More Robust Standard Error and Confidence Interval for SEM Parameters Given Incorrect Model and Nonnormal Data

Objective: Study was aimed to see the effects of hypothyroidism on GFR as a renal function. Material and methods: Total of Fifty-eight patients were included in the study. Out of those forty-eight patients were female and the rest were male. Out of fifty eight patients, fifty three patients were of thyroid cancer in which hypothyroidism was due to discontinuation of thyroxine before the administration of radioactive iodine for Differentiated thyroid cancer.Moreover, remaining five patients were post radioactive iodine treatment (for hyperthyroidism) hypothyroid. All of the patients were above eighteen years of age with TSH value > 30µIU/ml. Pregnant and lactating females were excluded.Renal function tests (urea/creatinine, creatinine clearance) and serum electrolytes followed by Tc-99m-DTPA renal scan for GFR assessment (GATES’ method) were carried out in all subjects twice during the study, One study during hypothyroid state (TSH > 30 µIU/ml) and other during euthyroid state (TSH between 0.4 to 4µ IU/ml). The results of Student’s t-test showed significant difference in renal functions (Urea, creatinine, creatinine clearance, GFR values) in euthyroid state and hypothyroid state (p-value <0.05). RESULTS: In case of creatinine the paired t test reveal the mean 1.014±0.428, with standard error of 0.669 within 95% confidence interval, for creatinine clearance 80.11±14.12 with standard error of 1.94 within 95% confidence intervals, for urea the mean 28±12.13 with standard error of 1.607 within 95% confidence intervals and for GFR for individual kidney is 38.056±8.56 with standard error of 1.3717 within 95% confidence interval. There was no difference in the outcome of the 2 groups. Conclusion: Hypothyroidism impairs renal function to a significant level and hence needs to be prevented and corrected as early as possible.

Download Full-text

The validity and reliability of a novel mobile app to measure agility performance in the physically active youth population

European Journal of Human Movement ◽

10.21134/eurjhm.2020.45.9 ◽

2020 ◽

Vol 45 ◽

pp. 85-92

Author(s):

Juan A. Escobar-Alvarez ◽

Rocio Carrasco ◽

Pedro R. Olivares ◽

Sebastián Feu ◽

Robinson Ramírez-Velez ◽

...

Keyword(s):

Correlation Analysis ◽

Confidence Interval ◽

Standard Error ◽

Low Cost ◽

Intraclass Correlation ◽

Mobile App ◽

Validity And Reliability ◽

Physically Active ◽

Youth Population ◽

Acceptable Reliability

Agility is a key component of physical fitness in adolescents. However, the measurement of this variable is usually complex, requiring high cost instruments and complex software. To test the validity and reliability of a novel iPhone app (Lap Tracker Auto-timer) to measure agility performance among adolescents. Twenty-four physically active adolescents (15.7 ± 2.3 years old) participated in two testing sessions (separated by 7 days). They performed three 4 x 10 m agility test trials measured by Photocell or the iPhone app. The correlation analysis revealed high validity (r = .92; 95% confidence interval [CI] = .88 – .95), with a standard error of the estimate of 0.56 s (p < 0.001). The coefficient of variation (CV; 0.09) and intraclass correlation coefficient (ICC; .93; 95% CI = .85 – .97) showed an acceptable reliability. This study demonstrated that the iPhone App Lap Tracker Auto-timer could be a valid, reliable and low-cost tool to evaluate agility performance in adolescents. However, more studies are required to guarantee the utility of this app.

Download Full-text

Types of Child Abuse: Standard Error of a Percentage and Confidence Interval

Interpreting Basic Statistics ◽

10.4324/9781315266404-58 ◽

2016 ◽

pp. 169-171

Keyword(s):

Child Abuse ◽

Confidence Interval ◽

Standard Error

Download Full-text

Simplified large African carnivore density estimators from track indices

PeerJ ◽

10.7717/peerj.2662 ◽

2016 ◽

Vol 4 ◽

pp. e2662 ◽

Cited By ~ 10

Author(s):

Christiaan W. Winterbach ◽

Sam M. Ferreira ◽

Paul J. Funston ◽

Michael J. Somers

Keyword(s):

Linear Regression ◽

Confidence Interval ◽

Population Size ◽

Linear Model ◽

Standard Error ◽

Linear Models ◽

Information Criteria ◽

Large Carnivores ◽

Simple Linear Regression ◽

Akaike Information Criteria

BackgroundThe range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices.MethodsWe did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear modely = αx + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models.ResultsThe Lion on Clay and Low Density on Sand models with intercept were not significant (P > 0.05). The other four models with intercept and the six models thorough origin were all significant (P < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support.DiscussionOur results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formulaobserved track density = 3.26 × carnivore densitycan be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km2or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Download Full-text