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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
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LTR retrotransposons are class I transposable elements with long terminal repeats flanking an internal coding region. These elements are less abundant in mammals compared to other class I transposable elements. About 8 percent of human genomic DNA comprises LTR retrotransposons. Some of the common examples of LTR retrotransposons are Ty elements in yeast and Copia elements in Drosophila.
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When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Left truncation in linked data: A practical guide to understanding left truncation and applying it using SAS and R.

Yanling Jin1, Thanh G N Ton2, Devin Incerti2

  • 1Hoffmann-La Roche Ltd, Mississauga, Ontario, Canada.

Pharmaceutical Statistics
|July 17, 2022
PubMed
Summary
This summary is machine-generated.

This study addresses left truncation bias in time-to-event data analysis, common in medical research. It demonstrates methods to accurately analyze data with both left truncation and right censoring, crucial for reliable therapeutic efficacy studies.

Keywords:
SAS/Rimmortal time biasleft truncationsurvival analysistime to event

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Informatics

Background:

  • Time-to-event data, including survival time, are vital in medical research and drug development.
  • Right censoring is a common feature of such data, with established analytical methods like Kaplan-Meier and Cox models.
  • Left truncation, where subjects are excluded due to early event occurrence, introduces selection bias and immortal time bias, often overestimating survival.

Purpose of the Study:

  • To provide a tutorial on analyzing time-to-event data with both left truncation and right censoring without bias.
  • To illustrate potential biases, such as immortal time bias, arising from left truncation.
  • To demonstrate practical implementation using SAS and R.

Main Methods:

  • Utilized a nationwide electronic health record-derived de-identified database.
  • Applied statistical methods designed to handle left-truncated and right-censored data.
  • Provided example code in SAS and R for reproducible analysis.

Main Results:

  • Demonstrated that left truncation can lead to biased results, specifically overestimation of survival time.
  • Showcased methods to correct for left truncation and right censoring, yielding unbiased estimates.
  • The tutorial provides practical code for implementing these unbiased analytical techniques.

Conclusions:

  • Accurate analysis of time-to-event data requires addressing both left truncation and right censoring.
  • Ignoring left truncation can lead to significant biases in survival estimates, impacting therapeutic evaluation.
  • The presented methods and code facilitate unbiased survival data analysis in medical research.