Two-factor factorial design refers to the research involving two experimental factors and the number of the experimental groups equals to the product of the levels of the two experimental factors. In other words, it is the complete combination of the levels of the two experimental factors. The research subjects are randomly divided into the experimental groups. The two experimental factors are performed on the subjects at the same time, meaning that there is no order. The two experimental factors are equal during statistical analysis, that is to say, there is no primary or secondary distinction, nor nested relation. This article introduces estimation of sample size and testing power of quantitative data with two-factor factorial design.

Research factors are a very important element in any research design. Research factors include experimental and non-experimental factors. The former is the general term used to describe the similar experimental conditions that researchers are interested in, while the latter are other factors that researchers have little interest in but may influence the result. This article mainly focuses on the following issues: the definition of research factors, the selection and arrangement of experimental factors and non-experimental factors, the interaction between research factors, the standardization of research factors and the common mistakes frequently made by researchers.

Aim To observe the effects of liver cirrho-sis on the expression of transforming growth factor-β1 ( TGF-β1 ) and ColⅠin rat myocardium and interven-tion of erythropoietin ( EPO ) . Methods Thirty-six male Sprague-Dasley rats were randomly divided into three groups:control group, liver cirrhosis group and EPO group, then the cardic hemodynamic parameters in vivo and levels of serum lactate dehydrogenase ( LDH ) as well as creatine kinase isoenzyme ( CK-MB) were measured. With Masson′s trichrome stain, changes of collagen formation of myocardial tissue in different groups were observed. Also the mRNA ex-pressions of TGF-β1 and ColⅠin myocardium were de-tected by RT-PCR. Results In contrast to control group, rats in liver cirrhosis group showed a decline in systolic and diastolic function of left ventricule, rising myocardial enzyme, a distinct increase of cardiac colla-gen deposition, as well as an elevation of TGF-β1 and ColⅠmRNA expressions. In contrast to liver cirrhosis group, rats in EPO group demonstrated an improve-ment in systolic and diastolic function of left ventricule as well as in cardiac collagen deposition, and a de-crease in both myocardial enzyme and TGF-β1 and ColⅠmRNA expressions. Conclusion Liver cirrhosis can lead to the changes of myocardial structure and function in rats,and it can accelerate myocardial inter-stitial fibrosis; EPO can protect the myocardial injury in liver cirrhosis rats.

This article introduces the definition and sample size estimation of three special tests (namely, non-inferiority test, equivalence test and superiority test) for qualitative data with the design of one factor with two levels having a binary response variable. Non-inferiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is not clinically inferior to that of the positive control drug. Equivalence test refers to the research design of which the objective is to verify that the experimental drug and the control drug have clinically equivalent efficacy. Superiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is clinically superior to that of the control drug. By specific examples, this article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.

Sample size estimation is necessary for any experimental or survey research. An appropriate estimation of sample size based on known information and statistical knowledge is of great significance. This article introduces methods of sample size estimation of difference test for data with the design of one factor with two levels, including sample size estimation formulas and realization based on the formulas and the POWER procedure of SAS software for quantitative data and qualitative data with the design of one factor with two levels. In addition, this article presents examples for analysis, which will play a leading role for researchers to implement the repetition principle during the research design phase.

ABSTRACT: Estimation of sample size and testing power is an important component of research design. This article introduced methods for sample size and testing power estimation of difference test for quantitative and qualitative data with the single-group design, the paired design or the crossover design. To be specific, this article introduced formulas for sample size and testing power estimation of difference test for quantitative and qualitative data with the above three designs, the realization based on the formulas and the POWER procedure of SAS software and elaborated it with examples, which will benefit researchers for implementing the repetition principle.

The research subject is the first key element of the three key elements in the research design. An appropriate selection of research subjects is crucial to the success of the research. This article summarizes the general principles for the selection of research subjects, the types and numbers of research subjects and the common mistakes that researchers tend to make in the selection of the research subjects. This article also provides the methodology suggestions for the selection of research subjects.

This article introduces the general concepts and methods of sample size estimation and testing power analysis. It focuses on parametric methods of sample size estimation, including sample size estimation of estimating the population mean and the population probability. It also provides estimation formulas and introduces how to realize sample size estimation manually and by SAS software.

This article introduces definitions of three special tests, namely, non-inferiority test (to verify that the efficacy of the experimental drug is clinically not inferior to that of the positive control drug), equivalence test (to verify that the efficacy of the experimental drug is equivalent to that of the control drug) and superiority test (to verify that the efficacy of the experimental drug is superior to that of the control drug), and methods of sample size estimation under the three different conditions. By specific examples, the article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.