Bias in Observational Studies (schedule)
Instructor: Dr. Ian Dohoo, Professor Emeritus, CVER, AVC, UPEI
Instructor: Dr. Simon Dufour, Professor, University of Montreal
Instructor: Dr. Henrik Stryhn, Professor, CVER, AVC, UPEI
This course covers 2 topics essential for producing valid results from observational data:
– bias (and quantitative bias analysis)
– use of Bayesian methods to incorporate bias correction into the study analyses
In the first section, we will review the three fundamental types of bias (confounding, selection bias, and information bias) including causes of the bias, approaches to preventing the bias and an evaluation of the potential impact these biases could have on study results. Given that not all biases can be prevented, it is important to know how to deal with biases which may affect a study. Two general approaches will be presented. Probabilistic quantitative bias analysis is a post-hoc approach which allows an investigator to apply knowledge about factors which may have biased a study in order to adjust observed estimates of effects (eg odds ratios) to remove the bias effects. While it does allow for adjustment for multiple biases and for uncertainty in bias parameters estimates, probabilistic quantitative bias analysis is usually applied to models that can be summarized by a 2×2 table.
Bayesian methods allow for incorporation of bias parameters directly during the analysis phase and, consequently, can be applied to more complex models. For instance multivariable logistic regression model (ie models with more than one predictor, including continuous predictors), mixed models (ie models with random effects), etc can be run with these. Using the Bayesian methods allows an investigator to compute unadjusted and bias-adjusted point estimate and 95% CI that will, hopefully, be closer to the true counterfactual effect. At the very least, it would allow for estimating the biases direction and magnitude.
The course is planned to run all days 12 – 4 pm ET during Aug 23 – Sep 3, excluding the weekend. Online participation will be possible, and in-person participation may be possible as well.
Software used will be a combination of spreadsheets (provided) and R programs. Participants will need to have some proficiency in R (primarily for the Bayesian analyses). Guidance in developing this proficiency will be provided.
The following registration fees apply (amounts are in $Cdn and include taxes and administration fees):
Participant Course Fees Student* 550 Non-student 1000
*Proof of enrollment in a graduate program at a university (not necessarily UPEI) required.
Click on the following Eventbrite logo to register for the Epi on the Island Summer Course 2021.
The online registration will only accept a limited number of participants, and if interest should exceed the number of seats initially available, a waiting list will be established.
For further inquiries, including requests to be put on a waiting list, please contact Jenny Yu at firstname.lastname@example.org.