A semiparametric approach for analyzing nonignorable missing data

Xie, Hui; Qian, Yi; Qu, Leming

doi:10.5705/ss.2009.252

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…In this scenario, one may consider using the index of sensitivity to nonignorability methodology, or ISNI, for assessing whether an MAR dropout model is reasonable or more complex MNAR dropout models are required at an early stage of analysis. Recently, Xie, Qian, and Qu (2011) proposed a generalized additive model to measure sensitivity. Their computation can be performed using PROC GAM in SAS or the S-Plus/R function gam.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values

Vallejo

García

Livacic-Rojas

et al. 2011

Multivariate Behavioral Research

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Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured covariance matrix (CPM-U) and the true covariance matrix (CPM-T), multiple imputation-based generalized estimating equations (MI-GEE), and weighted generalized estimating equations (WGEE). Under the missing at random mechanism, the MI-GEE approach was always robust. The CPM-T and CPM-U methods were also able to control the error rates provided that certain minimum sample size requirements were met, whereas the WGEE was more prone to inflated error rates. In contrast, under the missing not at random mechanism, all evaluated approaches were generally invalid. Our results also indicate that the CPM methods were more powerful than the MI-GEE and WGEE methods and their superiority was often substantial. Furthermore, we note that little or no power was sacrificed by using CPM-U method in place of CPM-T, although both methods have less power in situations where some participants have incomplete data. Some aspects of the CPM-U and MI-GEE methods are illustrated using real data from 2 previously published data sets. The first data set comes from a randomized study of AIDS patients with advanced immune suppression, the second from a cohort of patients with schizotypal personality disorder enrolled in a prevention program for psychosis.

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Section: Discussionmentioning

confidence: 99%

Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values

Vallejo

García

Livacic-Rojas

et al. 2011

Multivariate Behavioral Research

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“…One can consider the ignorable nonresponse models for Table 1 as the important baseline models (Rubin, 1976;Copas and Eguchi, 2001;Xie, Qian and Qu, 2011). The MCAR model is represented as the log-linear model without…”

Section: Nonignorable Multinomial Log-linear Modelsmentioning

confidence: 99%

“…To aid the model selection and the assessment of untestable assumptions for the missing data mechanism, several methods for sensitivity analysis have been proposed (Copas and Eguchi, 2001;Molenberghs, Kenward and Goetghebeur, 2001;Baker, Ko and Graubard, 2003;Troxel, Ma and Heitjan, 2004;Vansteelandt, Goetghebeur, Kenward and Molenberghs, 2006;Xie, Qian and Qu, 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of nonignorable nonresponse log-linear models for an incomplete two-way contingency table

Kim¹,

Kim²

2018

STAT SINICA

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A challenging problem in the analysis of an incomplete contingency table is that the use of nonignorable nonresponse models requires explicit specification of missing data mechanism. In this paper we propose a data analytic approach to aid in distinguishing between plausible nonignorable log-linear models for an incomplete contingency table. The proposed method involves the computation of a set of response odds and nonresponse odds that are directly connected with the magnitude of the parameters representing types of nonignorable mechanism assumed in the log-linear models. These odds can be easily estimated from observed counts. We illustrate the performance of the proposed method with simulation and a real data. We also discuss a generalizability of the proposed method in two directions, its applicability for a three-way incomplete contingency table and its applicability for nonignorable nonresponse models other than the log-linear models.

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“…In the literature, this index is well developed for univariate and multivariate normal and non-normal longitudinal data with a possibility of the NMAR dropout [6,19,31]. Qian and Xie [34] and Xie [32] extended the ISNI method to evaluate the potential effect of non-ignorable non-monotone missingness on model parameters in longitudinal data.Also recently, Xie et al [35] studied the ISNI with semi-parametric missing mechanisms.…”

Section: Introductionmentioning

confidence: 98%

An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data

Mahabadi

Ganjali

2012

Journal of Applied Statistics

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A semiparametric approach for analyzing nonignorable missing data

Cited by 13 publications

References 29 publications

Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values

Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values

Assessment of nonignorable nonresponse log-linear models for an incomplete two-way contingency table

An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data

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