Introduction to Quantitative Bias Analysis
Data analyses usually make assumptions (which may be explicit or, more commonly, implicit): for example, “no unmeasured confounding”. When assumptions are untestable their potential importance can only be addressed through a quantitative bias analysis (also known as a sensitivity analysis). This course will introduce you to quantitative bias analysis methods that have been developed to account for unmeasured sources of bias due to confounding, non-random selection into a study, and measurement error/misclassification.
| Date | 3 - 4 June 2025 |
|---|---|
| Fee | £440 |
| Format | Online |
| Audience | Open to all applicants (prerequisites apply) |
Course profile
This course is an introduction to quantitative bias analysis (also known as a sensitivity analysis) to unmeasured sources of bias.
Please click on the sections below for more information.
Structure
This 2-day course will be online and consist of live and pre-recorded lectures, live summary plus question & answer sessions, and live computer practical exercises. Each pre-recorded lecture will be followed by a live session which will summarise the main points of the lecture and allow participants to ask questions about the contents of the lecture.
The course is timetabled to start at 09:30 and end at 16:30, including time for coffee breaks and lunch.
Intended Learning Objectives
By the end of the course participants will be able to understand the concept of conducting a quantitative bias analysis to account for:
- unmeasured confounding;
- non-random selection into a study;
- measurement error/misclassification; and
- multiple sources of bias (i.e., multi-bias analysis).
Target audience
The course is intended for statisticians, health economists, epidemiologists and other researchers who are involved in performing statistical analyses of epidemiological datasets.
It is assumed that participants will have attended the Causal Inference in Epidemiology: Concepts and Methods short course (or an equivalent course). Participants should be familiar with the concept of unmeasured confounding, selection bias, and information bias; standard regression methods for dichotomous and continuous outcomes beyond the basic introductory level; and using software packages, Stata or R, for statistical analyses of the data.
Outline
The course will include:
- a brief revision of unmeasured sources of bias due to unmeasured confounding, non-random selection into a study, and measurement error/misclassification;
- introduction to the principles of a quantitative bias analysis;
- guidance on conducting a quantitative bias analysis to unmeasured confounding, selection bias, and information bias;
- information on available software implementations of quantitative bias analysis methods; and
- guidance on conducting a multi-bias analysis.
Teaching staff
Teaching staff will be drawn from researchers in causal inference at the Department of Population Health Sciences and the MRC Integrative Epidemiology Unit. Course organisers, Dr Rachael Hughes and Dr Emily Kawabata, are actively working in the research area of quantitative bias analysis.
Prerequisites
To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:
| Knowledge | Prior attendance of the Causal Inference in Epidemiology: Concepts and Methods short course (or an equivalent course) is recommended. Participants should be familiar with the concepts of unmeasured confounding, sample selection bias and information bias, and with standard regression methods for continuous and binary outcomes beyond a basic level. |
|---|---|
| Software |
We will be using Posit Cloud* as an interface for R in the practical sessions. Alternatively you can use your own desktop version of R.
Where possible we will provide the practical sessions in Stata** as well. If you wish to use Stata, you must have version 13 or later installed in advance of the course. *Go to R Installation Instructions for help getting set up. **Internal University of Bristol participants are given access to Stata. Go to Stata Installation Instructions (internal only) for help setting it up before the start of the course. External participants are responsible for providing their own access to Stata, however if you are an employee of a university or another institution you may be able to get a short term free Evaluate license. If you are a student, Stata offer a short term free Student licence (one week). |
| Recommendation | For the computer practicals, we recommend that participants either have access to the use of two screens or the ability to print materials in advance of the course. |
Bookings
Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.
Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.
For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.
Course materials
Participants are granted access to our virtual learning platform (Blackboard) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.
To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.
All course participants retain access to the online learning materials and recordings for 3 months after the course.
University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.
Testimonials
100% of attendees recommend this course*.
*Attendee feedback from March 2024.
Here is a sample of feedback from the course:
"Really useful background to all things QBA, with some nice applied examples in R of existing packages" - Course feedback, March 2024
"Really well structured, good overview, good signposting to further resources/reading. Presenters clearly very knowledgeable about the topic" - Course feedback, March 2024
"Really useful overview of a field I didn't know much about previously - and useful further resources if I ever venture into applying this" - Course feedback, March 2024
"An overview of the (generally four) different methods used to quantify bias. Made things a bit clearer about packages that I'd heard of before, e.g. sensemakR" - Course feedback, March 2024
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Note: Bookings close 2 weeks before the course start date.
Really well structured, good overview, good signposting to further resources/reading. Presenters clearly very knowledgeable about the topic
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