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.

Objective To observe the expression of pancreatic K_(ATP) channels (Kir6.2/SUR1) in rats with chronic pancreatitis and explore the intervention of nateglinide on the changes.Methods Wistar rats were induced to suffer from chronic pancreatitis and then randomized into model group, nateglinide group and control group.Then OGTT of them were observed.RT-PCR was used to detect the expression of Kir6.2 and SUR1 mRNA, and western blot to detect the expression of Kir6.2 and SUR1 proteins.Results Model rats displayed impaired glucose tolerance (IGT).The expression of Kir6.2 and SUR1 in model group decreased significantly(P＜0.05), and nateglinide displayed up-reg-ulation to the expression in some degree.Conclusion The expression of pancreatic K_(ATP) channels in rats with chronic pancreatitis diminished, which might be the important mechanism of the development of pancreatogenic diabetes.Nateglinide can up-regulate the expression in some degree, which indicates that it may have latent effect of ameliorating the prognosis of patients with chronic pancreatitis.

Objective To observe the protective effect of anthocyanin on irradiation induced bone marrow c-kit positive cell injury, and further explore its possible mechanism. Methods Mouse bone marrow c-kit positive cells were collected by cell sorting method. There were 2 groups: control group and anthocyanin group, which were sub-divided into three groups and received 0 Gy, 1 Gy and 4 Gy irradiation respectively. The control group was added 700μL cell suspension and an equal volume of serum-free hematopoietic stem/progenitor cell culture medium. The 2 × 10-5 mol/L anthocyanin was co-cultured with mouse bone marrow c-kit positive cells of anthocyanin group half an hour before irradiation exposure, then cells were cultured for 18 hours under the conventional culture conditions (37℃,5%CO2). Mouse c-kit positive cell viability was measured by bioluminescence, and which was reflected by relative light units (RLU). The ability of colony-forming units was reflected by CFU-GM. The reactive oxygen species (ROS) level and mean fluorescence intensity (MFI) ofγ-H2AX were detected by flow cytometry. Results Compared to un-irradiated control group, the cell viability and the number of CFU-GM were decreased significantly, while the ROS level and MFI ofγ-H2AX were increased in c-kit positive cells irradiated with 1 Gy and 4 Gy (P<0.05). Compared to 1 Gy and 4 Gy irradiation groups, c-kit positive cell viability and the number of CFU-GM were increased, the ROS level and MFI of γ-H2AX were decreased in anthocyanin group (P < 0.05). Conclusion Anthocyanin exhibits a promising protective effect on radiation-induced bone marrow c-kit positive cell injury, which may be related to the alleviating ROS and DNA damage in bone marrow cells.

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.

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.