Initial Results in Alzheimer's Disease Progression Modeling Using Imputed Health State Profiles

Fleet, B.D.; Deller, James; Goodman, Erik D.

doi:10.1109/csci.2016.0010

Cited by 3 publications

(2 citation statements)

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“…Traditional imputation methods [57,6,19] impute missing values in Group 2 based on the associations between missing and observed variables obtained from complete observations in Group 1, while samples in Groups 3 and 4 are not utilized. However, Groups 3 and 4, also containing values from Source 3, can provide additional information in imputing missing variables X m(r) through correlations with other covariates.…”

Section: Background and Motivationmentioning

confidence: 99%

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Integrating Multisource Block-Wise Missing Data in Model Selection

Xue

2020

Journal of the American Statistical Association

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For multi-source data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this paper, we propose a Multiple Block-wise Imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and optimally integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random.Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms.

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Section: Background and Motivationmentioning

confidence: 99%

“…The single regression imputation (SI) method [2,57,6,19,39] is another popular approach which predicts missing values through regression using observed variables as predictors. Suppose that the subjects from multi-source data are divided into groups according to their missing patterns.…”

Section: Introductionmentioning

confidence: 99%