Scientific Methods for Health Sciences

SOCR Wiki: Scientific Methods for Health Sciences

Scientific Methods for Health Sciences EBook

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 97^th 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.

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).

Chapter IV: Special Topics

Network Analysis

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