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## Statistical Analysis with Missing Data (2002)

Citations: | 2674 - 21 self |

### Citations

1810 |
Multiple imputation for nonresponse in surveys.
- Rubin
- 1987
(Show Context)
Citation Context ...for the missing data mechanism (examples of ILmethods include ignorable maximum likelihood (IML), Bayesian inferences, or multiple imputation based on the predictive distribution from aBayesianmodel (=-=Rubin, 1987-=-), as in SAS PROC MI (SAS Institute, 2010) or IVEware (Raghunathan et al., 2001)) and (c) non-ignorable modelling, which derives inference from the likelihood function based on a joint distribution of... |

647 |
Inference and missing data.
- Rubin
- 1976
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Citation Context ...me that themissing data are missing at random (MAR), in the sense that missingness of variables that contain missing values does not depend on themissing values, after conditioning on available data (=-=Rubin, 1976-=-; Address for correspondence: Roderick J. Little, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA. E-mail: rlittle@umich.edu 592 R. J. Littl... |

524 |
Statistical analysis with missing data (2nd ed.).
- Little, Rubin
- 2002
(Show Context)
Citation Context ...orrespondence: Roderick J. Little, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA. E-mail: rlittle@umich.edu 592 R. J. Little and N. Zhang =-=Little and Rubin, 2002-=-). CC analysis involves a loss of information but has the advantage of yielding valid inferences when missingness depends on the missing covariates X but not the response Y , a potentially non-ignorab... |

241 |
Partial likelihood.
- Cox
- 1975
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Citation Context ...ables and can be assumed to be MAR, then SIL on the subsample with measurements within the detection limit yields valid regression inference. Generally, SIL methods are based on a partial likelihood (=-=Cox, 1972-=-) with the component Lign,w.θ/ discarded from the analysis and hence involve a loss of efficiency relative to full likelihood methods. However, they are more efficient than CC analysis and avoid the n... |

166 |
A Language and program for complex Bayesian modeling.
- Gilks, Thomas, et al.
- 1994
(Show Context)
Citation Context ... increased generality. As noted, options for IL include IML, multiple imputation using software like PROC MI or IVEware (Raghunathan et al., 2001) and fully Bayes methods using software such as BUGS (=-=Gilks et al., 1994-=-). Mixing these methods is also advantageous in some settings. The idea of SIL is presented here in the context of likelihood-based analyses, but it also applies to non-likelihood analyses that are va... |

147 | Regression with Missing X’s: A Review.” - Little - 1992 |

128 |
Patten-Mixture models for multivariate incomplete data.
- Little
- 1993
(Show Context)
Citation Context ...ly high. It can be shown by an extension of the arguments in Little and Wang (1996) that, for the data in example 2, the SIL method is in fact full ML for a particular normal pattern–setmixturemodel (=-=Little, 1993-=-). This aspect of SILmethodswill be the subject of future work. The form of IL method in SIL is left unspecified in this paper where possible, for increased generality. As noted, options for IL includ... |

78 | Socio-economic status and blood pressure: an overview analysis. - HM, Hemingway, et al. - 1998 |

28 | Missing Covariates in Generalized Linear Models When the Missing Data Mechanism Is Nonignorable - Ibrahim, Lipsitz, et al. - 1999 |

21 | for Disease and Control and Prevention (2004) National Health and Nutrition Examination Survey Data 2003–2004 - Centers |

12 | Multiple imputation of missing income data in the National Health Interview Survey - Schenker, Raghunathan, et al. - 2006 |

11 | Missing data methods for generalized linear models: a comparative review - Ibrahim, Chen, et al. - 2005 |

8 | Socioeconomic inequality in blood pressure and its determinants: cross-sectional data from Trinidad and - Gulliford, Mahabir, et al. - 2004 |

7 | bayesian Methods for Generalized Linear Models With Missing Covariates.” The Canadian Journal of Statistics - Ibrahim, Chen, et al. - 2002 |

7 | Maximum likelihood inference for multiple regression with missing values: a simulation study - Little - 1979 |

7 | Potential implications of missing income data in population-based surveys: an example from a postpartum survey in California - Kim, Egerter, et al. - 2007 |

6 |
The epidemiologic transition theory
- Mackenbach
- 1994
(Show Context)
Citation Context ...e an important basis for public health interventions. The effect of socio-economic status on blood pressure generally varies by geographical region and time as the risk factors in populations change (=-=Mackenbach, 1994-=-). The data set that is analysed in this paper is from the 2003–2004 NHANES (Centers for Disease Control and Prevention, 2004), which was a survey designed to assess the health and nutritional status ... |

5 | Sieve maximum likelihood estimation for regression models with covariates missing at random - Chen, Zeng, et al. - 2007 |

4 | Theory and inference for regression models with missing responses and covariates - Chen, Ibrahim, et al. - 2008 |

1 | Regression estimates and missing data: complete-case analysis - Glynn, Laird - 1986 |

1 | 2001)Amultivariate technique formultiply imputing missing values using a sequence of regression models - Raghunathan, Lepkowski, et al. |

1 | Institute (2010) Statistical Analysis with SAS/STAT Software - SAS |

1 | interdependence between incomeand education - Tolley, E - 1971 |