Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

337
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...
337
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

206
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
206
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

290
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
290
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

684
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
684
Survival Tree01:19

Survival Tree

172
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
172
Censoring Survival Data01:09

Censoring Survival Data

267
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...
267

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records.

JCO clinical cancer informatics·2023
Same author

Fast permutation tests and related methods, for association between rare variants and binary outcomes.

Annals of human genetics·2017
See all related articles

Related Experiment Video

Updated: Oct 1, 2025

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

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Estimating survival parameters under conditionally independent left truncation.

Arjun Sondhi1

  • 1Flatiron Health, Inc, New York, New York, USA.

Pharmaceutical Statistics
|March 9, 2022
PubMed
Summary

This study introduces a new statistical method to address bias in electronic health records (EHRs) data. The approach uses conditional independence to provide more accurate survival estimates from truncated datasets.

Keywords:
left truncationreal world datasurvival analysis

More Related Videos

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Related Experiment Videos

Last Updated: Oct 1, 2025

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

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Area of Science:

  • Biostatistics
  • Health Informatics
  • Epidemiology

Background:

  • Electronic health records (EHRs) databases often suffer from left truncation bias.
  • Standard methods for bias correction rely on marginal independence assumptions that may not hold.
  • This bias impacts the reliability of statistical parameters derived from EHR data.

Purpose of the Study:

  • To develop and validate statistical methods for unbiased estimation in left-truncated datasets.
  • To explore the utility of conditional independence assumptions for bias correction.
  • To enable more accurate analysis of real-world databases, including clinico-genomic data.

Main Methods:

  • Investigated the estimability of conditional parameters under a weaker conditional independence assumption.
  • Utilized reference data with non-truncated confounder information for marginal parameter estimation.
  • Implemented and tested proposed methods through simulation studies and a real-world clinico-genomic database.

Main Results:

  • Demonstrated unbiased estimation of statistical parameters using the proposed conditional independence approach.
  • Validated the accuracy of statistical inference in simulation studies.
  • Successfully estimated survival distributions in a real-world dataset with conditionally independent left truncation.

Conclusions:

  • A weaker assumption of conditional independence allows for unbiased estimation in left-truncated EHR data.
  • The proposed methods offer a valuable alternative to standard techniques when marginal independence is violated.
  • This work enhances the utility of real-world databases for robust statistical and causal inference.