Causal Inference in Epidemiology: Recent Methodological Developments, Short Course

  • N/A
    Application Deadline
  • 5 days
    Duration
  • Tuition
    1105
    Tuition (Module)
    1105
    Tuition (Module)
  • English (take IELTS)
    Language
,
Most courses on statistical modelling in epidemiology concentrate on the use of regression models to estimate causal effects while adjusting for measured confounders. These address uncertainty due to sampling error but not, in general, other sources of error and uncertainty which may arise from missing data, measurement error, uncontrolled confounding, and/or selection bias.
  • Overview
  • Programme outline
  • Key facts
  • Admission requirements
  • Fees and funding

About

Most courses on statistical modelling in epidemiology concentrate on the use of regression models to estimate causal effects while adjusting for measured confounders. These address uncertainty due to sampling error but not, in general, other sources of error and uncertainty which may arise from missing data, measurement error, uncontrolled confounding, and/or selection bias. Recent methodological advances make it feasible to incorporate at least some of these sources of error into statistical models so that quantitative assessments can be made of their impact on estimates of causal effect and the uncertainty around those estimates. The proposed course will discuss the current state of the art with respect to these issues, while retaining a practical focus. Participants will acquire awareness of the common threads across these new methods and competence in applying them in simple settings.

Who should apply?

Participants will be expected to be numerate epidemiologists, or applied statisticians with an interest in epidemiology and clinical trials. An MSc Degree in Epidemiology or Medical Statistics, or previous attendance to the LSHTM Advanced Course in Epidemiological Analysis (ACEA), would be an advantage.

Detailed Programme Facts

  • More details: 10 - 14 November 2014
  • Full-time duration 5 days
  • Study intensity Full-time
  • Languages
    • English
  • Delivery mode
    On Campus
  • Entry Level
    Master

Programme Structure

The topics covered will be:

  • Causal diagrams and the backdoor criterion for the identification of confounders
  • Missing data and measurement error mechanisms as components of causal diagrams
  • Methods to deal with the bias introduced by measured and unmeasured confounders, measurement error and missing data. These will include: regression calibration, propensity score and instrumental variable methods, multiple imputation, structural equation models, inverse probability weighting of marginal structural models, multivariate modelling, and sensitivity analysis.
  • Practical experience of the above methods in Stata.

Course Certificate and Assessment

There will be no formal assessment. Participants will receive a Certificate of Attendance.

Lecturers

Bianca DeStavola

Simon Cousens

Rhian Daniel

Academic Requirements

Participants will be expected to be numerate epidemiologists, or applied statisticians with an interest in epidemiology and clinical trials. An MSc Degree in Epidemiology or Medical Statistics, or previous attendance to the LSHTM Advanced Course in Epidemiological Analysis (ACEA), would be an advantage.

Tuition Fee Per Module

  • GBP 1105 International
  • GBP 1105 EU/EEA

Funding

Travel and Accommodation
The School cannot organise travel or provide accommodation for participants. A list of hotels and other accommodation located in the vicinity of the School can be provided on request to Registry.

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