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Biostatistics is the application of statistical methods to biological data. It can be used to analyze the data from clinical trials, surveys, and natural experiments. Biostatistics is a major field of study in health care, public health, and related fields.

Biostatistics is an applied discipline that uses statistical methods to analyze biological data. Biostatisticians work in many different areas such as research design and analysis, medical studies and clinical trials, medical records analysis, epidemiology and disease surveillance, forensic science, pharmaceutical companies and government agencies. Read on to know more about Biostatistics Course Outline, what is biostatistics all about, what is biostatistics and its types, fundamentals of biostatistics syllabus and advanced biostatistics syllabus.

Biostatistics Course Outline

We begin with Biostatistics Course Outline, what is biostatistics all about, what is biostatistics and its types, fundamentals of biostatistics syllabus and advanced biostatistics syllabus.

This course is designed to provide you with a solid foundation in biostatistics, with an emphasis on the practical application of statistical methods, covering a variety of topics, including descriptive statistics, probability and probability distributions, hypothesis testing, correlation and regression analysis, survival analysis, and categorical data analysis. This course will be taught using R software.

This course is designed for students who are interested in learning more about statistical methods used in health sciences research. It is also appropriate for students who want to learn more about how to use R as a tool for data management and analysis or who plan on taking other courses that require knowledge of statistical methods.

This course is designed for students who are interested in learning more about statistical methods used in health sciences research. It is also appropriate for students who want to learn more about how to use R as a tool for data management and analysis or who plan on taking other courses that require knowledge of statistical methods.

Below are the course outline for Biostatistics:

Course Outline

Introduction to Statistics

Measurement and Data

Descriptive Statistics

Inference: Confidence Intervals and Hypothesis Testing

Inferential Statistics: Association, Correlation, Regression, and Multivariate Analysis

Analysis of Variance (ANOVA)

what is the scope of biostatistics

Next, we review what is biostatistics all about, what is biostatistics and its types, fundamentals of biostatistics syllabus and advanced biostatistics syllabus.

Using the tools of statistics, biostatisticians help answer pressing research questions in medicine, biology and public health, such as whether a new drug works, what causes cancer and other diseases, and how long a person with a certain illness is likely to survive.

“Biostatistics is central to all of science, because science needs that gathering of evidence and the evaluation of that evidence to make a judgment.”

Biostatisticians use their quantitative skills to team with experts in other fields, from biologists and cancer specialists to surgeons and geneticists. But they are not mere number-crunchers. They play pivotal roles in designing studies to ensure enough data and the right kind of information are collected. Then they analyze, evaluate and interpret the results – accounting for variables, biases and missing data along the way.

“It is a field that merges passion and skill with biomedical science and mathematics and statistics,” Heagerty says. “It’s got to have the bio in it somewhere.”

Adds Associate Professor Daniela Witten, “What we bring to the table is an understanding of not only the key statistical issues, but also the underlying biological and medical context.”

Some examples of ongoing and recent biostatistics work at the UW and its potential impact:

• Professor and former Department Chair Thomas Fleming was senior author of a study that showed antiretroviral therapy reduced the risk of heterosexual transmission of HIV by 96 percent – a discovery that could save countless lives and illnesses. Science magazine dubbed it the 2011 “Breakthrough of the Year.”
• Heagerty’s work on back pain with other UW colleagues showed that epidural injections for a common type of back pain made virtually no difference for patients – a finding that could potentially save costs and unnecessary medical procedures.
• Finding links between genetic variants and certain diseases. Now that genome sequencing is relatively cheap, scientists “should be able to identify the genetic underpinnings of a lot of human diseases and obtain a much better understanding of the science than was ever possible before,” according to Witten. That’s what precision medicine and targeted therapy is all about.
• Working with UW Biology Professor Samuel Wasser to use DNA from elephant tusks and dung to pinpoint where poaching occurs in Africa, giving law enforcement and conservation authorities the tools they need to crack down on the illegal trade.

At the UW, the Department of Biostatistics does two main things, according to Heagerty. It prepares students to practice biostatistics on a wide range of scientific teams and it develops ground-breaking thinkers through its PhD program.

“We’re training the next generation of innovators in the methodology,” he says.

That’s important in an era of emerging data sets, from genome sequencing to electronic medical records. New statistical tools and software are often needed to interpret the massive amounts of data and to detect correlations and causations.

The department has a long history of developing innovative methodology.

One of the classic investigative techniques in epidemiology is the case control study. This is research that starts with the outcome, or disease, first, then goes back to find risk factors that could have caused the disease. “It’s sort of a backward study design,” Heagerty says. “But it’s a really efficient study design. Those ideas were developed here by Ross Prentice (professor of Biostatistics).”

Professor Emeritus Norm Breslow, a former Chair of the department, was a leader in the development of survival methods used to study the time until an event such as death occurs. “These are huge contributions to medicine,” Heagerty says.

Then there are clinical trials, where researchers study the impact of a drug versus a placebo.

“Our department became prominent because of its work on how clinical trials are conducted and how the results are interpreted,” says Professor and former Chair Bruce Weir, a pioneer of statistical genetics. “Tom Fleming, Scott Emerson, Norm Breslow and Patrick Heagerty have developed new statistical methods to design clinical trials to interpret the results.”

UW biostatisticians also use their expertise to serve on data safety monitoring committees, overseeing numerous trials to see if they should be stopped early to prevent harm to participants or because a therapy or drug proves immediately effective.

The job market for biostatisticians is hot, from high-tech and pharmaceutical companies to research institutions. Some of the UW’s graduates have gone on to head academic departments elsewhere, giving the department a major role in shaping the careers and educations of biostatisticians across the country.

“The demand for people with our training is enormous,” Heagerty says. “This is a data-rich world and people who can gather the evidence and evaluate it are incredibly valuable in every domain – research, business or health care systems.”

what is biostatistics and its types

Now, we find out about what is biostatistics and its types, fundamentals of biostatistics syllabus and advanced biostatistics syllabus.

Biostatistics is a branch of biological science which deals with the study and methods of collection, presentation, analysis and interpretation of data of biological research. Biostatistics is also called as biometrics since it involves many measurements and calculations.

The field of statistics is divided into two major divisions: descriptive and inferential.

• Descriptive statistics: These include the collection and presentation of numerical information about an individual or group. For example, you might want to know how many people have a disease and what their demographics are (age, gender, etc.). This type of analysis is used before any other types of analysis are performed on your data.
• Inferential statistics: These are used to draw conclusions based on the raw data you’ve collected using descriptive statistics. You might want to know if there’s a difference between genders in terms of disease prevalence; this type of analysis will help you determine whether there truly is a difference or if it might just be random variation in the numbers.

Types of Data – Biostatistics

Observations recorded during research constitute data. There are three types of data i.e. nominal, ordinal, and interval data. Statistical methods for analysis mainly depend on type of data.

Observations recorded during research constitute data. There are three types of data i.e. nominal, ordinal, and interval data. Statistical methods for analysis mainly depend on type of data.

 Nominal data: This is synonymous with categorical datawhere data is simply assigned “names” or categories based on the presence or absence of certain attributes/characteristics without any ranking between the categories. For example,bacterial culture studies are categorized by growth as positive or negative to particular growth media. It also includes binominal data, which refers to two possible outcomes. For example, outcome of cancer may be death or survival, drug therapy with drug ‘X’ will show improvement or no improvement at all.

Ordinal data: It is also called as ordered, categorical, orgraded data. Generally, this type of data is expressed as scores or ranks. There is a natural order among categories, and they can be ranked or arranged in order. For example, speed may be classified as slow, medium, and fast. Since there is an order between the three grades of speed, this type of data is called as ordinal. To indicate the intensity of speed, it may also be expressed as scores (slow = 1, medium = 2, fast = 3). Hence, data can be arranged in an order and rank.

Interval data: This type of data is characterized by an equaland definite interval between two measurements. For example, weight is expressed as 20, 21, 22, 23, 24 kg. The interval between 20 and 21 is same as that between 23 and 24. Interval type of data can be either continuous or discrete. A continuous variable can take any value within a given range. For example: hemoglobin (Hb) level may be taken as 11.3, 12.6, 13.4 gm % while a discrete variable is usually assigned integer values i.e. does not have fractional values. For example, number of meals per day by a person is generally discrete variables. Sometimes, certain data may be converted from one form to another form to reduce skewness and make it to follow the normal distribution. For example, plant growth are converted to their log values and plotted in growth response curve to obtain a straight line so that analysis becomes easy. Data can be transformed by taking the logarithm, square root, or reciprocal. Logarithmic conversion is the most common data transformation used in agricultural research.

biostatistics major courses

A solid foundation in mathematics is highly recommended for those seeking a Bachelor of Science (BS) in biostatistics. Prerequisite classes may include courses in math and biology and possibly ecology, epidemiology, chemistry or other science courses. Classes that focus on statistical packages, such as SPSS or EpiInfo, could be considered helpful as well.

Courses that improve communication skills are also recommended. This is because biostatisticians have to interpret data and communicate research findings to other public health professionals and stakeholders. 

Many schools may not offer a bachelor’s degree in biostatistics. A BS in statistics or mathematics are relevant degrees for those interested in biostatistics. They will still provide the foundational skills needed to apply to master’s programs. Electives may help students gain a better understanding of biostatistics and its integral relationship to public health.

Curriculum for a Bachelor’s Degree in Biostatistics

Schools that do offer bachelor’s degrees in biostatistics will have required courses that focus on learning statistical methods. While most coursework will be in statistics, students will also need to take biology courses. Internships may be required, depending on the college or university. Students pursuing this degree can benefit from research-focused internships.

Examples of undergraduate courses in biostatistics include:

Intermediate Statistics: Design and Analysis
This course will build on information from introductory statistics courses. This covers two-sample t-tests, analysis of variance, contingency tables analysis, and Screening and Simpson’s paradox (Yule-Simpson effect). Students will learn issues in experimental and nonexperimental design and sampling plans. This course will use a statistical computer package. 

Basic Elements of Probability and Statistical Inference

Calculus classes are required to prepare for this course. Students will learn the fundamentals of probability, discrete and continuous distributions, and functions of random variables. In addition, the course provides an overview of descriptive statistics and the fundamentals of statistical inference, including estimation and hypothesis testing.

Principles of Microbiology

This introductory course provides a foundation for understanding how microbial pathogens and viral agents cause human disease. Students will learn the principles of cell structure and compare prokaryotic and eukaryotic cells; viral agents; bacterial genetics and antibiotic resistance. Students will learn about infectious disease, pathogenesis and immune response. This course will also cover the importance of vaccination as a key public health measure.

fundamentals of biostatistics syllabus

SYLLABUS OF COURSES OFFERED IN SEMESTER – 1

BSTA 101 DESCRIPTIVE STATISTICS, PROBABILITY AND
DISTRIBUTIONS

UNIT 1. Elementary concepts in Statistics: Concepts of statistical population and
sample from a population; qualitative and quantitative data; nominal, ordinal, ratio,
interval data; cross sectional and time series data; discrete and continuous data.
Collection and scrutiny of data: Primary data; designing a questionnaire and a schedule;
secondary data and sources of secondary data. Presentation of data: Diagrammatic and
graphical representation of data; frequency distributions and cumulative frequency
distributions; histogram, frequency polygon, stem and leaf chart and ogives. Descriptive
statistics: Concepts of central tendency or location, Absolute and relative measures of
dispersion; Box plot, Lorenz curve; skewness and kurtosis.

UNIT 2. Probability: Random Experiment; sample point; sample space; events;
mutually exclusive and exhaustive events; frequency and classical definitions of
probability. Axiomatic definition of probability; addition and multiplication theorems;
conditional probability and independence; Bayes’ theorem. (The main thrust is on
numerical problems and applications), Discrete and continuous random variables;
probability density functions and distribution functions; expectation of a random variable.

UNIT 3. Standard Univariate Distributions: Standard univariate discrete and
continuous distributions- uniform; binomial; Poisson; geometric; negative binomial and
hyper-geometric distributions. Uniform; exponential; normal; Laplace, gamma, beta, lognormal,
logistic and Weibull distributions.(elementary properties and applications only)

UNIT 4. Sampling Distributions, Law of large numbers and Central Limit
Theorem: Concepts of random sample and statistic; distribution of sample mean from a
normal population; chi-square distribution; F and t statistics, distributions (no
derivations) and their applications. Chi-square test for goodness of fit, Central Limit
Theorem for i.i.d case (statement and examples only). Evaluation of probabilities from
the binomial and Poisson distributions using central limit theorem. Chebychev’s
inequality and weak law of large numbers (statement and applications only).

advanced biostatistics syllabus

The aim of the course is that students should have a working knowledge of:

• Practically significant difference, equivalence and non-inferiority
• Relative Risk & Odds Ratio and their confidence intervals and Number Needed to Treat
• Randomization methods, stratification, blinding
• Missing data, Intention to Treat versus Per Protocol, Full analysis sets
• SPSS
• Analyses of variance
• Analyses for paired data (Paired t and McNemar’s tests)
• Study types (Pros and cons)
• Correlation and regression
• Analysis of covariance
• Multi-center Studies
• Bayes theorem and diagnostic tests
• Measures of agreement – Cronbach alpha and Cohen kappa
• Statistical analysis process – Protocol and Statistical Analysis Plan etc.
• Non-parametric methods
• Multiple testing
• Survival analysis
• Stratified analysis – Mantel-Haenszel
• Logistic Regression
• Meta-analysis
• Interim analysis, group sequential and adaptive designs

On successful completion of this module, students should be able to:

• exploit the greater generalizability and more rapid recruitment available with multi-centre studies.
• select appropriate methods of subject randomisation.
• use measures such as Relative Risk etc to express the clinical significance of a trial using a dichotomous end-point.
• calculate and interpret measures of agreement such as Cronbach’s alpha and Cohen’s kappa.
• identify trial structures that are appropriately analysed by more advanced statistical procedures such as analysis of variance, correlation & regression, analysis of covariance, the paired t & McNemar’s tests, logistic regression, survival analysis, non-parametric methods and stratified analyses such as Mantel-Haenszel.
• use SPSS to carry out the analyses mentioned above.
• understand the difference between Intention to Treat and Per protocol analyses and when each is most appropriately applied.
• carry out meta-analyses
• avoid the potentially increased hazard of false positives associated with multiple analyses.
• understand the potential hazards and advantages of interim analyses and group sequential and adaptive designs for trials.
• provide a suitable interpretation of any finding of statistical significance or non-significance which takes account of the prior likelihood of a real treatment effect and reflects the clinical significance of the finding.

All in all, the Biostatistics course if very interesting and provides a wide variety of topics to learn. It is also concerned with data analysis and its related problems, helping students to create techniques that address issues of interest to scientists.