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Survival Analysis
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
A common multiple comparison method is the Bonferroni method, which uses a significance level of for each test. To make the Bonferroni correction for each test made, simply multiply the p-value, p, obtained for each test by N, to give the adjusted p-value, for the test. Then compare this with the significance level, α. The Bonferroni correction is rather conservative and only cautiously rejects a null-hypothesis.
Working with continuous outcome variables
Published in Ewen Harrison, Pius Riinu, R for Health Data Science, 2020
The adjusted values can then be compared to a threshold of 0.05, as is the case above. The Bonferroni method is particularly conservative, meaning that type II errors may occur (failure to identify true differences, or false negatives) in favour or minimising type I errors (false positives).
THE ANALYSIS OF ANIMAL CARCINOGENICITY EXPERIMENTS
Published in Richard G. Cornell, Statistical Methods for Cancer Studies, 2020
Richard G. Cornell, Robert A. Wolfe, William J. Butler
significant result would be 1-(1-0.05) =0 . 64. Thus, even if the significance level for each site were 0.05, the overall probability of rejection of a true null hypothesis of no carcinogenic treatment effect would be much higher. To avoid this problem, a lower significance level can be utilized for each site and then the Bonferroni method would lead to an acceptably low experiment-wide significance level for all sites combined. This method is based on an inequality which shows that an overall experiment-wide significance level of a or less can be attained by utilizing significance levels in separate comparisons, in this case for separate sites, which sum to a. For instance, for five separate sites, a significance level of a/5 could be used for each site. For further discussion of the use of the Bonferroni inequality in this context (see Gart et cli. (1979)).
Trajectories of receptive and expressive vocabulary in Mandarin speaking children under 4 years of age fitted with cochlear implants: a 12-month longitudinal study
Published in International Journal of Audiology, 2023
Gang Li, Fei Zhao, Yong Tao, Lin Zhang, Yun Zheng
The SSF-MCDI-W&G scores were analysed by comparing the children with CIs to children with normal hearing according to the methods used by Li et al. (2014). Several statistical analyses were conducted, i.e. general characteristics comparisons of children in three age groups were analysed using the Chi-squared test for categorical variables, the Mann–Whitney U test, and Kruskal–Wallis test for non-normally distributed variables. The first and second hypotheses were tested using the Mann–Whitney U test and Kruskal–Wallis tests to compare the receptive or expressive vocabulary scores at the same interval in the three age groups. A General Linear Model (GLM) Univariate analysis was performed, to compare the trajectories of receptive or expressive vocabulary. The third hypothesis was verified with the Mann–Whitney U test to compare the receptive and expressive vocabulary scores at post-implantation assessment points. Significance was set at the conventional 0.05 level and corrected with the Bonferroni method if necessary. R2 was used to predict the accuracy of the linear regression function, and a good prediction set at equal to or more than 0.800.
Natural progress history of asymptomatic bacterial vaginosis in Chinese Han women and associated risk factors
Published in Postgraduate Medicine, 2022
Rui Zhang, Zhaohui Liu, Yan Zhang, Dai Zhang, Qinping Liao
Data were entered and stored in Microsoft Excel and were analyzed by using SPSS (version 26.0). Proportions were compared using x2 and Fisher’s exact tests. Univariate and multivariate logistic regression models were used to estimate odds ratios (ORs) and the associated 95% confidence intervals (95% CIs). Multiplicative interactions were evaluated by including an interaction term in the logistic regression model, and statistical significance was determined using the likelihood-ratio test. Additive interactions were evaluated with the use of a synergy index. In all analyses, the overall significance level for each type of test was set at 0.05, and the Bonferroni method was used to adjust for multiple testing. The Auto-regressive Integrated Moving Average (ARIMA) model was used to predict the outcomes of subjects in the following months, ARIMA (p, d, q), p is the autoregressive terms, d is the number of nonseasonal differences, q is the number of lagged forecast errors.
Association between the Extent of Peripheral Blood DNA Methylation of HIF3A and Accumulation of Adiposity in community-dwelling Women: The Yakumo Study
Published in Endocrine Research, 2022
Genki Mizuno, Hiroya Yamada, Eiji Munetsuna, Mirai Yamazaki, Yoshitaka Ando, Ryosuke Fujii, Yoshiki Tsuboi, Atsushi Teshigawara, Itsuki Kageyama, Keisuke Osakabe, Keiko Sugimoto, Hiroaki Ishikawa, Naohiro Ichino, Yoshiji Ohta, Koji Ohashi, Shuji Hashimoto, Koji Suzuki
All data were statistically analyzed using JMP version 14.0 (SAS Institute, Cary, NC, USA). Serum aspartate transaminase (AST), alanine transaminase (ALT), triglyceride, and high-density lipoprotein (HDL) cholesterol levels are shown as geometric means and interquartile ranges with a log-normal distribution. Other characteristics (including DNA methylation) are shown as mean ± standard deviation (SD). We analyzed associations between DNA methylation at each CpG site of intron 1 in HIF3A with BMI, VAT, SAT and %body fat using single and multiple linear regression adjusted for age, systolic blood pressure, hemoglobin A1c, % neutrophils, smoking and exercise. Multiple comparisons were also corrected using the Bonferroni method. Values with P < .05 were considered statistically significant.