Literature DB >> 7981401

Regression analysis of grouped survival data with incomplete covariates: nonignorable missing-data and censoring mechanisms.

S G Baker1.   

Abstract

Previous methods for the analysis of survival data with incomplete covariates have assumed that the missing-data and censoring mechanisms are ignorable. We relax both assumptions for the analysis of grouped survival data.

Mesh：

Year:  1994        PMID： 7981401

Source DB:  PubMed          Journal:  Biometrics        ISSN： 0006-341X            Impact factor:   2.571


  2 in total

1.  Maximum likelihood estimation with missing outcomes: From simplicity to complexity.

Authors:  Stuart G Baker
Journal:  Stat Med       Date:  2019-08-08       Impact factor: 2.373

2.  Estimating the cumulative risk of false positive cancer screenings.

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Journal:  BMC Med Res Methodol       Date:  2003-07-03       Impact factor: 4.615
  2 in total

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