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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.
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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Demystifying inconsistent two-sample mendelian randomization estimations using selection diagram.

Lei Hou1,2, Yuanyuan Yu1,2, Zhi Geng3

  • 1Department of Medical Data, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China.

BMC Medical Research Methodology
|December 13, 2025
PubMed
Summary
This summary is machine-generated.

Two-sample Mendelian randomization (TSMR) can be biased by differing population distributions, termed different local mechanisms. This study defines inconsistent TSMR estimations (InTSMRE) and introduces the LATE Ratio to quantify causal effect deviation.

Keywords:
Inconsistent Two-Sample Mendelian Randomization EstimationsLocal Average Treatment EffectLocal mechanismsMonotonicity ConditionSelection Diagram

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetic Epidemiology

Background:

  • Two-sample Mendelian randomization (TSMR) is a powerful method for causal inference, but susceptible to bias from population-specific variable distributions.
  • Differences in exposure, outcome, or confounder distributions across populations, known as different local mechanisms, can compromise TSMR validity.
  • Unmeasured confounding is a key challenge addressed by TSMR, yet local mechanism differences introduce a distinct source of bias.

Purpose of the Study:

  • To clarify the impact of different local mechanisms on Local Average Treatment Effect (LATE) estimation in TSMR.
  • To formally define Complete and Partial Inconsistent TSMR Estimations (InTSMRE).
  • To introduce the LATE Ratio for quantifying deviations in LATE estimates from true causal effects.

Main Methods:

  • Utilized selection diagrams to analyze the influence of different local mechanisms on TSMR.
  • Developed a criterion for 'No InTSMRE' applicable to both continuous and binary outcomes.
  • Proposed the LATE Ratio as a metric to assess the accuracy of TSMR causal effect estimates.
  • Conducted simulation studies to identify conditions leading to InTSMRE.

Main Results:

  • Defined and distinguished Complete and Partial InTSMRE based on local mechanisms.
  • Demonstrated that violation of the Monotonicity condition leads to Complete InTSMRE, while its adherence results in Partial InTSMRE.
  • Introduced the LATE Ratio, showing its utility in evaluating the magnitude of InTSMRE.
  • Empirically demonstrated InTSMRE in a study of waist-to-hip ratio and Type 2 diabetes in European and mixed populations.

Conclusions:

  • Different local mechanisms pose a significant challenge to the validity of TSMR causal inference.
  • The proposed framework and LATE Ratio offer tools to identify and quantify bias in TSMR due to differing population distributions.
  • Understanding and addressing InTSMRE is crucial for reliable causal effect estimation in genetic epidemiology.