LMP2004H: Introduction to Biostatistics

Who can attend

A maximum of 15 students can be enrolled in this course.

We will prioritize students enrolled in the LMP graduate program.

Course description

This course will introduce graduate students in the Health Sciences to Biostatistics, focusing on topics and applications present in Laboratory Medicine and Pathobiology.

You will become familiar with fundamental concepts of biostatistical methods and their application to real research problems, such as:

  • types of data
  • descriptive statistics
  • probability distributions
  • hypothesis testing
  • correlation
  • linear regression analysis of variance.

You will use the R software for practical examples and practice exercises. We will provide tutorials on how to use the R software.

Acquiring an understanding of basic but essential concepts in biostatistics, you will develop the ability to apply these concepts and methods to practical applications.

By the end of the course, you should be able to:

  • Identify different types of data and use the appropriate descriptive statistics
  • Identify and apply the correct statistical tests for a given problem
  • Understand the assumptions behind the statistical tests and methods
  • Select and use the proper functions using the R statistical software
  • Critique the use of statistical tests and methods for a given data set and/or in a published study

Structure

You will be taught in three-hour sessions:

  • 1.5 hours: lecture
  • 1.5 hours: hands on tutorial, discussing practical examples using the R software.

Course coordinator

To be announced

lmp.grad@utoronto.ca for administrative queries.

Timings and location

To be announced

Location: Synchronous online delivery (Zoom)

Evaluation methods

Assignments - 40%: You will be given bi-weekly (approximately) assignments covering conceptual questions and practical exercises using the R software.

Mid-term exam - 25%: A mid-term examination will cover material up the lecture prior to the exam date.

Final examination - 35%

The final exam will cover material taught in class.

It will be a combination of multiple-choice and free-text questions.

Late assignment policy

A 10% reduction of the grade will be applied for every day of late submission.

Missed exam policy

If you miss an examination and would like to write a make-up exam, you must submit a letter stating the reason for the request and provide support for it such as a note from a physician or other relevant documentation.

You must submit this within one week of missing the exam. Failure to do so will result in a zero mark for the evaluation.

Schedule

lecture - date tbc Topic
Lecture 1 Types of data and proper descriptive statistics
Lecture 2 Graphing the data: different kinds of plots to explore patterns in data
Lecture 3 Probability concepts: independent events, conditional probabilities, properties
Lecture 4 Distributions: discrete and categorical, some distributions, expected value, variance
Lecture 5 Estimation: central limit theorem, confidence intervals, standard errors
Lecture 6 Principles of Hypothesis testing: Type I and II errors, Power, p-values Mid-term quiz
Lecture 7 Analysis of categorical data: Chi squared tests, 2 by 2 tables, rates
Lecture 8 Analysis of continuous data: t-tests (Unpaired, paired)
Lecture 9 Analysis of variance: One-way ANOVA, Two-way ANOVA
Lecture 10 Correlation and Linear regression: simple, multiple
Lecture 11 Non-parametric and distribution-free statistics
Lecture 12 Sample size considerations
Lecture 13 Final exam