Time‑to‑event analysis

Priya Ranganathan; Vishal Deo; C. S. Pramesh

doi:10.4103/picr.picr_52_25

Time‑to‑event analysis

Priya Ranganathan^*
, Vishal Deo
, C. S. Pramesh

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Survival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the basic aspects of conventional survival analysis. In this article, we discuss two unique features – nonproportional hazards (PH) and competing risks.

Original language	English
Pages (from-to)	102-105
Number of pages	4
Journal	Perspectives in Clinical Research
Volume	16
Issue number	2
DOIs	https://doi.org/10.4103/picr.picr_52_25
Publication status	Published - 01-04-2025

All Science Journal Classification (ASJC) codes

General Medicine

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10.4103/picr.picr_52_25

Cite this

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title = "Time‑to‑event analysis",

abstract = "Survival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the basic aspects of conventional survival analysis. In this article, we discuss two unique features – nonproportional hazards (PH) and competing risks.",

author = "Priya Ranganathan and Vishal Deo and Pramesh, \{C. S.\}",

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AU - Pramesh, C. S.

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N2 - Survival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the basic aspects of conventional survival analysis. In this article, we discuss two unique features – nonproportional hazards (PH) and competing risks.

AB - Survival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the basic aspects of conventional survival analysis. In this article, we discuss two unique features – nonproportional hazards (PH) and competing risks.

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JO - Perspectives in Clinical Research

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ER -

Time‑to‑event analysis

Abstract

All Science Journal Classification (ASJC) codes

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