Upcoming Online Short Courses at the Institute of Clinical Trials and Methodology. A few free places and discounts are offered to applicants from LMICs.

Missing Data Short Course

23-27 June | Hybrid and face-to-face | University of Witwatersrand | Apply

Sub-Saharan Africa Consortium for Advanced Biostatistics Training (SSACAB) in collaboration with the London School of Hygiene & Tropical Medicine, the University of KwaZulu Natal and the Witwatersrand University will be offering a short course on missing data methods.
Missing data frequently occurs in both observational and experimental research. They lead to a loss of statistical power, but more importantly, may introduce bias into the analysis. In this course, we adopt a principled approach to handling missing data, in which the first step is a careful consideration of suitable assumptions regarding the missing data for a given study. Based on this, appropriate statistical methods can be identified that are valid under the chosen assumptions.
The overall aim of this course is for participants to learn about how the method of multiple imputation can be used to handle missing data in statistical analyses and to understand the assumptions under which this is valid. In addition to introducing the method in more standard settings, we will explore its use in a range of more advanced situations, including in the presence of non-linearities and interactions, propensity score analysis, prognostic modeldevelopment, and for performing sensitivity analyses.

WHO SHOULD APPLY?
Postgraduate students in Statistics, Biostatistics, academics and researchers whose work require the use of statistical methods to account for missing data intheir research.A strong background in statistics is required.

COURSE OBJECTIVES

  • The effects of missing data on statistical inferences
  • Missingness mechanism assumptions include missing completely at random, missing at random, and missing not at random.
  • Multiple imputation for missing data, based on joint models and fully conditional specification approaches, and Rubin's pooling rules.
  • Multiple imputation accommodating non-linearities and interactions.
  • Multiple imputation for sensitivity analysis.Multiple imputation in the context of propensity score analysis.
  • Multiple imputation in the context of prognostic model development and deployment.


Missing Data short course information sheet


APPLY FOR THE MISSING DATA SHORT COURSE

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