Multi-level Survival Models

Multi-Level Survival Models is a statistical modeling framework that extends standard survival analysis by incorporating hierarchical or nested data structures, allowing for the estimation of time-to-event outcomes while accounting for correlated failures arising from multiple levels of clustering or random effects.

137

Publications

9.2K

Citations

391

Authors

250

Institutions

Publications per year

2017–2026

32

Authors

391

  1. JM
    Juan Merlo

    Lund University, Sweden

    4 publications · 178 citations

    4

    h-index

  2. JJ
    Jay J. Rotella

    Montana State University, United States

    3 publications · 102 citations

    3

    h-index

  3. KK
    Kelvin K.W. Yau

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 177 citations

    3

    h-index

  4. DZ
    Donglin Zeng

    University of North Carolina at Chapel Hill, United States

    3 publications · 89 citations

    3

    h-index

  5. PC
    Peter C. Austin

    University of Toronto, Canada

    3 publications · 610 citations

    3

    h-index

1–5 of 391

Venues

  1. Statistics in Medicine

    11 publications · 593 citations

    11

    h-index

  2. Lifetime Data Analysis

    7 publications · 244 citations

    7

    h-index

  3. Biometrics

    6 publications · 1.1K citations

    6

    h-index

  4. Technometrics

    4 publications · 266 citations

    4

    h-index

  5. The Auk

    4 publications · 233 citations

    4

    h-index

1–5