This document provides an overview of biostatistics and the role of biostatisticians. It discusses how biostatistics applies statistical methods to address questions in public health, medicine, and other biological fields. The document outlines some of the key roles of biostatisticians, which include identifying disease risk factors and treatments, designing and analyzing clinical studies, and developing new statistical methods for analyzing medical data. It also notes some of the challenges of biostatistics, such as separating systematic effects from random noise in data and making inferences.

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BIOSTATISTICS
MOTIVATION

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Statistics
 The science of collecting, monitoring,
analyzing, summarizing, and interpreting data
 This includes study design

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Biostatistics
 Statistics applied to biological (life) problems,
including:
 Public health
 Medicine
 Ecological and environmental
Much more statistics than biology, however
biostatisticians must learn the biology as well

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Biostatistician Roles
 Identify and develop treatments for disease and
estimate their effects.
 Identify risk factors for diseases.
 Design, monitor, analyze, interpret, and report
results of clinical studies.
 Develop statistical methodologies to address
questions arising from medical/public health data.

5. “Statistical thinking will one day be
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as necessary for efficient
citizenship as the ability to read
and write.”
H.G. Wells

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Challenges
 Statistical ideas can be intimidating and
difficult
 Thus:
 Statistical results are often “skipped-over” when
reading scientific literature
 Data is often mis-interpreted

7. Mis-Interpretation of Data
“On average, my class is doing well. Half of my
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students think that 2+2=3, the other half
thinks that 2+2=5.”

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You may think that:
 A Bar Chart is a map of the locations of the
nearest taverns
 A p-value is the result of a urinalysis
 A t-test is a blinded taste test between black
and green tea

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Challenge
 Much of life is composed of a systematic component (i.e., signal)
and a random component (i.e., error or noise)
 Example:
 Smoking is associated with lung cancer.
 Yet not everyone that smokes, gets lung cancer, and not
everyone that gets lung cancer, smokes
 Yet we know that there is an association (a systematic
component)
 Our challenge
 Identify the systematic component (separate it from the random
component), estimate it, and perhaps make inferences with it

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Article for Review

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Why me?

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http://stats.stackexchange.com/users/8165/o-devinyak

24. National program
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THE DISCIPLINE SCHEDULE
LECTURES
№ Theme
1 Concept and value of biostatistics as basic element of evident
medicine; history of the development and it role in health care
system.
2
Epidemiological researches in health care system, their
characteristics and the analysis.
3 Clinical epidemiology. Modern principles and rules of
conducting clinical researches. Concept of the null hypothesis,
principles of randomization and stratification in medical
researches.
4 Types of the data, principles of formation of statistical
aggregates (databases) for the analysis. Generalization of
statistical researches results and the meta-analysis. The analysis
of qualitative parameters, modern approaches to the analysis
and evaluation of quality of life. Types of questionnaires, their
characteristics.
5 Information supply of epidemiological and clinical researches.
Value of official statistics for the analysis of population health,
its advantages and disadvantages. The European base “Health
for all”. Statistical research software.
Our program
№ Theme
1 Descriptive statistics
2
Probability theory. Distribution of random
variables. Hypothesis testing
3 Evidence-based medicine. Design of
experiments. Clinical trials.
4 Correlation and regression
5 Odds and risks. Multiple comparisons.
Bayes rule.

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THE DISCIPLINE SCHEDULE
LECTURES
National program Our program
№ Theme
1. Methodological bases of the organization of statistical
researches in health care system.
2. Relative values (statistical coefficients). Graphic
methods of the analysis. Rows of dynamics, their
analysis.
3. Characteristics and the analysis of statistical data.
Averages and parameters of a variation.
4. Parametric methods of evaluation and the analysis of
statistical hypotheses.
5. Nonparametric methods of evaluation and the analysis
of statistical hypotheses.
6. Methods of standardization.
7. The analysis of interrelation between researched
parameters of statistical aggregates.
8. Risk factors. Methods of calculation and assessment of
risks.
9. Final module control.
№ Theme
1. Motivation. Introduction to biostatistics
2. Introduction to R. Descriptive statistics
3. Visualization of medical data.
Standardization
4. Hypothesis testing
5. Analysis of variance
6. Correlation and regression
7. Logistic regression (Step)
8. Survival analysis (Step)
9. Practical tasks solving (Step)
10. Final module control (Step)

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HOMEWORK
Think about your favourite field or topic in
medicine.
Find one recent full-text research (not review)
article on that topic (field).
Print it, read it and mark with boxes those
fragments that require knowledge of
biostatistics.

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How to search for articels?

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When Google Scholar does not
help
Copy the hyperlink from Google Scholar and
paste in secret website (sci-hub.org )