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Updated: Oct 17, 2025

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    Analyzing treatment switching and censoring in clinical trials is crucial. Different analytical methods significantly impact study results, especially with unbalanced censoring, affecting treatment effectiveness conclusions.

    Keywords:
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    Area of Science:

    • Health Services Research
    • Biostatistics
    • Clinical Epidemiology

    Background:

    • Comparative effectiveness research often faces challenges with patient treatment switching and data censoring.
    • Imbalanced censoring can bias treatment effect estimates in observational studies.

    Purpose of the Study:

    • To evaluate six analytical methodologies for handling imbalanced treatment switching and censoring.
    • To compare the impact of different analytic approaches on treatment effectiveness estimates.

    Main Methods:

    • Employed marginal structural models to address time-varying exposure, confounding, and informative censoring.
    • Utilized an administrative dataset of acute coronary syndrome patients treated with clopidogrel or ticagrelor.
    • Applied methodologies to simulated datasets with varying treatment switching and censoring frequencies.

    Main Results:

    • Different analytical approaches yielded varying point estimates and interpretations.
    • The impact on results was particularly pronounced when censoring was highly unbalanced.
    • Time-varying exposure models with censor-weighting showed potential for robust estimation.

    Conclusions:

    • The choice of analytical methodology significantly influences the interpretation of comparative effectiveness studies.
    • Careful consideration of treatment switching and censoring is essential for accurate clinical trial analysis.
    • Further research into optimal methods for handling these complexities is warranted.