Introduction to handling missing outcome and cost data in the economic evaluation of randomised controlled trials

Rita Faria; David M. Epstein

Introduction to handling missing outcome and cost data in the economic evaluation of randomised controlled trials

Rita Faria, David M. Epstein

Centre for Health Economics

Research output: Contribution to conference › Other

Abstract

Objectives and Outline
The course will provide practical solutions to the specific problems arising from missing and censored data on costs and quality –adjusted life years in randomised controlled trials (RCTs). Missing data reduces the effective sample size and can sometimes bias estimates of the treatment effect. Analysis of RCTs must carefully consider the possible reasons for missing data, and take appropriate account of the missing values in the methods of analysis.

The course will start with an introduction to the theoretical background on missing data, followed by a brief description of the methods (complete case, mean imputation, conditional mean imputation, multiple imputation and inverse probability weighting (Lin-Willan method). Practical applications of the methods will be discussed, illustrated with examples from published trials.

Original language	English
Publication status	Unpublished - 2012
Event	9th Health Technology Assessment International (HTAi) Annual Meeting - Bilbao, Spain Duration: 23 Jun 2012 → 27 Jun 2012 http://www.htai2012.org/

Conference

Conference	9th Health Technology Assessment International (HTAi) Annual Meeting
Abbreviated title	HTAi
Country/Territory	Spain
City	Bilbao
Period	23/06/12 → 27/06/12
Internet address	http://www.htai2012.org/

Cite this

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title = "Introduction to handling missing outcome and cost data in the economic evaluation of randomised controlled trials",

abstract = "Objectives and OutlineThe course will provide practical solutions to the specific problems arising from missing and censored data on costs and quality –adjusted life years in randomised controlled trials (RCTs). Missing data reduces the effective sample size and can sometimes bias estimates of the treatment effect. Analysis of RCTs must carefully consider the possible reasons for missing data, and take appropriate account of the missing values in the methods of analysis. The course will start with an introduction to the theoretical background on missing data, followed by a brief description of the methods (complete case, mean imputation, conditional mean imputation, multiple imputation and inverse probability weighting (Lin-Willan method). Practical applications of the methods will be discussed, illustrated with examples from published trials.",

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note = "9th Health Technology Assessment International (HTAi) Annual Meeting, HTAi ; Conference date: 23-06-2012 Through 27-06-2012",

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T1 - Introduction to handling missing outcome and cost data in the economic evaluation of randomised controlled trials

AU - Faria, Rita

AU - Epstein, David M.

PY - 2012

Y1 - 2012

N2 - Objectives and OutlineThe course will provide practical solutions to the specific problems arising from missing and censored data on costs and quality –adjusted life years in randomised controlled trials (RCTs). Missing data reduces the effective sample size and can sometimes bias estimates of the treatment effect. Analysis of RCTs must carefully consider the possible reasons for missing data, and take appropriate account of the missing values in the methods of analysis. The course will start with an introduction to the theoretical background on missing data, followed by a brief description of the methods (complete case, mean imputation, conditional mean imputation, multiple imputation and inverse probability weighting (Lin-Willan method). Practical applications of the methods will be discussed, illustrated with examples from published trials.

AB - Objectives and OutlineThe course will provide practical solutions to the specific problems arising from missing and censored data on costs and quality –adjusted life years in randomised controlled trials (RCTs). Missing data reduces the effective sample size and can sometimes bias estimates of the treatment effect. Analysis of RCTs must carefully consider the possible reasons for missing data, and take appropriate account of the missing values in the methods of analysis. The course will start with an introduction to the theoretical background on missing data, followed by a brief description of the methods (complete case, mean imputation, conditional mean imputation, multiple imputation and inverse probability weighting (Lin-Willan method). Practical applications of the methods will be discussed, illustrated with examples from published trials.

M3 - Other

T2 - 9th Health Technology Assessment International (HTAi) Annual Meeting

Y2 - 23 June 2012 through 27 June 2012

ER -