# Scientific Methods for Health Sciences

## Contents

- 1 SOCR Wiki: Scientific Methods for Health Sciences
- 2 Preface
- 3 Chapter I: Fundamentals
- 3.1 Exploratory Data Analysis, Plots and Charts
- 3.2 Ubiquitous variation
- 3.3 Parametric inference
- 3.4 Probability Theory
- 3.5 Odds Ratio/Relative Risk
- 3.6 Probability Distributions
- 3.7 Resampling/Simulation
- 3.8 Design of Experiments
- 3.9 Intro to Epidemiology
- 3.10 Experiments vs. Observational studies
- 3.11 Estimation
- 3.12 Hypothesis
- 3.13 Data management
- 3.14 Statistical Power, sample-size, effect-size, sensitivity, specificity
- 3.15 Bias/Precision
- 3.16 Association vs. Causality
- 3.17 Rate-of-change
- 3.18 Clinical vs. Stat significance
- 3.19 Statistical Independence Bayesian Rule

- 4 Chapter II: Applied Inference
- 4.1 Epidemiology
- 4.2 Correlation/SLR (ρ and slope inference, 1-2 samples)
- 4.3 ROC Curve
- 4.4 ANOVA
- 4.5 Non-parametric inference
- 4.6 Cronbach's α
- 4.7 Measurement Reliability/Validity
- 4.8 Survival Analysis
- 4.9 Decision theory
- 4.10 CLT/LLNs – limiting results and misconceptions
- 4.11 Association Tests
- 4.12 Bayesian Inference
- 4.13 PCA/ICA/Factor Analysis
- 4.14 Point/Interval Estimation (CI) – MoM, MLE
- 4.15 Instrument performance Evaluation
- 4.16 Study/Research Critiques
- 4.17 Common mistakes and misconceptions in using probability and statistics, identifying potential assumption violations, and avoiding them

- 5 Chapter III: Linear Modeling
- 5.1 MLR Regression
- 5.2 GLM
- 5.3 ANOVA
- 5.4 ANCOVA
- 5.5 MANOVA
- 5.6 MANCOVA
- 5.7 Repeated measures ANOVA
- 5.8 (Partial) Correlation
- 5.9 Time series analysis
- 5.10 Fixed, randomized and mixed models
- 5.11 Hierarchical Linear Models
- 5.12 Multi-Model Inference
- 5.13 Mixture modeling
- 5.14 Surveys
- 5.15 Longitudinal data
- 5.16 Generalized Estimating Equations (GEE) models
- 5.17 Model Fitting and Model Quality (KS-test)

- 6 Chapter IV: Special Topics
- 6.1 Scientific Visualization
- 6.2 PCOR/CER methods Heterogeneity of Treatment Effects
- 6.3 Big-Data/Big-Science
- 6.4 Missing data
- 6.5 Genotype-Environment-Phenotype associations
- 6.6 Medical imaging
- 6.7 Data Networks
- 6.8 Adaptive Clinical Trials
- 6.9 Databases/registries
- 6.10 Meta-analyses
- 6.11 Causality/Causal Inference, SEM
- 6.12 Classification methods
- 6.13 Time-series analysis
- 6.14 Scientific Validation
- 6.15 Geographic Information Systems (GIS)
- 6.16 Rasch measurement model/analysis
- 6.17 MCMC sampling for Bayesian inference
- 6.18 Network Analysis

## SOCR Wiki: Scientific Methods for Health Sciences

Electronic book (EBook) on Scientific Methods for Health Sciences (coming up ...)

## Preface

This is an electronic Internet-based *Scientific Methods for Health Sciences (SMHS) EBook*. The materials, tools and demonstrations presented in this EBook may be helpful to learners, instructors and scientists involved in biomedical, healthcare, Big Data, and informatics studies. The EBook is initially developed by the University of MIchigan Statistics Online Computational Resource (SOCR). However, many students, faculty, educators and member of the community participate, and others are encouraged to take part, in expanding, improving and validating the content of these learning materials.

There are 4 novel features of this specific *EBook*. It is community-built, completely open-access (in terms of use and contributions), blends information technology, scientific techniques and modern pedagogical concepts, and is multilingual.

### Format

Follow the instructions in this page to expand, revise or improve the materials in this EBook.

### Learning and Instructional Usage

This section describes the means of traversing, searching, discovering and utilizing the SMHS EBook resources in both formal and informal learning setting.

### Copyrights

The SMHS EBook is a freely and openly accessible electronic book developed by SOCR and the general community.

## Chapter I: Fundamentals

### Exploratory Data Analysis, Plots and Charts

### Ubiquitous variation

### Parametric inference

### Probability Theory

### Odds Ratio/Relative Risk

### Probability Distributions

### Resampling/Simulation

### Design of Experiments

### Intro to Epidemiology

### Experiments vs. Observational studies

### Estimation

### Hypothesis

### Data management

### Statistical Power, sample-size, effect-size, sensitivity, specificity

### Bias/Precision

### Association vs. Causality

### Rate-of-change

### Clinical vs. Stat significance

### Statistical Independence Bayesian Rule

## Chapter II: Applied Inference

### Epidemiology

### Correlation/SLR (ρ and slope inference, 1-2 samples)

### ROC Curve

### ANOVA

### Non-parametric inference

### Cronbach's α

### Measurement Reliability/Validity

### Survival Analysis

### Decision theory

### CLT/LLNs – limiting results and misconceptions

### Association Tests

### Bayesian Inference

### PCA/ICA/Factor Analysis

### Point/Interval Estimation (CI) – MoM, MLE

### Instrument performance Evaluation

### Study/Research Critiques

### Common mistakes and misconceptions in using probability and statistics, identifying potential assumption violations, and avoiding them

## Chapter III: Linear Modeling

### MLR Regression

### GLM

### ANOVA

### ANCOVA

### MANOVA

### MANCOVA

### Repeated measures ANOVA

### (Partial) Correlation

### Time series analysis

### Fixed, randomized and mixed models

### Hierarchical Linear Models

### Multi-Model Inference

### Mixture modeling

### Surveys

### Longitudinal data

### Generalized Estimating Equations (GEE) models

### Model Fitting and Model Quality (KS-test)

## Chapter IV: Special Topics

### Scientific Visualization

### PCOR/CER methods Heterogeneity of Treatment Effects

### Big-Data/Big-Science

### Missing data

### Genotype-Environment-Phenotype associations

### Medical imaging

### Data Networks

### Adaptive Clinical Trials

### Databases/registries

### Meta-analyses

### Causality/Causal Inference, SEM

### Classification methods

### Time-series analysis

### Scientific Validation

### Geographic Information Systems (GIS)

### Rasch measurement model/analysis

### MCMC sampling for Bayesian inference

### Network Analysis

- SOCR Home page: http://www.socr.umich.edu

Translate this page: