標(biāo)題: Titlebook: Topics in Biostatistics; Walter T. Ambrosius Book 2007 Humana Press 2007 Radiologieinformationssystem.bioinformatics.biostatistics.cancer. [打印本頁(yè)] 作者: Spring 時(shí)間: 2025-3-21 19:26
書目名稱Topics in Biostatistics影響因子(影響力)
書目名稱Topics in Biostatistics影響因子(影響力)學(xué)科排名
書目名稱Topics in Biostatistics網(wǎng)絡(luò)公開度
書目名稱Topics in Biostatistics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Topics in Biostatistics被引頻次
書目名稱Topics in Biostatistics被引頻次學(xué)科排名
書目名稱Topics in Biostatistics年度引用
書目名稱Topics in Biostatistics年度引用學(xué)科排名
書目名稱Topics in Biostatistics讀者反饋
書目名稱Topics in Biostatistics讀者反饋學(xué)科排名
作者: 遵循的規(guī)范 時(shí)間: 2025-3-21 23:43
Observational Study Design,troduces the major types of observational study designs: the longitudinal or cohort study, the comparative or case-control study, and some of their variants. It also includes examples of the key measures of relationship between factor and outcome in observational studies, the relative risk and the o作者: 宣稱 時(shí)間: 2025-3-22 02:56 作者: 天文臺(tái) 時(shí)間: 2025-3-22 06:23 作者: cumber 時(shí)間: 2025-3-22 12:08
Statistical Inference on Categorical Variables,pter, we first describe types of categorical data (nominal and ordinal) and how these types of data are distributed (binomial, multinomial, and independent multinomial). Next, methods for estimation and making statistical inferences for categorical data in commonly seen situations are presented. Thi作者: thyroid-hormone 時(shí)間: 2025-3-22 13:39 作者: ENNUI 時(shí)間: 2025-3-22 20:35
Correlation and Simple Linear Regression, continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex stati作者: 機(jī)構(gòu) 時(shí)間: 2025-3-22 21:32
Multiple Linear Regression, continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit. The special cases of regression with interactions among the variables, polynomial regression, regressions with categorical (grouping) variables作者: 半導(dǎo)體 時(shí)間: 2025-3-23 05:10 作者: Scintillations 時(shí)間: 2025-3-23 06:34
Linear Mixed Effects Models, requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for most methods described in previous chapters. In some experiments, the amount of random variation作者: 正常 時(shí)間: 2025-3-23 11:43
Design and Analysis of Experiments, completely randomized designs (treatments are assigned completely at random); (b) randomized block designs (experimental units are subdivided into blocks of like subjects, with one subject in each block randomly assigned to each treatment); (c) stratified designs (subjects are categorized into subp作者: 弄污 時(shí)間: 2025-3-23 16:27
Analysis of Change,nge. In these study designs, each subject serves as its own control. In this chapter, we consider different ways to assess change over time, for example, analyses for evaluating changes from a baseline condition. Study designs and analyses for single group studies and studies with two groups are dis作者: BIDE 時(shí)間: 2025-3-23 21:34
Logistic Regression, describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable.” Logistic regression models are used to study effects of predictor variables on categorical outcom作者: 火海 時(shí)間: 2025-3-23 23:53 作者: inspiration 時(shí)間: 2025-3-24 04:19 作者: ticlopidine 時(shí)間: 2025-3-24 07:34
Overview of Missing Data Techniques,vestigators to be able to perform analyses that will lead to proper inference. This chapter will review different missing data mechanisms, including random and non-random mechanisms. Basic methods will be presented using examples to illustrate approaches to analyzing data in the presence of missing 作者: Parallel 時(shí)間: 2025-3-24 11:58 作者: LATHE 時(shí)間: 2025-3-24 16:48 作者: 過于光澤 時(shí)間: 2025-3-24 22:55 作者: 披肩 時(shí)間: 2025-3-25 03:08 作者: NEX 時(shí)間: 2025-3-25 04:02
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.作者: 硬化 時(shí)間: 2025-3-25 08:25
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.作者: Mendicant 時(shí)間: 2025-3-25 13:40
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.作者: POINT 時(shí)間: 2025-3-25 16:13
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.作者: 是剝皮 時(shí)間: 2025-3-25 22:01
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.作者: 領(lǐng)袖氣質(zhì) 時(shí)間: 2025-3-26 02:49
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.作者: 油氈 時(shí)間: 2025-3-26 05:57 作者: 亂砍 時(shí)間: 2025-3-26 12:07
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.作者: adjacent 時(shí)間: 2025-3-26 13:31 作者: 護(hù)航艦 時(shí)間: 2025-3-26 17:51
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.作者: canonical 時(shí)間: 2025-3-26 21:24
Analysis of Change,cussed in detail. Examples come from published data. Statistical methods used in the examples include paired .-tests and analysis of covariance. The use of difference scores is discussed relative to analysis of covariance.作者: Fantasy 時(shí)間: 2025-3-27 04:05
Survival Analysis,discuss in some detail the proportional hazards model, which is a semiparametric regression model specifically developed for censored data. All methods are illustrated with artificial or real data sets.作者: sacrum 時(shí)間: 2025-3-27 05:58 作者: 原告 時(shí)間: 2025-3-27 13:10
Power and Sample Size,has too large a sample size and wastes resources. We will show how the power and required sample size can be calculated for several common types of studies, mention software that can be used for the necessary calculations, and discuss additional considerations.作者: 祖先 時(shí)間: 2025-3-27 17:01 作者: 不適當(dāng) 時(shí)間: 2025-3-27 19:49
