Power and sample size calculation for the win odds test: application to an ordinal endpoint in COVID-19 trials.

The win odds is a distribution-free method of comparing locations of distributions of two independent random variables. Introduced as a method for analyzing hierarchical composite endpoints, it is well suited to be used in the analysis of ordinal scale endpoints in COVID-19 clinical trials. For a single outcome, we provide power and sample size calculation formulas for the win odds test. We also provide an implementation of the win odds analysis method for a single ordinal outcome in a commonly used statistical software to make the win odds analysis fully reproducible.

How to use χ2 test correctly—— χ2 test for the data of four-fold tables collected from the cohort design / 四川精神卫生

The purpose of this paper was to introduce the χ2 test for the data of four-fold tables collected from the cohort design， the hypothesis test and interval estimation of relative risk， and the implementation of SAS and R software. In view of the characteristics of cohort design， the concept of relative risk， hypothesis testing and interval estimation were emphasized. It was given that the interpretation of the results calculated by SAS and R software， as well as the statistical and professional conclusions.

How to use χ2 test correctly—— χ2 test for the data of four-fold tables collected from the cross-sectional design / 四川精神卫生

The purpose of this article was to introduce the χ2 test for the data of four-fold tables collected from the cross-sectional design and the realization of SAS and R software. Specifically， three methods were introduced， namely " Pearson΄s χ2 test"， "corrected Pearson΄s χ2 test" and "likelihood ratio χ2 test". The contents involved the test hypotheses， calculation principles and formulas， prerequisites， implementation of SAS and R software， interpretation of results and statement of conclusions of the aforementioned three statistical analysis methods.

How to use χ2 test correctly——χ2 test for the data of four-fold table collected from the matched pairs design / 四川精神卫生

The purpose of this paper was to introduce the McNemar΄s χ2 test and SAS and R software implementation of four-fold table data collected from the matched pairs design. Firstly， it was proposed that there were three situations for the data of four-fold table of the paired design， namely ①the data of paired design four-fold table with the special "gold standard" was worthy of statistical analysis； ②the data of four-fold table of the paired design without the special "gold standard" was not worthy of statistical analysis； ③the data of four-fold table collected from the matched pairs design with implicit "gold standard" was worthy of statistical analysis. Secondly， taking the "problems and data" in the first case as the object of statistical analysis， SAS and R software were used to analyze the differences， the calculation results were given and explained， and the statistical and professional conclusions were also made.

How to use χ2 test correctly——CMH χ2 tests for the data collected from the three kinds of R×C contingency tables / 四川精神卫生

The purpose of this paper was to introduce the CMH χ2 test and SAS software implementation of the three kinds of R×C contingency table data. The first type was called “two-way unordered R×C contingency table data”. The CMH χ2 test corresponding to this type of data was essentially the Pearson’s χ2 test. The second type was called “R×C contingency table data with an ordinal outcome variable”. The CMH χ2 test corresponding to this kind of data was essentially a rank sum test. The third type was called “R×C contingency table data which was of two ordinal variables with different attributes”. The CMH χ2 test corresponding to the data was essentially Pearson’s correlation analysis or Spearman’s rank correlation analysis. When there were 1 or 2 “ordinal variables” in the R×C contingency table data， it was necessary to “assign or score” the ordinal variables before performing statistical analysis. In the FREQ procedure of SAS/STAT， there were four scoring methods. With different scoring approach， both the expression form and the calculation results of CMH χ2 test statistics could change accordingly.

How to use χ2 test correctly—— χ2 tests for the data collected from the two kinds of two dimensional contingency tables with an ordinal variable in one direction / 四川精神卫生

The purpose of this paper was to introduce the linear trend χ2 tests for the data of the two kinds of one-way ordinal two-dimensional contingency tables and the implementation of SAS and R software. The two one-way ordered two-dimensional contingency tables referred to the “R×2 contingency table” with R values ordinal variable as the cause variable and the “2×C contingency table”with C values ordinal variable as the outcome variable. Their linear trend tests required the use of the Cochran-Armitage’s linear trend χ2 test and the Lee’s linear trend χ2 test， respectively.

How to use χ2 test correctly—— χ2 tests for the data collected from the three kinds of two dimensional contingency tables without ordinal variables in two directions / 四川精神卫生

The purpose of this article was to introduce the χ2 test and SAS and R software implementation of three kinds of two-way unordered two-dimensional contingency table data except the four-fold table data. The three kinds of the tables referred to the two-way unordered “R×2”“2×C” and “R×C” （both R and C were greater than 2） contingency tables. Generally speaking， the purpose of analyzing them was to test whether the two attribute variables in the contingency table were independent. When certain prerequisites were met， the simple statistical analysis method that could be used was the χ2 test， when the specific prerequisites were not met， the Fisher’s exact test， which required the large amount of calculation， should be selected.

How to use χ2 test correctly—— χ2 test for the data of four-fold tables collected from the case-control design / 四川精神卫生

The purpose of this paper was to introduce the χ2 test for the data of four-fold tables collected from the case-control design， the hypothesis test and interval estimation of odds ratio， and the implementation of SAS and R software. In view of the characteristics of case-control design， the concept of odds ratio， hypothesis testing and interval estimation were emphasized. It was given that the interpretation of the results calculated by SAS and R software， as well as the statistical conclusion and professional.

The Impact of Race and Geographical Location on the Treatment Options of Cervical Cancer in Black and White Women Living in the State of Alabama.

This study assesses the association between age, race, geographical-location, stage, and treatments of Cervical Cancer (CerCancer) in Black and White women, living in Alabama. Data from 2004-2013 was provided by the Alabama Cancer Registry. To perform Chi-square and logistic regression tests, SAS-software was used for analysis. In urban counties, Blacks 40-49 years old diagnosed with localized stage of CerCancer were the most likely to receive surgery (74.14%; P < .0001), followed by Whites 17-39 years old diagnosed with regional stage, were the most likely to receive radiation and surgery-radiation sequence (66.32 and 66.67%; P < .0001) respectively. Also, Whites 50 years and older diagnosed with regional stage were the most likely to receive chemotherapy (65.87%; P < .0001). In rural counties, Blacks 40-49 years old diagnosed with regional stage were the most likely to receive radiation (70.37%) and chemotherapy (83.33%) with P = .005 and .003 respectively, followed by Whites 17-39 years old diagnosed with localized stage were the most likely to receive surgery (76.81%; P < .0001). Adjusting for age, stage and county, Blacks had 1.12 (95% CI = .88-1.42) times the odds of receiving more radiation treatment. Blacks had .76 times adjusted odds (95% CI .59-.99) of receiving less surgery compared to Whites. Treatment disparities exist between Blacks and Whites in Alabama.

Disparities in Cervical Cancer Treatment Options between African American (Black) and White Women in Alabama.

There are disparities in cervical cancer treatment options between African American (Black) and White women in Alabama. The objective of this study was to identify and assess factors contributing to the prevailing inequalities in cervical cancer treatment options between Blacks and Whites, who are living in urban, rural Black Belt (BB), and other rural counties of Alabama. The data of our study population, which was comprised of 2,124 cases of cervical cancer in women 17 years and older, were extracted from the 2004 to 2013 dataset of the Alabama Department of Public Health (ADPH) Cancer Registry. For the analysis of frequency distributions, chi-square, and logistic regression tests SAS software was used. Racial disparities in cervical cancer treatment options for Blacks living within the same counties as Whites still exist. The study analysis showed that younger Blacks living in urban counties with advanced stages of cervical cancer were more likely to receive radiation treatment options but were less likely to undergo surgical treatment options (p-value <.0001). Younger Blacks living in the rural BB and other rural counties were mainly treated with radiation options for the early stages of cervical cancer (p-value 0.001), while older ones received surgery options (p-value <.0001), and combined therapy of surgery and radiation options (p-value 0.05). When adjusted for age, stage of cancer, and county of residence, Blacks had 2.76 (95% CI 0.90-8.86) times the odds ratio of receiving immunotherapy options compared to Whites. Blacks had 0.74 (95% CI 0.58-0.95) times adjusted odds ratio of undergoing less surgery option compared to Whites. Our study findings suggest that cervical cancer treatment options and control interventions targeted towards disadvantaged women, particularly Blacks living in the rural BB and other rural counties have the potential to reduce and/or eradicate this preventable disease.

Matching Cases and Controls Using SAS® Software.

Matching is frequently used in observational studies, especially in medical research. However, only a small number of articles with matching programs for the SAS software (SAS Institute Inc., Cary, NC, USA) are available, even less are usable for inexperienced users of SAS software. This article presents a matching program for the SAS software and links to an online repository for examples and test data. The program enables matching on several variables and includes in-depth explanation of the expressions used and how to customize the program. The selection of controls is randomized and automated, minimizing the risk of selection bias. Also, the program provides means for the researcher to test for incomplete matching.

PSHREG: a SAS macro for proportional and nonproportional subdistribution hazards regression.

We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sample analyses. Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld-type residuals and testing correlation of these residuals with time, or by including interactions of covariates with functions of time. We illustrate application of these extended methods for competing risk regression using our macro, which is freely available at: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/pshreg, by means of analysis of a real chronic kidney disease study. We discuss differences in features and capabilities of %pshreg and the recent (January 2014) SAS PROC PHREG implementation of proportional subdistribution hazards modelling.

Applied statistical analysis for fourfold table data with the worksheet function of Excel / 中国地方病学杂志

Objective To use Excel worksheet function to achieve statistical analysis of the fourfold table qualitative data,and to improve knowledge of the.majority of researchers on the statistical capabilities of Excel.Methods The result of actual research published in Chinese Journal of Endemiology was the data source,and statistical analysis results of fourfold table data from Excel and from SAS software were contrasted.Results The worksheet function of Excel could realize the analysis fourfold table data chi-square test,obtained an accurate P values,but not just P ＞ 0.05 or P ＜ 0.05.The results of chi-square test of Excel were the same as the results from SAS software,but Excel could not obtain probability value of fisher exact probability.Conclusion As a dependent statistical analysis software,Excel is an easy to learn,easy to use,efficient and a good helper.In the absence of professional statistical software,Excel can be used to achieve data statistical analysis.

Selection of High-yield Agro-antibiotic 2-16 Producing Strain and Optimization of Its Cultivation / 微生物学通报

In order to improve the production of agro-antibiotic 2-16,the producing strain(Streptomyces ahygroscopicus var.huangshanensis) was treated by protoplast regeneration,ultraviolet radiation,NTG mutagenesis and low energy C~(+) ion implantation.At last,a high-yield strain No.515 was obtained.The production of ~()No.515 was increased by 223.10%.By using Plackett-Burman design and Response Surface Analysis provided by SAS software,the cultivation condition of No.515 was optimized.The amount of agro-antibiotic 2-16 was increased by 38.53% when the strain No.515 was cultivated in the optimum medium instead of the initial one.