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Predicting the Effect of a Predictor When Controlling for Baseline.

Kimmo Sorjonen1, Bo Melin1, Michael Ingre1,2

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|July 14, 2020
PubMed
Summary

Adjusting for baseline outcome (Y0) when analyzing predictor effects (X) on follow-up outcome (Y1) can yield spurious findings. Researchers should verify results using an unadjusted analysis of X on the Y1-Y0 difference to avoid Type 1 errors.

Keywords:
Type 1 erroradjusting for baselinecorrelationfollow-upregression effect

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Area of Science:

  • Statistical modeling
  • Biostatistics
  • Psychometrics

Background:

  • Regression analysis is frequently used to assess the impact of predictors on outcomes.
  • Adjusting for baseline measurements is a common practice in longitudinal studies.
  • Potential biases in regression models require careful consideration.

Purpose of the Study:

  • To investigate the potential for spurious findings in regression analyses.
  • To examine the impact of baseline outcome correlation and test-retest reliability on regression results.
  • To provide recommendations for avoiding statistical errors in longitudinal data analysis.

Main Methods:

  • Simulation study design.
  • Analysis of regression effects with and without baseline adjustment.
  • Varying correlation coefficients between predictor, baseline outcome, and test-retest reliability.

Main Results:

  • Regression analysis adjusting for baseline outcome (Y0) can produce spurious results.
  • Spurious findings are more likely with a strong correlation between predictor (X) and Y0.
  • Weak test-retest correlation between Y0 and Y1 exacerbates the issue.

Conclusions:

  • Researchers must be cautious when interpreting regression effects adjusted for baseline outcomes.
  • Verification using unadjusted analysis of the change score (Y1-Y0) is recommended.
  • Awareness of this statistical phenomenon can help prevent Type 1 errors in research.