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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Randomized Experiments01:13

Randomized Experiments

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...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

You might also read

Related Articles

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

Sort by
Same author

The role of glycaemic and lipid risk factors in mediating the effect of BMI on coronary heart disease: a two-step, two-sample Mendelian randomisation study.

Diabetologia·2017
Same author

Prospective associations of psychosocial adversity in childhood with risk factors for cardiovascular disease in adulthood: the MRC National Survey of Health and Development.

International journal for equity in health·2017
Same author

Are parents' motivations to exercise and intention to engage in regular family-based activity associated with both adult and child physical activity?

BMJ open sport & exercise medicine·2017
Same author

Different strategies for diagnosing gestational diabetes to improve maternal and infant health.

The Cochrane database of systematic reviews·2017
Same author

Association of pre-pregnancy body mass index with offspring metabolic profile: Analyses of 3 European prospective birth cohorts.

PLoS medicine·2017
Same author

Cardiometabolic phenotypes and mitochondrial DNA copy number in two cohorts of UK women.

Mitochondrion·2017
Same journal

Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

American journal of epidemiology·2026
Same journal

A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

American journal of epidemiology·2026
Same journal

Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

American journal of epidemiology·2026
Same journal

Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

American journal of epidemiology·2026
Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026
Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026
See all related articles

Related Experiment Videos

Missing data methods in Mendelian randomization studies with multiple instruments.

Stephen Burgess1, Shaun Seaman, Debbie A Lawlor

  • 1Medical Research Council Biostatistics Unit, Robinson Way, Cambridge CB2 0SR, United Kingdom. stephen.burgess@mrc-bsu.cam.ac.uk

American Journal of Epidemiology
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces four Bayesian imputation methods to address missing genetic data in Mendelian randomization studies, enhancing statistical power and precision. These methods improve causal inference, offering benefits comparable to a 25% sample size increase.

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Biostatistics

Background:

  • Mendelian randomization (MR) studies often suffer from low statistical power.
  • Utilizing multiple genetic instruments can enhance precision in MR analyses.
  • Sporadically missing genetic data can diminish the precision gains from using multiple instruments.

Purpose of the Study:

  • To present and evaluate four Bayesian imputation methods for handling missing genetic data in MR studies.
  • To improve the precision and power of MR analyses by effectively imputing missing data.
  • To estimate the causal relationship between C-reactive protein and fibrinogen/coronary heart disease using imputed genetic data.

Main Methods:

  • Four Bayesian imputation techniques were developed: multiple imputation, single nucleotide polymorphism (SNP) imputation, latent variable imputation, and haplotype imputation.
  • Methods were assessed via simulation studies.
  • The methods were applied to estimate the causal effect of C-reactive protein on fibrinogen and coronary heart disease using 3 SNPs from the British Women's Heart and Health Study.

Main Results:

  • A complete-case analysis using all 3 SNPs was more precise than using single SNPs.
  • The four proposed imputation methods further improved precision, yielding results comparable to a 25% increase in sample size.
  • All imputation methods produced similar results and showed robustness to violations of the missing-at-random assumption.

Conclusions:

  • Bayesian imputation methods effectively address missing genetic data in MR studies, significantly enhancing precision.
  • The proposed methods offer a valuable approach to maximize the utility of available genetic instruments.
  • These imputation techniques provide reliable estimates for causal inference in genetic epidemiology.