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Binary regression with continuous outcomes

S Suissa1, L Blais

  • 1Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.

Statistics in Medicine
|February 15, 1995
PubMed
Summary
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Analyzing continuous clinical data using a continuous approach is more efficient and reliable than dichotomizing it. This method improves statistical power and avoids fitting issues common with binary analysis.

Area of Science:

  • Biostatistics
  • Clinical Epidemiology

Background:

  • Clinical research often uses continuous outcomes (e.g., cholesterol) analyzed by mean-based methods.
  • Interest frequently lies in proportions below/above clinical thresholds, leading to dichotomization.
  • Dichotomizing continuous data can lead to loss of information and statistical power.

Purpose of the Study:

  • To present a parametric, generalized linear model (GLM) approach for analyzing continuous outcomes directly.
  • To compare the efficiency and bias of continuous vs. dichotomized analysis methods.
  • To demonstrate the application of GLMs for continuous outcomes in clinical research.

Main Methods:

  • Utilized generalized linear models (GLMs) to fit regression models on original continuous outcomes.
  • Considered Gaussian and three-parameter log-normal distributions for continuous data.

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  • Employed simulation analyses to assess precision and bias under various conditions.

    • Main Results:

    • The continuous approach successfully fitted samples where the dichotomous approach failed.
    • Continuous analysis yielded estimates 25-85% more efficient than the dichotomous approach.
    • Simulation results highlighted the superiority of the continuous method in terms of precision and applicability.

    Conclusions:

    • Analyzing continuous clinical outcomes directly using GLMs is statistically advantageous over dichotomization.
    • The continuous approach enhances statistical efficiency and avoids potential fitting problems.
    • This method offers a more robust and informative analysis for clinical trial data.