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Related Concept Videos

Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Longitudinal Studies01:26

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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...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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  9. Indigenous Data And Data Technologies
  11. Cross-site Imputation Can Recover Missing Variables In Federated Multicenter Studies.
  1. Home
  3. Research Domains
  5. Indigenous Studies
  7. Other Indigenous Data, Methodologies And Global Indigenous Studies
  9. Indigenous Data And Data Technologies
  11. Cross-site Imputation Can Recover Missing Variables In Federated Multicenter Studies.

Related Experiment Video

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

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Cross-site imputation can recover missing variables in federated multicenter studies.

Robert Thiesmeier1, Paul Madley-Dowd2, Nicola Orsini3

  • 1Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden; Department of Neurobiology, Social Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.

Journal of Clinical Epidemiology
|May 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Cross-site imputation is a new method to recover missing data in multisite studies without pooling individual data. This approach successfully imputes variables, enabling complete analysis across all study sites.

Keywords:
Cross-site imputationDistributed data networkDistributed learningFederated analysis

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Observational Research

Background:

  • Multisite studies often face challenges with missing key variables at certain sites.
  • Pooling data across sites can be logistically or legally infeasible.
  • Existing imputation methods may not be suitable when data pooling is restricted.

Purpose of the Study:

  • To introduce a novel multiple imputation method called cross-site imputation.
  • To enable the recovery of missing variables across study sites without individual-level data pooling.
  • To address data limitations in multisite observational research.

Main Methods:

  • Cross-site imputation utilizes predicted regression coefficients and variances from sites with observed data.
  • It imputes missing variables at sites lacking recorded data.
Meta-analysis
Missing data
Multiple imputation
  • The method was illustrated using Swedish hospital data to recover missing confounders.

    • Main Results:

    • Cross-site imputation effectively recovered systematically missing confounding variables.
    • Imputation was successful independently at sites where data were initially missing.
    • The method facilitated the inclusion of all hospitals in the final, fully adjusted analysis.

    Conclusions:

    • Cross-site imputation presents a practical solution for handling missing variables in multisite studies.
    • This method is valuable given the growing reliance on multisite research designs.
    • It offers a viable alternative when data pooling is not an option.