Regularized approach for data missing not at random.
It is common in longitudinal studies that missing data occur due to subjects' no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data missing not at random (MNAR) have to be considered. However, models for data MNAR ofte...
| Publicado en: | Statistical Methods in Medical Research Vol. 28; no. 1; pp. 134 - 151 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133860292&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133860292 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Jan2019 vid: 28 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 133860292 133860292 NLM28671033 133860292 10.1177/0962280217717760 NLM28671033 133860292 ppf: 134 ppct: 17 formats: tig: atl: Regularized approach for data missing not at random. aug: au: Tseng, Chi-hong Chen, Yi-Hau affil: Department of Medicine, University of California, Los Angeles sug: subj: Data Analysis, Statistical Scleroderma, Systemic Complications Patient Dropouts Statistics and Numerical Data Cough Etiology Models, Statistical Prospective Studies Regression Probability Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source ab: It is common in longitudinal studies that missing data occur due to subjects' no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data missing not at random (MNAR) have to be considered. However, models for data MNAR often suffer from the identifiability issue and hence result in difficulty in estimation and computational convergence. To ameliorate this issue, we propose the LASSO and ridge-regularized selection models that regularize the missing data mechanism model to handle data MNAR, with the regularization parameter selected via a cross-validation procedure. The proposed models can be also employed for sensitivity analysis to examine the effects on inference of different assumptions about the missing data mechanism. We illustrate the performance of the proposed models via simulation studies and the analysis of data from a randomized clinical trial. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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