LMP2004H: Introduction to Biostatistics

Who can attend

A maximum of 20 students can be enrolled in this course.

Ten of these will be from the MHSc in Laboratory Medicine program, while for the remaining 10 spots priority will be given to students from the research streams at the Department of LMP.

Course description

This course will introduce you to a wide variety of statistical concepts and methods that are commonly used in biological research. 

You will learn how to review different types of data, structure questions of that data using appropriate statistical approaches, and review the use of statistics in scientific literature.

You will be introduced to limitations and assumptions that underly statistical analyses.

By the end of the course, you should be familiar enough with statistics to be avoid most of the following errors in your analysis (or at least know when you need to look up how to avoid them) or spot them in studies you may read:

  • Failures to clearly state hypotheses being tested
  • Failures to check the accuracy of data being used for analysis
  • Failures to describe the statistical tests being used, or to properly describe the software/algorithms used to perform statistical testing
  • Failures to understand the appropriateness of a statistical test for the problem at hand
  • Failure to use control groups properly
  • Misuse of one-tailed/two-tailed tests
  • Misuse of paired/unpaired tests
  • Failure to assess sample sizes and power calculations for the given experiments
  • Confusing standard deviation and standard errors
  • Use of multiple tests without using multiple testing corrections
  • Misinterpretation of P-values, and mistaking statistical significance for importance
  • Misleading use of data visualization
  • Incorrect interpretations of sensitivity and specificity

Structure

You will be taught in twelve three-hour sessions:

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

Course coordinator

Dr. Paul Krzyzanowski

paul.krzyzanowski@oicr.on.ca

lmp.grad@utoronto.ca for administrative queries.

Timings and location

Wednesdays 9 am – 12 pm

Location: Synchronous online delivery (Zoom)

Evaluation methods

Participation – 10%

Weekly assignments - 50% (5% each. Best 10 out of 11 for Weeks 2 - 12)

Mid-term exam - 20%: A mid-term examination will cover material up the lecture prior to the exam date.

Final examination - 20%

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.

Software

Access to and familiarity with a Windows computing environment is required for this course. Tutorials will present topics in the RStudio Desktop environment, with possible manipulation of data tables in Excel.

RStudio Desktop is available as an open source package and downloadable from the RStudio website.

Schedule

Lecture - date TBC

Topic

Lecture 1

Introduction to course

Basic statistical concepts

Basic descriptive statistics

Tutorial: Intro to RStudio

Lecture 2

Types of data, continuous and discrete distributions

More on descriptive statistics Outliers

Lecture 3

Probability

Lecture 4

Hypothesis Testing

Type I/II Errors

Parametric, Nonparametric tests (KS), Concordance, Power, sample sizing, effect sizes

Multiple testing corrections

Lecture 5

Analysis of variance

Lecture 6

Correlations

Lecture 7

Midterm

Regressions I 

No Tutorial Today

Lecture 8

Regressions II

Lecture 9

Odds Ratio, Relative Risk, Attributable Risk, Number Needed to Treat Sensitivity/Specificity/ROC curves

PPV/NPV

Lecture 10

Serial Measurements, Time Series

Lecture 11

Dose Response curves

Survival Analysis

Lecture 12

Resampling statistics

Permutations, Monte Carlo methods

Lecture 13

Final exam

Text and readings

 The main material for the course will consist of the slides presented in lectures, and these are based on material found in the online textbook: Hoffman et al. Basic biostatistics for medical and biomedical practitioners. 2019