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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Carryover Effects in Sibling Comparison Designs.

Arvid Sjölander1, Thomas Frisell, Ralf Kuja-Halkola

  • 1From the aDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; bClinical Epidemiology Unit, Karolinska Institutet, Stockholm, Sweden; and cDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.

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Sibling comparison designs can address confounding, but sibling carryover effects can introduce bias. This study investigates when and why these effects cause bias in sibling comparison studies.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal inference

Background:

  • Sibling comparison designs are a valuable tool for controlling for unmeasured confounding in observational studies.
  • Standard analysis assumes no carryover or contagion effects between siblings.
  • Carryover effects, where one sibling's exposure or outcome influences another's, are common in real-world scenarios.

Purpose of the Study:

  • To explore the consequences of sibling carryover effects on sibling comparison designs.
  • To identify conditions under which carryover effects lead to biased results.
  • To investigate the direction and magnitude of bias caused by carryover effects.

Main Methods:

  • Utilized causal diagrams to illustrate the pathways of bias.
  • Analyzed various scenarios to understand the impact of different carryover effect magnitudes.
  • Theoretically investigated the sign and magnitude of bias.

Main Results:

  • Carryover effects can systematically bias estimates from sibling comparison studies.
  • The presence and nature of carryover effects determine when and why bias occurs.
  • Causal diagrams effectively demonstrate the mechanisms leading to bias.

Conclusions:

  • The assumption of no sibling carryover effects is critical for valid sibling comparison studies.
  • Researchers must consider and, if possible, account for potential carryover effects to avoid biased inferences.
  • This work provides a framework for understanding and assessing bias due to sibling contagion.