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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

460
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.
460
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.1K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

658
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...
658
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

878
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
878
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.1K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Health Technology Assessment - value and limitations to inform resource allocation in India.

Health economics, policy, and law·2026
Same authorSame journal

Qualitative research - Part 1.

Perspectives in clinical research·2026
Same author

Patient and public involvement in cancer care and research: the Indian perspective.

Ecancermedicalscience·2026
Same author

Advancements and perspectives of neoadjuvant immunotherapy in resectable non-small cell lung cancer.

Current problems in cancer·2026
Same author

Addressing low-value care (LVC) in Asia: a narrative review of Choosing Wisely and other initiatives across Asia.

BMJ open quality·2026
Same author

Robotic Ivor Lewis oesophagectomy with intrathoracic hybrid anastomosis.

Multimedia manual of cardiothoracic surgery : MMCTS·2026
Same journal

Cross-tool evaluation of artificial intelligence-drafted informed consent documents: A 3-level study.

Perspectives in clinical research·2026
Same journal

Preparing for central drugs standard control organization ethics committee inspections in India: A review of regulatory expectations and readiness strategies.

Perspectives in clinical research·2026
Same journal

Competencies and operations of research ethics committee members and the protection of research participants: A scoping review.

Perspectives in clinical research·2026
Same journal

The Consolidated Standards of Reporting Trials Statement-2025: New epoch for improving the transparency of randomized trials reporting.

Perspectives in clinical research·2026
Same journal

Cost analysis and drug utilization pattern in diabetic patients attending outpatient at tertiary care teaching hospital in South Gujarat.

Perspectives in clinical research·2026
See all related articles

Related Experiment Video

Updated: Feb 24, 2026

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

10.9K

Common pitfalls in statistical analysis: Logistic regression.

Priya Ranganathan1, C S Pramesh2, Rakesh Aggarwal3

  • 1Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India.

Perspectives in Clinical Research
|August 23, 2017
PubMed
Summary
This summary is machine-generated.

Logistic regression analysis examines relationships between predictor variables and binary outcomes. This article explores its applications and inherent limitations in statistical modeling.

Keywords:
Biostatisticslogistic modelsregression analysis

More Related Videos

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

2.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

776

Related Experiment Videos

Last Updated: Feb 24, 2026

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

10.9K
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

2.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

776

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Logistic regression is a widely used statistical method.
  • It is employed to model binary outcomes based on predictor variables.

Purpose of the Study:

  • To provide an overview of logistic regression analysis.
  • To discuss the limitations associated with this statistical technique.

Main Methods:

  • The article focuses on the conceptual understanding of logistic regression.
  • It involves analyzing the relationship between independent variables and a dichotomous dependent variable.

Main Results:

  • Logistic regression quantifies the impact of predictors on a binary outcome.
  • Understanding its limitations is crucial for accurate interpretation.

Conclusions:

  • Logistic regression is a valuable tool for binary outcome analysis.
  • Awareness of its limitations ensures appropriate application and interpretation in research.