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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

174
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
174
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

286
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...
286
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

418
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
418
Odds Ratio01:09

Odds Ratio

260
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
260
Study Design in Statistics01:15

Study Design in Statistics

8.5K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
8.5K
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Does gestational vitamin D attenuate the negative effects of prenatal depression on offspring emotional and behavioral problems? Findings in the ECHO cohort.

Psychological medicine·2026
Same author

Pregnancy Weight Gain and Longer-Term Maternal Cardiometabolic Conditions.

Hypertension (Dallas, Tex. : 1979)·2026
Same author

From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes.

The British journal of nutrition·2026
Same author

Cardiomyocyte-derived BDNF restricts cardiac fibrosis by decreasing the activity of the TGF-β/Smad2/3 pathway and increasing Smad7 expression.

Frontiers in cell and developmental biology·2026
Same author

Major Depression and Antidepressant Treatment: Impact on Pregnancy and Neonatal Outcomes.

Focus (American Psychiatric Publishing)·2026
Same author

Prepregnancy body mass index, pregnancy weight gain, and postpartum weight retention in Pennsylvania 2003 to 2020: an age-period-cohort analysis.

American journal of epidemiology·2026

Related Experiment Video

Updated: Sep 11, 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

14.6K

Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be.

Zhaohua Zeng1, Lisa M Bodnar2, Ashley I Naimi1

  • 1From the Department of Epidemiology, Emory University, Atlanta, GA.

Epidemiology (Cambridge, Mass.)
|August 15, 2025
PubMed
Summary
This summary is machine-generated.

Using a diverse Super Learner ensemble with doubly robust estimators is recommended. Including many algorithms in causal inference studies, like estimating the effects of diet on preeclampsia risk, improves estimate stability.

Keywords:
Average treatment effectDietary intakeDoubly robust estimatorMachine learningNutritionPreeclampsiaSuper Learning

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
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

697

Related Experiment Videos

Last Updated: Sep 11, 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

14.6K
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
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

697

Area of Science:

  • Epidemiology
  • Biostatistics
  • Machine Learning

Background:

  • Super Learner is an ensemble method for causal effect estimation.
  • It is typically used with doubly robust estimators.
  • Diversity in the algorithm library is recommended, but its impact is not well-quantified.

Purpose of the Study:

  • To evaluate the impact of algorithm library size on Super Learner performance.
  • To assess the sensitivity of causal effect estimates to library composition.
  • To investigate the association between periconceptional diet and preeclampsia risk.

Main Methods:

  • Applied Super Learning with augmented inverse probability weighting (AIPW) and targeted minimum loss-based estimation (TMLE).
  • Estimated the average treatment effect (ATE) of fruit and vegetable intake on preeclampsia risk in 7923 women.
  • Compared estimates from a reference ensemble with subsets of algorithms and single algorithms.

Main Results:

  • High fruit and vegetable intake (≥2.5 cups/1000 kcal) was associated with lower preeclampsia risk.
  • ATE estimates for AIPW and TMLE were -0.019 and -0.023, respectively.
  • Removing individual algorithms had minimal impact, but using a single algorithm increased variability.

Conclusions:

  • Empirical evidence supports using diverse machine learning algorithms in Super Learner ensembles.
  • This approach enhances the reliability of causal effect estimates from doubly robust estimators.
  • Diverse ensembles are crucial for robust findings in epidemiologic research.