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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

380
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
380
Stratified Sampling Method01:16

Stratified Sampling Method

13.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
13.9K
Sampling Plans01:23

Sampling Plans

565
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
565
Multiple Regression01:25

Multiple Regression

3.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.4K
Randomized Experiments01:13

Randomized Experiments

8.5K
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...
8.5K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Maintenance Pemetrexed/Pembrolizumab Versus Pembrolizumab in Non-Small Cell Lung Cancer: A Propensity Score-Weighted Analysis.

JCO oncology practice·2026
Same author

Communication With Clinicians and Relatives About Cascade Genetic Testing in Cancer Patients With Germline Pathogenic Variants.

JCO precision oncology·2026
Same author

Joint modeling of multiple longitudinal biomarkers and survival outcomes via threshold regression: variability as a predictor.

Biometrics·2026
Same author

IL1β/IL1R1/IRAK4 Drives Inflammatory Ovarian Cancer Seeding at the inflamed sites and Is Reversed by an IRAK4 inhibitor UR241-2.

bioRxiv : the preprint server for biology·2026
Same author

Grand rounds in methodology: four key things to know about the reliability of measurement.

BMJ quality & safety·2026
Same author

Complement-mediated ADCP as a distinct and finite cytotoxic mechanism of monoclonal antibodies.

Frontiers in immunology·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Nov 8, 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

14.8K

Using multiple imputation to classify potential outcomes subgroups.

Yun Li1,2,3, Irina Bondarenko3, Michael R Elliott3,4

  • 1Division of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA.

Statistical Methods in Medical Research
|April 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new causal inference framework to understand how medical tests influence treatment decisions, especially for specific patient groups. It addresses missing data and improves the precision of causal estimates, using a 21-gene assay in breast cancer as an example.

Keywords:
Causal inferenceeffect heterogeneitymissing datamultiple imputation

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K
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

10.5K

Related Experiment Videos

Last Updated: Nov 8, 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

14.8K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K
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

10.5K

Area of Science:

  • Causal Inference
  • Health Services Research
  • Biostatistics

Background:

  • Increasing availability of medical tests raises concerns about over-testing, over-treatment, and healthcare costs.
  • Traditional statistical methods often focus on average treatment effects, potentially overlooking subgroup variations.
  • Missing data and the need for causal interpretability are significant challenges in analyzing testing's impact on treatment.

Purpose of the Study:

  • To develop a causal inference framework for classifying patients into subgroups based on how test results influence treatment selection.
  • To address missing data issues using multiple imputation for potential outcomes and observed values simultaneously.
  • To examine the influence of the 21-gene assay on chemotherapy selection in breast cancer patients using proposed methods.

Main Methods:

  • Utilized the Rubin Causal Model framework to define four potential outcomes subgroups based on test result impact on treatment.
  • Employed multiple imputation techniques to handle missing potential outcomes and regular missing data concurrently.
  • Conducted sensitivity analyses to assess the impact of potential violations of the conditional independence assumption.

Main Results:

  • The proposed subgroup classification effectively captures differential influences of medical testing on treatment selection.
  • Incorporating causal inference assumptions into multiple imputation improved precision for certain causal estimates.
  • Bias can arise if the potential outcomes conditional independence assumption is violated; sensitivity analyses are crucial.

Conclusions:

  • The novel causal framework offers a nuanced understanding of medical test influence on treatment decisions, identifying targets for improved test utilization.
  • The multiple imputation approach effectively handles missing data while providing precise causal estimates.
  • The methods were successfully applied to the 21-gene assay in breast cancer, demonstrating their practical utility in real-world clinical scenarios.