sequencing data for the SLC1A1 gene from 300 whites and 200 blacks. We found that VT method obtains higher power and performs better than C-alpha method with the simulated data we used. The type I errors were well-controlled for both methods. In addition, both VT and C-alpha methods suggested no statistical evidence for the association between the SLC1A1 gene and DBP. Overall, our findings provided an important comparison of the two statistical methods for future reference and provided preliminary and pioneer findings on the association between the SLC1A1 gene and blood pressure.^