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Adjustment for baseline characteristics in randomized trials using logistic regression: sample-based model versus

Thomas Perneger1, Christophe Combescure2, Antoine Poncet2

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Summary
This summary is machine-generated.

Sample-based adjustment models in clinical trials provide reliable treatment effect estimates, comparable to true adjustment models. This method is preferable to no adjustment for improved accuracy in randomized controlled trials.

Keywords:
Baseline imbalanceOver-fittingRandomized clinical trialsSimulation studyStatistical adjustment

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Randomized clinical trials commonly use sample-based regression models for baseline factor adjustment.
  • Overfitting in sample-based models can lead to inaccurate estimations of treatment effects.
  • This study investigates the performance of sample-based versus true adjustment models using simulated data.

Purpose of the Study:

  • To compare the accuracy of sample-based adjustment models against true adjustment models for estimating treatment effects.
  • To assess the impact of overfitting in sample-based models on treatment effect estimation.
  • To evaluate the performance of different adjustment strategies in randomized controlled trials.

Main Methods:

  • A simulation study was conducted using logistic regression with binary outcomes.
  • Compared unadjusted, sample-adjusted, and true-adjusted treatment effect estimates.
  • Varied sample size, treatment effect magnitude, confounder type, and confounder effect size.

Main Results:

  • Sample-based adjustment models produced less biased treatment effect estimates than true adjustment models.
  • Both methods demonstrated similar variance, accuracy, power, and type 1 error rates.
  • Unadjusted analyses showed conservative bias and odds ratio bias away from the null in small datasets.

Conclusions:

  • Sample-based adjustment provides results comparable to exact adjustment for treatment effect estimation.
  • Employing sample-based adjustment is a superior strategy compared to performing no adjustment.
  • These findings support the use of sample-based adjustment in clinical trial analysis.