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Related Experiment Videos

Effect of acyclovir on herpetic ocular recurrence using a structural nested model.

Stephen R Cole1, Haitao Chu

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street-Room E7640, Baltimore, MD 21205, United States. scole@jhsph.edu

Contemporary Clinical Trials
|May 25, 2005
PubMed
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Noncompliance in clinical trials can skew treatment effect estimates. Structural nested models, like those used in the Herpetic Eye Disease Study, can correct for noncompliance to reveal true causal effects.

Area of Science:

  • Ophthalmology
  • Clinical Trials
  • Biostatistics

Background:

  • Noncompliance with assigned therapies is common in randomized clinical trials.
  • Intent-to-treat analyses may underestimate true treatment effects when noncompliance occurs.
  • Robins' structural nested models offer a method to correct for noncompliance.

Purpose of the Study:

  • To evaluate the causal effect of acyclovir on herpes recurrence using structural nested models.
  • To compare intent-to-treat estimates with causal estimates derived from a structural nested model.
  • To demonstrate the application of structural nested models in a real-world clinical trial setting.

Main Methods:

  • Utilized data from the Herpetic Eye Disease Study, a randomized trial of acyclovir for ocular herpes.

Related Experiment Videos

  • Employed Robins' structural nested models with a Weibull distribution to adjust for treatment noncompliance.
  • Compared hazard ratios from intent-to-treat analysis with those from the structural nested model.

    • Main Results:

    • Intent-to-treat analysis showed a 0.55 hazard ratio for acyclovir versus placebo.
    • Structural nested model analysis revealed a 0.41 hazard ratio for constant acyclovir exposure compared to non-exposure.
    • The causal estimate was 34% larger than the intent-to-treat estimate, despite over 90% compliance.

    Conclusions:

    • Intent-to-treat estimates can significantly undervalue the causal effect of treatments, even with high compliance.
    • Structural nested models provide a more accurate estimation of treatment effects by accounting for noncompliance.
    • This study highlights the importance of employing advanced statistical methods for robust clinical trial data interpretation.