Course Overview
This 5 day course is aimed at PhD students and researchers engaged in applied health and social research to assess effects of interventions or risk factors.
The course provides a comprehensive introduction to causal inference and evaluation, covering study designs and statistical analysis methods that enable the principled assessment of causal effects of treatments or risk factors. Throughout the emphasis will be on introducing modern methods from the causal inference field that are readily accessible to the end-user. Their utility will be demonstrated by example analyses of real data sets drawn largely, but not exclusively from mental health research, and using the general purpose statistical software STATA.
The course will be of interest to statisticians, researchers from all areas of applied health research, behavioural and social sciences, mathematical and natural sciences and economics who wish to acquire and/or consolidate skills and knowledge of advanced research methods.
Course Description
Much of applied health and social research is aimed at estimating causal effects of risk factors or interventions (treatments, programmes, policies etc.). This includes analyses and assessments carried out by research organisations to inform their strategies and help with their management and funding decisions (policy and programme evaluation).
However, it is not always clear whether the quantity targeted in a particular analysis/assessment has a causal interpretation and even if that is the case, whether it can be estimated without bias from the study data. For example, findings from observational studies are frequently criticised for lacking confounding control. Similarly non-adherence with randomly allocated treatments can bias efficacy assessments in trials.
This workshop will review statistical designs and analyses that enable valid causal effect estimation, including propensity scoring in large observational studies, methods for dealing with non-compliance in trials, mediation analysis and some quasi-experimental designs. We will focus on methods that are easily accessible to the applied researcher. Throughout methods will be motivated and demonstrated with real data examples, typically from mental health research.
The workshop will use STATA although analyses are easily translatable to other general purpose statistical software packages.
Teaching will be through lectures and hands on computer lab sessions interspersed with discussions. Practical sessions will involve the analyses and interpretation of practice data sets. STATA syntax will be provided and explained but some familiarity with a syntax based software (R, STATA, SAS) is advised.
A short 1.5 h introduction to STATA will be provided at the beginning of the course.
Successful applicants will need to bring a laptop to the session with STATA preloaded.
Instructors
- Professor Sabine Landau
- Mr Nick Magill
- Dr Cedric Ginestet
- Dr Ioannis Bakolis
- Dr Kimberly Goldsmith
- Professor Richard Emsley
Academic Lead: Sabine Landau, Professor of Biostatistics
Date: Monday 19th February - Friday 23rd February 2018
Time: 09:30-17:30
Cost: £250 - 500
Please click here for more information and booking.
Application: To apply please email iop-biostatisticseducation@kcl.ac.uk with the following details:
Email Subject Line: Application for Causal Modelling and Evaluation: A Practical Hands-on Workshop 2018
Name:
Email Address:
Contact Phone Number:
- Are you affiliated with KCL and/or King's Health Partners?
- If Yes, indicate how you are affiliated with KCL and/or King's Health Partners
- Indicate your education/employee status: KCL PhD, KCL student, KCL staff, King's Health Partners affiliate, External Student or External
- In 100 words, state why you wish to enrol/participate in this course:
- In 100 words, state which skills you hope to acquire:
Once your application has been approved, you will be sent a link to payment and a discount code if one is to be applied.