[How to scientifically estimate sample size in physiological research].

Objective: To bring about physiological researchers' attention of the importance of sample size estimation.

Methods: The significance as well as the current problems of sample size estimation were illustrated and the commonly-used sample size estimation methods were introduced.

Results: The basic concepts and necessary premises of sample size estimation were stated.

[Appropriate application of statistical methods in physiological research].

Objective: To offer a series of efficient methods to physiologists in appropriate selection, and application of statistical techniques.

Methods: We bring about two questions as follows:What's the role of statistics in the process of a physiological research? How to make sure the results produced in a physiological research can be repeatable in practice in the long run. From the answers to these two questions, we highlight the importance of the discipline of statistics to research work, explain why it is difficult, how to choose a suitable statistical method in a specific situation, and offer the critical methods to use statistics accurately and appropriately.

Clinical progression and predictors of death in patients with severe fever with thrombocytopenia syndrome in China.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease of which the clinical progression and factors related to death are still unclear.

Objective: To identify the clinical progression of SFTS and explore predictors of fatal outcome throughout the disease progress.

Study Design: A prospective study was performed in a general hospital located in Xinyang city during 2011-2013.

Multifactor designs able to examine the interactions.

Multifactor designs that are able to examine the interactions include factorial design, factorial design with a block factor, repeated measurement design; orthogonal design, split-block design, etc. Among all the above design types that are able to examine the interactions, the factorial design is the most commonly used. It is also called the full-factor experimental design, which means that the levels of all the experimental factors involved in the research are completely combined, and k independent repeated experiments are conducted under each experimental condition.

Three-factor designs unable to examine the interactions (part 2).

Three-factor designs that are unable to examine the interactions include crossover design and Latin square design, which can examine three factors: an experimental factor and two block factors. Although the two design types are not quite frequently used in practical research, an unexpected research effect will be achieved if they are correctly adopted on appropriate occasions. This article introduced the 3×3 crossover design and the Latin square design by examples.

Three-factor designs unable to examine the interactions (part 1).

Three-factor designs that are unable to examine the interactions include crossover design and Latin square design, which can examine three factors, namely, an experimental factor and two block factors. Although the two design types are not quite frequently used in practical research, an unexpected research effect will be achieved if they are correctly adopted on appropriate occasions. Due to the limit of space, this article introduces two forms of crossover design.

Two-factor designs unable to examine the interactions (Part 2).

Two-factor designs are very commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of experimental data under a certain condition (usually one of the experimental conditions that are formed by the complete combination of the levels of the two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced incomplete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design.

Two-factor designs unable to examine the interactions (part 1).

Two-factor designs are quite commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of the experimental data under a certain condition (usually it is one of the experimental conditions which is formed by the complete combination of the levels of two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced non-complete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design.

How to choose an appropriate experimental design type (Part 2).

How to choose an appropriate design type to arrange research factors and their levels is an important issue in scientific research. Choosing an appropriate design type is directly related to the accuracy, scientificness and credibility of a research result. When facing a practical issue, how can researchers choose the most appropriate experimental design type to arrange an experiment based on the research objective and the practical situation? This article mainly introduces the related contents of the design of one factor with two levels and the design of one factor with k (k≥3) levels by analyzing some examples.

How to choose an appropriate experimental design type (Part 1).

How to choose an appropriate experimental design type to arrange research factors and their levels is an important issue in experimental research. Choosing an appropriate design type is directly related to the accuracy and reliability of the research result. When confronting a practical issue, how can researchers choose the most appropriate design type to arrange the experiment based on research objective and specified situation? This article mainly introduces the related contents of the single-group design and the paired design through practical examples.

The balance principle in scientific research.

The principles of balance, randomization, control and repetition, which are closely related, constitute the four principles of scientific research. The balance principle is the kernel of the four principles which runs through the other three. However, in scientific research, the balance principle is always overlooked.

Estimation of sample size and testing power (part 7).

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.

Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus.

Background: Various methods can be applied to build predictive models for the clinical data with binary outcome variable. This research aims to explore the process of constructing common predictive models, Logistic regression (LR), decision tree (DT) and multilayer perceptron (MLP), as well as focus on specific details when applying the methods mentioned above: what preconditions should be satisfied, how to set parameters of the model, how to screen variables and build accuracy models quickly and efficiently, and how to assess the generalization ability (that is, prediction performance) reliably by Monte Carlo method in the case of small sample size.

Methods: All the 274 patients (include 137 type 2 diabetes mellitus with diabetic peripheral neuropathy and 137 type 2 diabetes mellitus without diabetic peripheral neuropathy) from the Metabolic Disease Hospital in Tianjin participated in the study.

Estimation of sample size and testing power (Part 4).

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.

Mycoplasma pneumoniae pneumonia in hospitalized children diagnosed at acute stage by paired sera.

Background: Mycoplasma pneumoniae (M. pneumoniae) is a frequent cause of respiratory tract infections. However, there is deficient knowledge about the clinical manifestations of M.

Estimation of sample size and testing power (Part 3).

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.

Estimation of sample size and testing power (part 2).

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.

Estimation of sample size and testing power (Part 1).

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.

The repetition principle in scientific research.

The repetition principle is important in scientific research, because the observational indexes are random variables, which require a certain amount of samples to reveal their changing regularity. The repetition principle stabilizes the mean and the standard variation, so that statistics of the sample can well represent the parameters of the population. Thus, the statistical inference will be reliable.

The control principle in scientific research.

The control principle is one of the four basic principles of research design. Without a control group, the conclusion of research will be unconvincing; furthermore, if the control group is not set properly, the conclusion will be unreliable. Generally, there is more than one control group in a multi-factor design.

Methods and analysis of realizing randomized grouping.

Randomization is one of the four basic principles of research design. The meaning of randomization includes two aspects: one is to randomly select samples from the population, which is known as random sampling; the other is to randomly group all the samples, which is called randomized grouping. Randomized grouping can be subdivided into three categories: completely, stratified and dynamically randomized grouping.

The principle of randomization in scientific research.

Scientific research design includes specialty design and statistics design which can be subdivided into experimental design, clinical trial design and survey design. Usually, statistics textbooks introduce the core aspects of experimental design as the three key elements, the four principles and the design types, which run through the whole scientific research design and determine the overall success of the research. This article discusses the principle of randomization, which is one of the four principles, and focuses on the following two issues--the definition and function of randomization and the real life examples which go against the randomization principle, thereby demonstrating that strict adherence to the randomization principle leads to meaningful and valuable scientific research.

How to appropriately choose observed indexes.

Observed index is a very important element in a research design, because it is a specific reflection of the effects of research factors on the research subjects and is indispensable in any research. Generally, there are two types of observed indexes: the indexes that reflect natural attributes, habits or states of the research subjects and the indexes that reflect the effects of different drugs or treatments on research subjects. This article mainly introduces the definition, characteristics, selection and observation of research indexes and the major and minor indexes.