Living With Survival Analysis in Orthopedics

Cynthia S. Crowson; Dirk R. Larson; Katrina L. Devick; Elizabeth J. Atkinson; Carly S. Lundgreen; David G. Lewallen; Daniel J. Berry; Hilal Maradit Kremers

doi:10.1016/j.arth.2021.04.014

Living With Survival Analysis in Orthopedics

Cynthia S. Crowson, Dirk R. Larson, Katrina L. Devick, Elizabeth J. Atkinson, Carly S. Lundgreen, David G. Lewallen, Daniel J. Berry, Hilal Maradit Kremers

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

Time to event data occur commonly in orthopedics research and require special methods that are often called “survival analysis.” These data are complex because both a follow-up time and an event indicator are needed to correctly describe the occurrence of the outcome of interest. Common pitfalls in analyzing time to event data include using methods designed for binary outcomes, failing to check proportional hazards, ignoring competing risks, and introducing immortal time bias by using future information. This article describes the concepts involved in time to event analyses as well as how to avoid common statistical pitfalls. Please visit the following https://youtu.be/QNETrx8B6IU and https://youtu.be/8SBoTr9Jy1Q for videos that explain the highlights of the paper in practical terms.

Original language	English (US)
Pages (from-to)	3358-3361
Number of pages	4
Journal	Journal of Arthroplasty
Volume	36
Issue number	10
DOIs	https://doi.org/10.1016/j.arth.2021.04.014
State	Published - Oct 2021

Keywords

Cox model
censoring
survival analysis
time-to-event analysis
total joint arthroplasty

ASJC Scopus subject areas

Orthopedics and Sports Medicine

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10.1016/j.arth.2021.04.014

Cite this

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title = "Living With Survival Analysis in Orthopedics",

abstract = "Time to event data occur commonly in orthopedics research and require special methods that are often called “survival analysis.” These data are complex because both a follow-up time and an event indicator are needed to correctly describe the occurrence of the outcome of interest. Common pitfalls in analyzing time to event data include using methods designed for binary outcomes, failing to check proportional hazards, ignoring competing risks, and introducing immortal time bias by using future information. This article describes the concepts involved in time to event analyses as well as how to avoid common statistical pitfalls. Please visit the following https://youtu.be/QNETrx8B6IU and https://youtu.be/8SBoTr9Jy1Q for videos that explain the highlights of the paper in practical terms.",

keywords = "Cox model, censoring, survival analysis, time-to-event analysis, total joint arthroplasty",

author = "Crowson, {Cynthia S.} and Larson, {Dirk R.} and Devick, {Katrina L.} and Atkinson, {Elizabeth J.} and Lundgreen, {Carly S.} and Lewallen, {David G.} and Berry, {Daniel J.} and {Maradit Kremers}, Hilal",

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year = "2021",

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doi = "10.1016/j.arth.2021.04.014",

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AU - Larson, Dirk R.

AU - Devick, Katrina L.

AU - Atkinson, Elizabeth J.

AU - Lundgreen, Carly S.

AU - Lewallen, David G.

AU - Berry, Daniel J.

AU - Maradit Kremers, Hilal

PY - 2021/10

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N2 - Time to event data occur commonly in orthopedics research and require special methods that are often called “survival analysis.” These data are complex because both a follow-up time and an event indicator are needed to correctly describe the occurrence of the outcome of interest. Common pitfalls in analyzing time to event data include using methods designed for binary outcomes, failing to check proportional hazards, ignoring competing risks, and introducing immortal time bias by using future information. This article describes the concepts involved in time to event analyses as well as how to avoid common statistical pitfalls. Please visit the following https://youtu.be/QNETrx8B6IU and https://youtu.be/8SBoTr9Jy1Q for videos that explain the highlights of the paper in practical terms.

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Living With Survival Analysis in Orthopedics

Abstract

Keywords

ASJC Scopus subject areas

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