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 and Simulation
- 3.8 Design of Experiments
- 3.9 Intro to Epidemiology
- 3.10 Experiments vs. Observational Studies
- 3.11 Estimation
- 3.12 Hypothesis Testing
- 3.13 Statistical Power, Sensitivity and Specificity
- 3.14 Data Management
- 3.15 Statistical Power, sample-size, effect-size, sensitivity, specificity]]
- 3.16 Bias and Precision
- 3.17 Association and Causality
- 3.18 Rate-of-change
- 3.19 Clinical vs. Statistical Significance
- 3.20 Correction for Multiple Testing
- 3.21 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
The Scientific Methods for Health Sciences (SMHS) EBook is designed to support a 4-course training of scientific methods for graduate students in the health sciences.
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
Review of data types, exploratory data analyses and graphical representation of information.
Ubiquitous variation
There are many ways to quantify variability, which is present in all natural processes.
Parametric Inference
Foundations of parametric (model-based) statistical inference.
Probability Theory
Random variables, stochastic processes, and events are the core concepts necessary to define likelihoods of certain outcomes or results to be observed. We define event manipulations and present the fundamental principles of probability theory including conditional probability, total and Bayesian probability laws, and various combinatorial ideas.
Odds Ratio/Relative Risk
The relative risk, RR, (a measure of dependence comparing two probabilities in terms of their ratio) and the odds ratio, OR, (the fraction of one probability and its complement) are widely applicable in many healthcare studies.
Probability Distributions
Probability distributions are mathematical models for processes that we observe in nature. Although there are different types of distributions, they have common features and properties that make them useful in various scientific applications.
Resampling and Simulation
Resampling is a technique for estimation of sample statistics (e.g., medians, percentiles) by using subsets of available data or by randomly drawing replacement data. Simulation is a computational technique addressing specific imitations of what’s happening in the real world or system over time without awaiting it to happen by chance.
Design of Experiments
Design of experiments (DOE) is a technique for systematic and rigorous problem solving that applies data collection principles to ensure the generation of valid, supportable and reproducible conclusions.
Intro to Epidemiology
Epidemiology is the study of the distribution and determinants of disease frequency in human populations. This section presents the basic epidemiology concepts. More advanced epidemiological methodologies are discussed in the next chapter.
Experiments vs. Observational Studies
Experimental and observational studies have different characteristics and are useful in complementary investigations of association and causality.
Estimation
Estimation is a method of using sample data to approximate the values of specific population parameters of interest like population mean, variability or 97th percentile. Estimated parameters are expected to be interpretable, accurate and optimal, in some form.
Hypothesis Testing
Hypothesis testing is a quantitative decision-making technique for examining the characteristics (e.g., centrality, span) of populations or processes based on observed experimental data.
Statistical Power, Sensitivity and Specificity
The fundamental concepts of type I (false-positive) and type II (false-negative) errors lead to the important study-specific notions of statistical power, sample size, effect size, sensitivity and specificity.
Data Management
All modern data-driven scientific inquiries demand deep understanding of tabular, ASCII, binary, streaming, and cloud data management, processing and interpretation.
Statistical Power, sample-size, effect-size, sensitivity, specificity]]
Bias and Precision
Bias and precision are two important and complementary characteristics of estimated parameters that quantify the accuracy and variability of approximated quantities.
Association and Causality
An association is a relationship between two, or more, measured quantities that renders them statistically dependent so that the occurrence of one does affect the probability of the other. A causal relation is a specific type of association between an event (the cause) and a second event (the effect) that is considered to be a consequence of the first event.
Rate-of-change
Rate of change is a technical indicator describing the rate in which one quantity changes in relation to another quantity.
Clinical vs. Statistical Significance
Statistical significance addresses the question of whether or not the results of a statistical test meet an accepted quantitative criterion, whereas clinical significance is answers the question of whether the observed difference between two treatments (e.g., new and old therapy) found in the study large enough to alter the clinical practice.
Correction for Multiple Testing
Multiple testing refers to analytical protocols involving testing of several (typically more then two) hypotheses. Multiple testing studies require correction for type I (false-positive rate), which can be done using Bonferroni's method, Tukey’s procedure, family-wise error rate (FWER), or false discovery rate (FDR).
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
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