This 1-day course will be online and consist of 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 08:30 and end at 17:30, including time for coffee breaks and lunch.

By the end of the course participants will be able to understand the concept of conducting a quantitative bias analysis to account for:

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.

The course will include:

1. a brief revision of unmeasured sources of bias due to unmeasured confounding, non-random selection into a study, and measurement error/misclassification;
2. introduction to the principles of a quantitative bias analysis;
3. guidance on conducting a quantitative bias analysis to unmeasured confounding, selection bias, and information bias; and
4. information on available software implementations of quantitative bias analysis methods.

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.

To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:

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.

Please remember that we do not charge fees for pilot courses, nor do they count against your allocation of free course places. However, in return we ask that you take the time to provide full and thorough feedback so we can effectively evaluate the success of 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.

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. 

Please note that this is a pilot course and therefore no Materials & Recordings (UoB only) option is available.

100% of attendees recommend this course*.
*Attendee feedback from March 2024.

Here is a sample of feedback from the course: