Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Mantel-Cox Log-Rank Test

The Mantel-Cox Log-Rank Test

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 interest.

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...

Dose Response Curve: Conventional Versus Nonmonotonic

Dose Response Curve: Conventional Versus Nonmonotonic

The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response relationships...

Dose-Response Relationship: Overview

Dose-Response Relationship: Overview

Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...

You might also read

Related Articles

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

Sort by

Same author

Modelling alternately recurring events using subject specific hazard estimation approach.

Journal of biopharmaceutical statistics·2024

Same author

Arsenic exposure from drinking water and staple food (rice): A field scale study in rural Bengal for assessment of human health risk.

Ecotoxicology and environmental safety·2021

Same author

Health system gaps in cardiovascular disease prevention and management in Nepal.

BMC health services research·2021

Same author

Analysis of Survival Among Adults With Early-Onset Colorectal Cancer in the National Cancer Database.

JAMA network open·2021

Same author

Utilizing a church-based platform for mental health interventions: exploring the role of the clergy and the treatment preference of women with depression.

Global mental health (Cambridge, England)·2021

Same author

Facilitators and barriers to healthy eating in a worksite cafeteria: a qualitative study.

BMC public health·2021

Search research articles

Related Experiment Video

Updated: Jul 14, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Comparing smoothing techniques in Cox models for exposure-response relationships.

Usha S Govindarajulu¹, Donna Spiegelman, Sally W Thurston

¹Yale Center for Clinical Investigation, Yale School of Medicine, New Haven, CT 06510, USA. usha.govindarajulu@yale.edu

Statistics in Medicine

|June 1, 2007

Summary

Flexible smoothing techniques accurately model non-linear exposure-response relationships in lung cancer mortality studies. Penalized and restricted cubic splines closely align, offering reliable dose-response curve analysis for occupational cohorts.

More Related Videos

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

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

Published on: December 9, 2015

Related Experiment Videos

Last Updated: Jul 14, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

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

Published on: December 9, 2015

Area of Science:

Occupational epidemiology
Biostatistics
Survival analysis

Background:

Non-linear exposure-response relationships are common in occupational health.
Accurate modeling of these relationships is crucial for risk assessment.
Traditional methods may not adequately capture complex dose-response patterns.

Purpose of the Study:

To compare flexible non-parametric smoothing techniques for modeling time to lung cancer mortality.
To evaluate penalized splines, restricted cubic splines, and fractional polynomials in Cox models.
To propose a standardized measure for quantifying differences between dose-response curves.

Main Methods:

Applied penalized splines, restricted cubic splines, and fractional polynomials to Cox models.
Analyzed two occupational cohorts with skewed exposure distributions.
Developed a novel area-based measure to quantify curve differences across exposure percentiles.

Main Results:

Dose-response curves from the three methods showed similarity in denser exposure ranges.
Differences between curves up to the 50th percentile were minimal (<1% of total difference).
Penalized splines and restricted cubic splines demonstrated closer agreement than with fractional polynomials.

Conclusions:

Flexible smoothing techniques effectively model non-linear exposure-response relationships in occupational lung cancer mortality.
Penalized splines and restricted cubic splines offer robust and comparable results.
The proposed area-based measure provides a precise way to assess curve differences, especially in skewed distributions.