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

Randomized Experiments01:13

Randomized Experiments

8.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

319
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
319
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

927
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...
927
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

242
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,...
242
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
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

243
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
243

You might also read

Related Articles

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

Sort by
Same author

Causal effect estimation from trans-regulatory single-cell CRISPR screens.

Cell genomics·2026
Same author

Comparison of Methods for Sensitivity Analysis of Heterogeneous Treatment Effects in Observational Studies and Application to Alzheimer's Disease and Cognitive Decline.

Statistics in medicine·2026
Same author

An approach to estimating how effective and well targeted Extreme Risk Protection Orders have been with respect to suicide prevention.

American journal of epidemiology·2026
Same author

Discussion on 'Causal inference with misspecified network interference structure' by Bar Weinstein and Daniel Nevo.

Biometrics·2026
Same author

Extending the Use of Mendelian Randomisation With Non-Inherited Variants to Assess Socially Transmitted Parental Exposures Under Assortative Mating.

Genetic epidemiology·2026
Same author

A More Robust Approach to Multivariable Mendelian Randomization.

Biometrika·2025

Related Experiment Video

Updated: Sep 28, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K

Nonparametric bounds in two-sample summary-data Mendelian randomization: Some cautionary tales for practice.

Ralph Møller Trane1, Hyunseung Kang1

  • 1Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Statistics in Medicine
|March 31, 2022
PubMed
Summary

Mendelian randomization (MR) uses genetic data to estimate causal effects. Nonparametric bound-based analysis in two-sample MR is more conservative than one-sample MR for informative causal effect bounds.

Keywords:
Mendelian randomizationcausal inferenceinstrument strengthnonparametric boundstwo-sample studies

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

2.2K
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

Related Experiment Videos

Last Updated: Sep 28, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
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
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

Area of Science:

  • Genetic epidemiology
  • Statistical genetics
  • Biostatistics

Background:

  • Mendelian randomization (MR) is a popular method for causal inference in genetic epidemiology.
  • Two-sample summary-data MR studies commonly use parametric methods with genome-wide association study (GWAS) summary statistics.
  • The application and implications of nonparametric bound-based analysis in two-sample MR remain underexplored.

Purpose of the Study:

  • To explore the use of nonparametric, bound-based analysis in two-sample Mendelian randomization studies.
  • To assess the potential for obtaining more informative bounds using a one-sample MR design compared to a two-sample design.
  • To provide practical guidance on applying bound-based analyses in MR.

Main Methods:

  • Investigated nonparametric, bound-based analysis within the two-sample Mendelian randomization framework.
  • Proposed a framework to compare the informativeness of bounds from one-sample versus two-sample MR designs.
  • Applied the methods to real data examples, including the causal effects of smoking on lung cancer and high cholesterol on heart attacks.

Main Results:

  • Nonparametric, bound-based analysis in two-sample MR settings yields more conservative bounds compared to one-sample MR.
  • The informativeness of bounds is highly dependent on the MR design (one-sample vs. two-sample).
  • Real data analyses demonstrated the practical application and comparative informativeness of bounds.

Conclusions:

  • While nonparametric bound-based analysis offers a valuable alternative in MR, its application in two-sample settings requires careful consideration due to conservatism.
  • One-sample MR designs may offer more informative bounds than two-sample designs for this analytical approach.
  • The findings provide insights into the trade-offs between different MR designs for causal effect estimation using bound-based methods.