Informative Censoring

Informative Censoring is a type of missing data mechanism in longitudinal causal inference where the timing or occurrence of censoring is statistically associated with unobserved outcomes or potential confounders, thereby violating the assumption that censoring is independent of the outcome given observed covariates.

158

Publications

13.4K

Citations

546

Authors

291

Institutions

Publications per year

2017–2026

40

Variants

Dependent CensoringNon-ignorable Censoring

Authors

546

  1. JS
    Jianguo Sun

    University of Missouri, United States

    7 publications · 194 citations

    7

    h-index

  2. MW
    Margaret Wu

    National Heart Lung and Blood Institute, United States

    6 publications · 1.2K citations

    6

    h-index

  3. JM
    James M. Robins

    Harvard University, United States

    5 publications · 2.9K citations

    5

    h-index

  4. FS
    Fotios Siannis

    MRC Biostatistics Unit, United Kingdom

    4 publications · 165 citations

    4

    h-index

  5. JN
    Jing Ning

    The University of Texas MD Anderson Cancer Center, United States

    3 publications · 184 citations

    3

    h-index

1–5 of 546

Venues

  1. Biometrics

    20 publications · 2.3K citations

    19

    h-index

  2. Statistics in Medicine

    25 publications · 1.9K citations

    18

    h-index

  3. 8

    h-index

  4. Biometrika

    7 publications · 587 citations

    7

    h-index

  5. Biostatistics

    7 publications · 237 citations

    7

    h-index

1–5