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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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 observed.
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Regression Toward the Mean01:52

Regression Toward the Mean

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Published on: December 9, 2015

Pseudo-observation regression for sequentially truncated data.

Jing Qian1, Erik T Parner2, Morten Overgaard2

  • 1Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003, United States.

Biometrics
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces pseudo-observation methods for regression modeling under sequential truncation, addressing limitations of existing techniques. The findings offer improved tools for analyzing time-to-event data in complex observational studies.

Keywords:
Alzheimer’s diseaseCox proportional hazards modelaccelerated failure time modelbiased samplingjackknifetruncation

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Last Updated: Jun 19, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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Published on: December 9, 2015

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

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Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Observational studies often face truncation, where event times are only observed within a specific range.
  • Sequential truncation, requiring specific event time orderings, presents a more complex challenge in data analysis.
  • Existing methods for estimating event time distributions under sequential truncation have limitations for regression modeling.

Purpose of the Study:

  • To develop regression modeling methods for time-to-event data with sequential truncation.
  • To adapt pseudo-observation techniques for this complex data structure.
  • To evaluate the performance of proposed methods in simulations and real-world data.

Main Methods:

  • Development of simple and modified pseudo-observation methods.
  • Application to Cox and accelerated failure time (AFT) regression models.
  • Validation through simulation studies and analysis of an Alzheimer's disease cohort.

Main Results:

  • The proposed pseudo-observation methods provide valid regression modeling under sequential truncation.
  • Modified pseudo-observations address limitations when truncation depends on covariates.
  • The methods are applicable to complex observational data, including health cohort studies.

Conclusions:

  • Pseudo-observation methods, particularly the modified approach, are effective for regression analysis in the presence of sequential truncation.
  • These methods enhance the ability to model time-to-event data in complex observational settings.
  • The study provides valuable tools for biostatisticians and epidemiologists analyzing cohort data.