Journal of Biometrics & Biostatistics

Survival Analysis Techniques for Time-to-Event Data in Research

Abstract

Jalin Karima*

Survival analysis is a branch of statistics that deals with the analysis of time-to-event data. It has applications across various fields, including medicine, engineering, social sciences, and economics. Time-to-event data, often referred to as survival data, involves the time until a specific event occurs, such as death, failure, or relapse. The unique characteristics of this type of data necessitate specialized techniques and models that can appropriately handle censoring and the non-normality of the distribution of survival times. At the core of survival analysis is time-to-event data, which measures the duration until an event of interest occurs. For example, in clinical trials, researchers may examine the time until a patient experiences an adverse event or reaches a specific health milestone. Censoring occurs when the event of interest has not been observed for some subjects during the study period. This can happen for various reasons, such as loss to follow-up or the study ending before the event occurs. Censoring is a critical concept in survival analysis because traditional statistical methods that assume complete data can lead to biased results.