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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Research Methods

Background:

  • Meta-analysis requires consistent data presentation across studies.
  • Non-uniform variable transformations (e.g., log vs. no transformation) hinder result synthesis.
  • Recent methods aim to re-express regression coefficients for comparability.

Purpose of the Study:

  • To evaluate the bias of three regression coefficient re-expression methods.
  • To assess the impact of independent variable skewness on re-expression bias.
  • To compare re-expressed coefficients with those from untransformed models.

Main Methods:

  • Simulations and 15 real-world data examples were used.
  • Independent variables exhibited skewed distributions.
  • Regression coefficients from log-transformed variables were re-expressed to an untransformed scale.

Main Results:

  • All three re-expression methods typically yielded biased results.
  • The degree of bias was predicted by the independent variable's skewness.
  • Re-expressed coefficients often differed from those derived from untransformed data.

Conclusions:

  • Current re-expression methods for regression coefficients are often biased.
  • Variable skewness is a key factor influencing bias in meta-analysis.
  • Synthesizing evidence from transformed and untransformed data remains challenging and context-dependent.