Introduction: The objective of the study is to identify the relationship between knowledge and the practice of mental training skill during competitions which requires the athletes to gain control and boost their confidence. The study significantly states that knowledge and the practice of mental training skill during competitions are able to increase the athletes’ ability to control their state of mind and raise their confidence. Methods: 115 students of Malaysian Sport School, Pahang Malaysia participated in the study. One set of Likert scale questionnaire, which consists of five parts is used as the study instrument. The study instrument is based on the Competitive State Anxiety Inventory (CSAI2), Test of Performance Strategies (TOPS) and Mental Toughness Questionnaire (MTQ-48). Results: The research findings demonstrate an average level of knowledge on mental training skill with reference to the athletes’ control of their mental state and their confidence. However, the significant relationship between the knowledge and the practice of mental training skill with regards to the athletes’ mind control and confidence during competitions shows a highly positive and moderate relationship. Conclusion: Healthy mental training skill helps improve the athletes’ personality during competition. Apart from prioritising on the physical element in training applied in the competitions, the main elements which become the athletes’ source of mental strength, level of control and confidence are able to overcome the mental challenges.
Confidence

The previous articles of the Statistical Round in the Korean Journal of Anesthesiology posed a strong enquiry on the issue of null hypothesis significance testing (NHST). P values lie at the core of NHST and are used to classify all treatments into two groups: "has a significant effect" or "does not have a significant effect." NHST is frequently criticized for its misinterpretation of relationships and limitations in assessing practical importance. It has now provoked criticism for its limited use in merely separating treatments that "have a significant effect" from others that do not. Effect sizes and CIs expand the approach to statistical thinking. These attractive estimates facilitate authors and readers to discriminate between a multitude of treatment effects. Through this article, I have illustrated the concept and estimating principles of effect sizes and CIs.
Anesthesiology ; Confidence Intervals* ; Thinking

Confidence Intervals ; Probability ; Population

The null hypothesis significance test method is popular in biological and medical research. Many researchers have used this method for their research without exact knowledge, though it has both merits and shortcomings. Readers will know its shortcomings, as well as several complementary or alternative methods, as such the estimated effect size and the confidence interval.
Biostatistics ; Confidence Intervals ; Models, Statistical

OBJECTIVE: To compare different methods for calculating sample size based on confidence interval estimation for a single proportion with different event incidences and precisions. METHODS: We compared 7 methods, namely Wald, AgrestiCoull add z2, Agresti-Coull add 4, Wilson Score, Clopper-Pearson, Mid-p, and Jefferys, for confidence interval estimation for a single proportion. The sample size was calculated using the search method with different parameter settings (proportion of specified events and half width of the confidence interval [ω=0.05, 0.1]). With Monte Carlo simulation, the estimated sample size was used to simulate and compare the width of the confidence interval, the coverage of the confidence interval and the ratio of the noncoverage probability. RESULTS: For a high accuracy requirement (ω =0.05), the Mid-p method and Clopper Pearson method performed better when the incidence of events was low (P < 0.15). In other settings, the performance of the 7 methods did not differ significantly except for a poor symmetry of the Wald method. In the setting of ω=0.1 with a very low p (0.01-0.05), failure of iteration occurred with nearly all the methods except for the Clopper-Pearson method. CONCLUSION Different sample size determination methods based on confidence interval estimation should be selected for single proportions with different parameter settings.
Confidence Intervals ; Sample Size ; Computer Simulation ; Monte Carlo Method ; Probability

The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled datasets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.
Area Under Curve ; Confidence Intervals ; Phenothiazines ; Therapeutic Equivalency

BACKGROUND/AIMS: Complete endotracheal tube obstruction is a medical emergency, and partial occlusion causes increased breathing rates and failure to wean off mechanical ventilation. Partial occlusion may be underestimated due to the lack of proper detection methods. We tested whether the sound of an endotracheal tube could be used to detect an endotracheal tube obstruction using an in vitro model. METHODS: An endotracheal tube was connected to a ventilator on one end and a test lung on the other. Sounds were recorded with a microphone located inside the endotracheal tube via a connector. During mechanical ventilation, we changed the endotracheal tube internal diameter from 5.0 to 8.0 mm and different grades of obstruction at different sites were used along the tube. Sound energy was compared among the different conditions. RESULTS: The energy of endotracheal tube sounds was positively correlated with the internal diameter and negatively correlated with the degree of obstruction. The rate of decline in energy differed with obstruction location. When the obstruction was more distal, the rate of decline in endotracheal sound energy was more rapid. CONCLUSIONS: Changes in the sound of an endotracheal tube can be used to detect an obstruction. Further studies are needed for clinical application.
*Airway Obstruction ; Confidence Intervals ; Exhalation ; Humans ; *Intubation, Intratracheal ; Models, Theoretical ; Positive-Pressure Respiration ; Statistics as Topic

PURPOSE: To investigate the characteristics of congenital and acquired color vision defects with Seohan computerized hue test and SNU (Seoul National University) computerized color test and to help to discriminate between congenital and acquired color vision defect METHODS: from June 2003 to January 2004, patient with congenital and acquired color vision defect and visual acuities more than 20/30 underwent Seohan computerized hue and SNU computerized color tests. Their results were compared with each other. Quadrant analysis and RQ calculation were done. RESULTS: On Seohan computerized hue and SNU computerized color tests, congenital color vision defects showed mainly red-green color vision defects (p<0.01, paired t-test) while acquired color vision defects showed blue-yellow color vision defect(p<0.01, paired t-test). RQ had 95% sensitivity and 98% specificity with a standard of 1.5 by Seohan computerized hue test, and 96% sensitivity and 98% specificity with standard of 1.0 by SNU computerized color test, for the discrimination of congenital and acquired color vision defects (ROC curve, confidence interval 95%). CONCLUSIONS: Seohan computerized hue and SNU computerized color tests were effective to classify types of color vision defects and discriminate between the congenital and acquired color vision defects.
Color Vision Defects* ; Color Vision* ; Confidence Intervals ; Discrimination (Psychology) ; Humans ; Sensitivity and Specificity ; Visual Acuity

Comparative pharmacokinetic (PK) analysis is often carried out throughout the entire period of drug development, the common approach for the assessment of pharmacokinetics between different treatments requires that the individual PK parameters, which employs estimation of 90% confidence intervals for the ratio of average parameters, such as AUC and Cmax, these 90% confidence intervals then need to be compared with the pre-specified equivalent interval, and last we determine whether the two treatments are equivalent. Unfortunately in many clinical circumstances, some or even all of the individuals can only be sparsely sampled, making the individual evaluation difficult by the conventional non-compartmental analysis. In such cases, nonlinear mixed effect model (NONMEM) could be applied to analyze the sparse data. In this article, we simulated a sparsely sampling design trial based on the dense sampling data from a truly comparative PK study. The sparse data were analyzed with NONMEM method, and the original dense data were analyzed with non-compartment analysis. Although the trial design and analysis methods are different, the 90% confidence intervals for the ratio of PK parameters based on 1000 Bootstrap are very similar, indicated that the analysis based on NONMEM is a reliable method to treat with the sparse data in the comparative pharmacokinetic study.
Area Under Curve ; Confidence Intervals ; Humans ; Nonlinear Dynamics ; Pharmacokinetics ; Sampling Studies

Number needed to treat (NNT) is a simple and effective index for clinical therapeutic effect assessment worldwide accepted in recent years. By calculation of absolute risk reduction (ARR) of classified variables, it made the effect estimate reflect objectively the therapeutic effect of an intervention. However, clinical application of this index was introduced rarely in Chinese literature. With the examples from some published clinical reports, the calculation of NNT and its 95% confidence interval were demonstrated in this paper, and its application was illustrated by some relevant terms explanation and Meta-analysis methods introduction.
Confidence Intervals ; Humans ; Meta-Analysis as Topic ; Numbers Needed To Treat ; Outcome Assessment (Health Care) ; methods ; Treatment Outcome