Titlebook: Topics in Biostatistics; Walter T. Ambrosius Book 2007 Humana Press 2007 Radiologieinformationssystem.bioinformatics.biostatistics.cancer.

NEX

Descriptive Statistics,f statistics, for example the variance, as well as the use of transformations. The concepts in this chapter are useful for uncovering patterns within the data and for effectively presenting the results of a project.

硬化

Logistic Regression,stic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.

Mendicant

Study Design: The Basics,h questions. In this chapter, the different kinds of experimental studies commonly used in biology and medicine are introduced. A brief survey of basic experimental study designs, randomization, blinding, possible biases, issues in data analysis, and interpretation of the study results are mainly provided.

POINT

Observational Study Design,riants. It also includes examples of the key measures of relationship between factor and outcome in observational studies, the relative risk and the odds ratio. The similarity of the two measures for low incidence outcomes is illustrated, as is the use of attributable risk to assess how much of a binary outcome is due to a single factor.

是剝皮

Correlation and Simple Linear Regression, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques. such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

領袖氣質(zhì)

Multiple Linear Regression,l fit. The special cases of regression with interactions among the variables, polynomial regression, regressions with categorical (grouping) variables, and separate slopes models are also covered. Examples in microbiology are used throughout.

油氈

亂砍

Statistical Inference on Categorical Variables,s includes approximation of the binomial distribution with a normal distribution, estimation and inference for one and two binomial samples, inference for . and . contingency tables, and estimation of sample size. Relevant data examples, along with discussions of which study designs generated the data, are presented throughout the chapter.

adjacent

護航艦

Linear Mixed Effects Models, differs between experimental groups. In other experiments, there are multiple sources of variability, such as both between-subject variation and technical variation. As demonstrated in this chapter, linear mixed effects models provide a versatile and powerful framework in which to address research objectives efficiently and appropriately.