### Abstract

Linear regression is one of the most popular statistical techniques. In linear regression analysis, missing covariate data occur often. A recent approach to analyse such data is a weighted estimating equation. With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse 'probability of being observed'. In this paper, we propose a weighted estimating equation in which we wrongly assume that the missing covariates are multivariate normal, but still produces consistent estimates as long as the probability of being observed is correctly modelled. In simulations, these weighted estimating equations appear to be highly efficient when compared to the most efficient weighted estimating equation as proposed by Robins et al. and Lipsitz et al. However, these weighted estimating equations, in which we wrongly assume that the missing covariates are multivariate normal, are much less computationally intensive than the weighted estimating equations given by Lipsitz et al. We compare the weighted estimating equations proposed in this paper to the efficient weighted estimating equations via an example and a simulation study. We only consider missing data which are missing at random; non-ignorably missing data are not addressed in this paper.

Original language | English |
---|---|

Pages (from-to) | 2421-2436 |

Number of pages | 16 |

Journal | Statistics in Medicine |

Volume | 21 |

Issue number | 16 |

DOIs | |

State | Published - Aug 30 2002 |

Externally published | Yes |

### Fingerprint

### Keywords

- Missing at random
- Missing completely at random
- Missing data mechanism

### ASJC Scopus subject areas

- Epidemiology

### Cite this

*Statistics in Medicine*,

*21*(16), 2421-2436. https://doi.org/10.1002/sim.1195

**A weighted estimating equation for linear regression with missing covariate data.** / Parzen, Michael; Lipsitz, Stuart R.; Ibrahim, Joseph G.; Lipshultz, Steven E.

Research output: Contribution to journal › Article

*Statistics in Medicine*, vol. 21, no. 16, pp. 2421-2436. https://doi.org/10.1002/sim.1195

}

TY - JOUR

T1 - A weighted estimating equation for linear regression with missing covariate data

AU - Parzen, Michael

AU - Lipsitz, Stuart R.

AU - Ibrahim, Joseph G.

AU - Lipshultz, Steven E

PY - 2002/8/30

Y1 - 2002/8/30

N2 - Linear regression is one of the most popular statistical techniques. In linear regression analysis, missing covariate data occur often. A recent approach to analyse such data is a weighted estimating equation. With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse 'probability of being observed'. In this paper, we propose a weighted estimating equation in which we wrongly assume that the missing covariates are multivariate normal, but still produces consistent estimates as long as the probability of being observed is correctly modelled. In simulations, these weighted estimating equations appear to be highly efficient when compared to the most efficient weighted estimating equation as proposed by Robins et al. and Lipsitz et al. However, these weighted estimating equations, in which we wrongly assume that the missing covariates are multivariate normal, are much less computationally intensive than the weighted estimating equations given by Lipsitz et al. We compare the weighted estimating equations proposed in this paper to the efficient weighted estimating equations via an example and a simulation study. We only consider missing data which are missing at random; non-ignorably missing data are not addressed in this paper.

AB - Linear regression is one of the most popular statistical techniques. In linear regression analysis, missing covariate data occur often. A recent approach to analyse such data is a weighted estimating equation. With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse 'probability of being observed'. In this paper, we propose a weighted estimating equation in which we wrongly assume that the missing covariates are multivariate normal, but still produces consistent estimates as long as the probability of being observed is correctly modelled. In simulations, these weighted estimating equations appear to be highly efficient when compared to the most efficient weighted estimating equation as proposed by Robins et al. and Lipsitz et al. However, these weighted estimating equations, in which we wrongly assume that the missing covariates are multivariate normal, are much less computationally intensive than the weighted estimating equations given by Lipsitz et al. We compare the weighted estimating equations proposed in this paper to the efficient weighted estimating equations via an example and a simulation study. We only consider missing data which are missing at random; non-ignorably missing data are not addressed in this paper.

KW - Missing at random

KW - Missing completely at random

KW - Missing data mechanism

UR - http://www.scopus.com/inward/record.url?scp=0037199838&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037199838&partnerID=8YFLogxK

U2 - 10.1002/sim.1195

DO - 10.1002/sim.1195

M3 - Article

VL - 21

SP - 2421

EP - 2436

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 16

ER -