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st: Fwd: Practical course on handling missing data in STATA


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Fwd: Practical course on handling missing data in STATA
Date   Fri, 6 Mar 2009 09:05:13 -0600

---------- Forwarded message ----------
From: Brian Francis <b.francis@lancaster.ac.uk>
Date: Mar 6, 2009 8:33 AM
Subject: Practical course on handling missing data in STATA
To: ECONOMETRIC-RESEARCH@jiscmail.ac.uk


Title: HANDLING MISSING DATA
 Statistical modelling with missing data using multiple imputation and
 inverse probability weighting

 Presenter: Dr James Carpenter

 Dates of Course: Monday 30th March - Wednesday 1st April 2009

 Cost for three day course: £75 postgraduate students/£150 Academic staff
 from higher education including notes, lunches, teas and coffees and a
 computer account.

 Venue: Postgraduate Statistics Centre, Lancaster University (For more info
 and costs for non-HE personnel see http://www.maths.lancs.ac.uk/missing-
 data )

 Overview:

 This subsidised short course will provide an introduction to the issues
 raised by missing data and statistical modelling with missing data. In
 particular, participants will gain an understanding of multiple imputation
 as a tool for handling missing data.
 They will learn how to implement this method through a series of practical
 computing exercises with example datasets in Stata and MLwiN.

 The course will have a strong practical focus with six computer sessions to
 consolidate the ideas presented in the lectures, and to gain experience with
 the various methods.

 Target audience:

 Social Scientists, epidemiologists, biostatisticians and other researchers
 with strong quantitative skills and substantial experience in statistical
 analysis including familiarity with multivariable regression methods. During
 computing practical sessions the participants will be provided with
 computing code, solutions and assistance.

 It is strongly recommended that participants are familiar with Stata, and to
 a lesser extent MLwiN.

 More detail:

 The main objectives of the course are:

    * To introduce the key concepts underpinning the analysis of partially
 observed data, together with a principled approach to the analysis;
    * To explain the shortcomings of frequently used ad-hoc methods
    * To introduce mulitple imputation, and gain familiarity with using the
 ICE software in Stata for multiple imputation,
      and the MLwiN software for multiple imputation, using simple and more
 complex examples
    * To explore the role of sensitivity analysis, and methods for
 performing approximate sensitivity analysis
    * To introduce inverse probability weighting and doubly robust estimation.

 Course Content:

 The course will consist of 6 sessions, each of which comprises a 1-h lecture
 followed by a short discussion and then a 1.5h computer practical. The key
 topics covered will be:

    * Session I: Introduction, issues raised by missing data, and towards a
 systematic approach
    * Session II: Shortcomings of ad-hoc methods; introduction to multiple
 imputation
    * Session III: Further issues in multiple imputation
    * Session IV: Multilevel multiple imputation
    * Session V: Sensitivity analysis
    * Session VI: Inverse probability weighting and doubly robust estimation.

 Computer workshops will enable course participants to put the methods into
 practice. The course will use the packages Stata (80%) and Mlwin (20%).


 Course Materials:

 Participants will receive written course notes.

 The Instructor:
 James Carpenter is a Reader in Medical and Social Statistics at the London
 School of Hygiene & Tropical Medicine (University of London). His main
 research interest is missing data, and developing and applying multiple
 imputation. He has led missing data courses in the UK and abroad, including
 under the ESRC's Researcher Development Initiative.


 Preparatory Reading:

    * Participants would benefit from reviewing the introductory material on
 www.missingdata.org.uk (under Getting Started)
    * See also Kenward, M. and Carpenter, J (2007): Muliple Imputation:
 Current perspectives. Statistical Methods in Medical Research, 16,
 199-218


-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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