Causal Inference for Assessing Effectiveness in Real World Data and Clinical Trials: A Practical Hands-on Workshop

5-Day Certified University Course: HTADS - Program on Health Technology Assessment & Decision Sciences
  • Wann 31.01.2022 bis 04.02.2022 (Europe/Berlin / UTC100)
  • Wo Online
  • Kontakt
  • Telefon des Kontakts +43 50-8648-3926
  • Teilnehmer The course is aimed at members of: Healthcare & health policy organizations, national HTA agencies; Regulatory agencies (EMA, FDA, etc.); Pharmaceutical & medical device industry; Academia and research institutions; Health insurances/sickness funds; Consultancy organizations
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The course runs over 5 days and combines lectures on theoretical concepts, discussions, case study exercises, interactive group work and hands-on computer sessions. Practical applications using real world case examples address health interventions from different health technologies and different disease areas. On day 3, participants have an extended break during the afternoon to review course materials, use office hours for questions or consulting for their own research work, catch up on emails, or energize themselves while relaxing. They reconvene on Thursday morning for the next session.
The course will be an online synchronous course (Austrian time zone) scheduled from 9am to 5pm. The course will not be recorded.


  • Introduction to Causal Inference
  • Overview of the problem (causality), causal effects of point actions and time-varying actions, directed acyclic graphs (DAGs), exercises
  • Optional: Stata Tutorial
  • Overview on causal study designs, different causal methods
  • Overview on treatment switching adjustment methods 1: naïve methods, software exercises
  • Overview on treatment switching adjustment methods 2: Inverse probability (of censoring) weighting (IPW) with marginal structural models (MSM), software exercises
  • Adjustment methods 3: Two-stage adjustment, software exercises
  • Overview on treatment switching adjustment methods 4: g-estimation with rank-preserving structural failure time models (RPSFTM), software exercises
  • Recommendations on appropriate methods
  • Optional: Course Examination

In addition, Teaching Assistants will be available for software programming exercises.

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