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Pre-Conference Course: Demystifying Causal Inference: Assessing efficacy when patients depart from randomised treatments

 

Date: Sunday 3rd June 2018, 13:00 - 17:00

 

Course Presenters:

Ian White
Professor of Statistical Methods for Medicine, MRC Clinical Trials Unit at UCL
ian.white@ucl.ac.uk  


Sabine Landau

Professor of Bio statistics, King’s College London
sabine.landau@kcl.ac.uk

Course Description:

Randomised trials provide a gold standard design for assessing the effectiveness of an intervention or treatment, based on an intention to treat analysis. However, this suffices only to answer a narrow question about the effectiveness of offering the intervention, based on comparing the average outcome between randomised groups. As described in the ICH E9 draft addendum, other important questions include “what is the effect of actually receiving the intervention?” and “what would be the effect of the intervention in practice?”. To answer these questions, we require different analysis approaches, using methods drawn from the causal inference literature.

This session aims to introduce participants to the concepts of causal inference in randomised trials and the statistical methods used to answer various causal questions. It will focus on worked examples from different clinical areas, modelling issues and the key assumptions, and how these methods can be implemented in standard statistical software. The workshop will increase participant’s understanding of principal stratification described in the ICH E9 draft addendum. The workshop will increase participant’s understanding of principal stratification described in the ICH E9 draft addendum.

The session will start with an introduction to the terminology of causal inference, the analysis of randomised trials following the intention-to-treat principle, and the problem caused by departures from randomised allocation.  We will then introduce alternative estimands including the complier average causal effect, and we will show how these can be estimated by two broad classes of methods. First, we will describe instrumental variables methods, which use randomisation to estimate a causal treatment effect in the presence of non-adherence with allocated treatment: we will illustrate these methods using a quantitative outcome in a mental health trial. Second, we will describe inverse probability weighting methods, which censor data after non-adherence with allocated treatment and then correct for selection bias under a no unmeasured confounders assumption: we will illustrate these methods using a time-to-event outcome in a neurology trial.

The plan for the session is two lectures each followed by a 20-30 minute practical exercise that can be carried out in small groups using pencil and paper. 

Target Audience

We are mainly aiming at trial statisticians with no previous experience of causal inference, so we will present the material both conceptually and mathematically. Non-statisticians with an interest in causal inference in trials will also benefit from the course. No prior knowledge of any particular software package is required.

 

Goals of Workshop

We aim to “demystify causal inference” by:

  • introducing participants to the concepts of causal inference in randomised trials;
  • introducing participants to accessible statistical methods used to answer various causal questions;
  • providing worked examples from different clinical areas;
  • pointing out modelling issues and the key assumptions required;
  • demonstrating how these methods can be implemented in standard statistical software.

Specifically, by the end of the first lecture and practical, participants should be able to:

  • understand the meaning of potential outcomes and the definition of individual and average causal effects;
  • have become familiar with causal estimands for quantifying effectiveness and efficacy of treatments;
  • understand instrumental variables (IV) approaches for estimating efficacy in trials;
  • understand the assumptions made;
  • understand the steps involved in IV estimation;

and by the end of the second lecture and practical, participants should be able to:

  • understand the assumptions of inverse probability weighting (IPW)
  • implement IPW in a cross-sectional setting;
  • understand the steps involved in implementing inverse probability of censoring weighting (IPCW) in a longitudinal setting.

 

Registration

 Registration Type  Cost
 Early Bird – up to 21st March 2018
 280 EUR
 Standard - 22nd March 2018 onwards  320 EUR

Registration for the course is available through the conference registration site. Places on this course will be limited, so book early to avoid disappointment and to take advantage of the early bird discount!  


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