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

Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.4K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.4K
Relative Risk01:12

Relative Risk

2.1K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.1K
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

13.5K
The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
13.5K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

487
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
487
Mechanical Protein Functions01:58

Mechanical Protein Functions

5.6K
Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force. 
5.6K
Functional Groups02:45

Functional Groups

88.4K
Functional groups are a group of atoms with characteristic properties, which when linked to the carbon skeleton of a molecule, alter the properties of that molecule. For example, the presence of certain functional groups on a molecule will make them hydrophilic, whereas others will make them hydrophobic. These functional groups are an indispensable part of organic chemistry and important components of biological molecules, such as carbohydrates, proteins, lipids, and nucleic acids. Each...
88.4K

You might also read

Related Articles

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

Sort by
Same author

Substance use and menopausal hormone therapy: Treatment initiation and interruption among US women with and without HIV, 2008-2019.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2026
Same author

Characterizing presenteeism among healthcare personnel at an academic medical center across eras of the COVID-19 pandemic.

Infection control and hospital epidemiology·2025
Same author

Attitudes Towards Aging, Depression, Physical Functioning, and Pain Among Women Living with HIV of Reproductive Age.

AIDS and behavior·2025
Same author

Substance Use and Drug Treatment Among Reproductive-Age Women With and Without HIV in the Southern United States.

Open forum infectious diseases·2025
Same author

Prevalence of genital and extragenital sexually transmitted infections among women of reproductive age with and without HIV in the Southern US: results from the study of treatment and reproductive outcomes.

Frontiers in medicine·2025
Same author

Effectiveness of levonorgestrel implant and depot medroxyprogesterone acetate injectable for women with HIV on efavirenz.

AIDS (London, England)·2025

Related Experiment Video

Updated: Jan 30, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

88.0K

Nonparametric Bounds for the Risk Function.

Stephen R Cole1, Michael G Hudgens2, Jessie K Edwards1

  • 1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina.

American Journal of Epidemiology
|January 31, 2019
PubMed
Summary

Nonparametric bounds for risk difference calculations are simple and avoid assumptions on unmeasured confounding or missing data bias. While often wide, these bounds effectively communicate uncertainty from potential systemic errors.

Keywords:
biasboundsinferencemissing data

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

2.6K
Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA
10:15

Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA

Published on: July 6, 2012

16.8K

Related Experiment Videos

Last Updated: Jan 30, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

88.0K
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

2.6K
Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA
10:15

Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA

Published on: July 6, 2012

16.8K

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Assessing treatment effects requires addressing potential biases like unmeasured confounding and selection bias from missing data.
  • Traditional methods may rely on untestable assumptions, limiting their applicability.

Purpose of the Study:

  • To present nonparametric bounds for the risk difference as a method to quantify uncertainty.
  • To demonstrate the straightforward calculation and interpretation of these bounds.

Main Methods:

  • Calculation of nonparametric bounds for the risk difference.
  • No assumptions are made regarding unmeasured confounding or selection bias due to missing data (e.g., dropout).

Main Results:

  • Nonparametric bounds for the risk difference are easily computed.
  • These bounds inherently communicate uncertainty arising from potential systemic errors.
  • The bounds can be wide, reflecting the extent of potential bias.

Conclusions:

  • Nonparametric risk difference bounds offer a robust approach to assess uncertainty without untestable assumptions.
  • The method provides a transparent way to acknowledge and quantify potential biases in observational studies.
  • An illustrative example is provided to guide practical application.