### Abstract

Incomplete data are common in many fields of research, and interest often lies in estimating a marginal mean based on available information. This paper is concerned with the comparison of different strategies for estimating the marginal mean of a response when data are missing at random. We evaluate these methods based on the asymptotic bias, empirical bias and efficiency.We show that complete case analysis gives biased results when data are missing at random, but inverse probability weighted estimating equations (IPWEE) and a method based on the expected conditionalmean (ECM) yield consistent estimators. While these methods give estimators which behave similarly in the contexts studied they are based on quite different assumptions. The IPWEE approach requires analysts to specify a model for the missing data mechanism whereas the ECMapproach requires a model for the distribution of auxiliary variables driving the missing data mechanism. The latter can be a challenge in practice, particularly when the covariates are of high dimension or are a mixture of continuous and categorical variables. The IPWEE approach therefore has considerable appeal in many practical settings.

Original language | English (US) |
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Title of host publication | Optimization and Data Analysis in Biomedical Informatics |

Editors | Panos M Pardalos |

Pages | 99-115 |

Number of pages | 17 |

DOIs | |

State | Published - Dec 1 2012 |

### Publication series

Name | Fields Institute Communications |
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Volume | 63 |

ISSN (Print) | 1069-5265 |

### Fingerprint

### ASJC Scopus subject areas

- Mathematics(all)

### Cite this

*Optimization and Data Analysis in Biomedical Informatics*(pp. 99-115). (Fields Institute Communications; Vol. 63). https://doi.org/10.1007/978-1-4614-4133-5_5

**Strategies for bias reduction in estimation of marginal means with data missing at random.** / Chen, Baojiang; Cook, Richard J.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Optimization and Data Analysis in Biomedical Informatics.*Fields Institute Communications, vol. 63, pp. 99-115. https://doi.org/10.1007/978-1-4614-4133-5_5

}

TY - CHAP

T1 - Strategies for bias reduction in estimation of marginal means with data missing at random

AU - Chen, Baojiang

AU - Cook, Richard J.

PY - 2012/12/1

Y1 - 2012/12/1

N2 - Incomplete data are common in many fields of research, and interest often lies in estimating a marginal mean based on available information. This paper is concerned with the comparison of different strategies for estimating the marginal mean of a response when data are missing at random. We evaluate these methods based on the asymptotic bias, empirical bias and efficiency.We show that complete case analysis gives biased results when data are missing at random, but inverse probability weighted estimating equations (IPWEE) and a method based on the expected conditionalmean (ECM) yield consistent estimators. While these methods give estimators which behave similarly in the contexts studied they are based on quite different assumptions. The IPWEE approach requires analysts to specify a model for the missing data mechanism whereas the ECMapproach requires a model for the distribution of auxiliary variables driving the missing data mechanism. The latter can be a challenge in practice, particularly when the covariates are of high dimension or are a mixture of continuous and categorical variables. The IPWEE approach therefore has considerable appeal in many practical settings.

AB - Incomplete data are common in many fields of research, and interest often lies in estimating a marginal mean based on available information. This paper is concerned with the comparison of different strategies for estimating the marginal mean of a response when data are missing at random. We evaluate these methods based on the asymptotic bias, empirical bias and efficiency.We show that complete case analysis gives biased results when data are missing at random, but inverse probability weighted estimating equations (IPWEE) and a method based on the expected conditionalmean (ECM) yield consistent estimators. While these methods give estimators which behave similarly in the contexts studied they are based on quite different assumptions. The IPWEE approach requires analysts to specify a model for the missing data mechanism whereas the ECMapproach requires a model for the distribution of auxiliary variables driving the missing data mechanism. The latter can be a challenge in practice, particularly when the covariates are of high dimension or are a mixture of continuous and categorical variables. The IPWEE approach therefore has considerable appeal in many practical settings.

UR - http://www.scopus.com/inward/record.url?scp=84874341159&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874341159&partnerID=8YFLogxK

U2 - 10.1007/978-1-4614-4133-5_5

DO - 10.1007/978-1-4614-4133-5_5

M3 - Chapter

AN - SCOPUS:84874341159

SN - 9781461441328

T3 - Fields Institute Communications

SP - 99

EP - 115

BT - Optimization and Data Analysis in Biomedical Informatics

A2 - Pardalos, Panos M

ER -