ABSTRACT
The purpose of this paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance of the univariate quantitative data with the Latin square design. The Latin square design could be divided into two categories： the general Latin square design and the Greek Latin square design. The former could be used for the experimental situation with one experimental factor and two block factors， the latter could be used for the experimental situation with two experimental factors and two block factors. In fact， Latin square designs could be further subdivided by whether or not the repeated experiments were performed and whether the block factor was a single individual type. Generally speaking， in addition to satisfying the requirements of "independence， normality and homogeneity of variance"， the interaction between all factors was required to be non-existent or negligible when performing an analysis of variance on the quantitative data with Latin square design. When the quantitative data did not meet the preconditions mentioned above， it was recommended to use a mixed-effects model to build the model and solve it， or to solve the estimated values of the parameters in the ANOVA model based on the generalized estimating equation method.
ABSTRACT
The purpose of the paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance for the quantitative data of the crossover design. In the calculation， three test statistics were involved， namely Ftreatment， Fstage and Findividual. They were three test statistics used to evaluate the statistical significance of the effect of the treatment factor， the stage factor， and the individual factor on the quantitative outcome variable， respectively. In general， it was assumed that there was no or negligible interaction among the three factors in a crossover design， so there was no need to evaluate whether the interaction term was statistically significant. With the help of SAS software， this paper conducted the univariate analysis of variance for the quantitative data of crossover designs for three examples of 2×2 crossover design， 3×3 crossover design and three-stage crossover design， and presented the calculation results and drew the statistical and professional conclusions.
ABSTRACT
The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the analysis of variance for the quantitative data with balanced incomplete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor B （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a balanced incomplete block design， so there was no need to evaluate whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with balanced incomplete block design on two examples， and presented the calculation results and made the statistical and professional conclusions.
ABSTRACT
The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the univariate analysis of variance for the quantitative data with randomized complete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a randomized block design， so there was no need to assess whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with randomized complete block design for two instances without and with repeated experiments， gave the calculation results， and made the statistical and professional conclusions.
ABSTRACT
OBJECTIVE: To compare randomized controlled trial (RCT) study and observational study systematically, and to provide reference for selecting suitable study design types for clinical researchers. METHODS: RCT study and observational study were compared in respects of study design and study report paradigm. Relevant literatures were retrieved from PubMed database and Chinese Journal Full-text Database. The differences of literature publication of RCT study and observational study were compared at home and abroad. RESULTS: There were differences in design principles, objectives, subjects, interventions and validity between RCT study and observational study. The requirements of CONSORT statemtnt and STROBE statement to the topics, abstracts, introduction, results and discussions of report paradigm of two studies were basically consistent, and main difference of them were in aspects of methods and other information. The number of literatures about RCT study and observational study had little gap at abroad, but had great gap at home, especially in cohort study with high-level evidence of evidence-based medicine. CONCLUSIONS: The observational study has developed rapidly in recent years, but RCT study is still a "gold standard" to evaluate the causal effect of clinical study. The researchers should choose the appropriate type of design according to the actual situation.
ABSTRACT
The core of experimental designs is introduced in this paper. Since some people often fail to fellow the principles of control and randomization in the experimental designs and clinical trials, a great many examples with wrong statistics in medical research are illustrated. It will help clinical doctors to improve the quality of the experimental designs through discrimination and explanation of those examples.