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Redefining effect modification.

Suhail A Doi1, Jazeel Abdulmajeed1,2, Chang Xu1,3

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This summary is machine-generated.

The odds ratio (OR) is often misunderstood in epidemiology. This study argues that the OR, despite concerns like noncollapsibility, may be the most appropriate measure for causal inference and effect modification analysis.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Medical Research Methodology

Background:

  • The odds ratio (OR) is frequently misunderstood in evidence-based medicine and clinical epidemiology.
  • Current interpretations often view "noncollapsibility" as a limitation, deeming the OR unsuitable for causal inference or effect modification.
  • Existing focus on relative risk (RR) and risk difference (RD) has limitations as they are not solely effect measures and vary with baseline risk.

Purpose of the Study:

  • To re-evaluate the utility of the odds ratio (OR) in epidemiological research.
  • To demonstrate that the OR may possess unique properties essential for causal inference and effect modification.
  • To challenge the prevailing focus on relative risk (RR) and risk difference (RD) by highlighting their inherent limitations.

Main Methods:

  • Examination of generalized linear models.
  • Analysis of three common binary effect measures used in epidemiology: odds ratio (OR), relative risk (RR), and risk difference (RD).
  • Comparative assessment of the properties of OR, RR, and RD concerning causal inference and effect modification.

Main Results:

  • The odds ratio (OR) may be the only effect measure suitable for interpreting effect modification.
  • The OR possesses properties that make it a compelling parameter of interest for causal inference.
  • Relative risk (RR) and risk difference (RD) are not solely measures of effect and are numerically dependent on baseline risk.

Conclusions:

  • The odds ratio (OR) should be reconsidered as a primary parameter of interest in epidemiological studies.
  • The perceived limitations of the OR, such as noncollapsibility, do not negate its value for causal inference and effect modification.
  • Further discussion and debate within the epidemiology community are encouraged regarding the optimal use of effect measures.