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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...
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Measurement errors in control risk regression: A comparison of correction techniques.

Annamaria Guolo1

  • 1Department of Statistical Sciences, University of Padova, Padova, Italy.

Statistics in Medicine
|October 16, 2021
PubMed
Summary

Control risk regression in meta-analysis can suffer from measurement error. This study evaluates correction methods, recommending structural approaches for high heterogeneity and score methods for low heterogeneity or small sample sizes.

Keywords:
SIMEXlikelihoodmeasurement errormeta-analysisscore function

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research Methodology

Background:

  • Control risk regression is a meta-analysis technique assessing treatment effectiveness by comparing outcome risks in treated versus control groups.
  • Illness severity, a key heterogeneity source, is often approximated by control group event rates, introducing potential measurement error.
  • Measurement error in control risk estimates necessitates correction for reliable meta-analysis inference.

Purpose of the Study:

  • To investigate the impact of measurement error in control risk regression under various scenarios.
  • To evaluate the performance of different measurement error correction methods.
  • To guide researchers in selecting appropriate correction techniques for meta-analysis.

Main Methods:

  • The study simulated measurement error effects on control risk distribution, including non-normality.
  • Evaluated likelihood-based structural methods and functional methods (simulation-based, score functions).
  • Assessed method performance via simulation, considering heterogeneity and sample size.

Main Results:

  • Structural approaches are favored for high heterogeneity; score methods are better for low heterogeneity and small sample sizes.
  • The simulation-based approach demonstrated robust performance across scenarios without convergence issues.
  • Applied methods to a meta-analysis on diabetes and Parkinson disease risk.

Conclusions:

  • Researchers must address measurement error in control risk regression to avoid erroneous conclusions.
  • The choice of correction method depends on heterogeneity levels and sample size.
  • Simulation-based methods offer a reliable option across diverse conditions.