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Clinical trials as diagnostic tests.

J L Pater, A R Willan

    Controlled Clinical Trials
    |June 1, 1984
    PubMed
    Summary
    This summary is machine-generated.

    Applying diagnostic test evaluation to clinical trials suggests more false positives than expected. However, this approach relies on questionable assumptions about trial power and prevalence, potentially leading to misleading conclusions.

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    Area of Science:

    • Biostatistics
    • Clinical Trial Design
    • Medical Diagnostics

    Background:

    • Diagnostic test evaluation concepts are increasingly applied to clinical trials.
    • Previous analyses suggest clinical trials yield a higher rate of false positives than anticipated.

    Purpose of the Study:

    • To critically examine the assumptions underlying the application of diagnostic test evaluation to clinical trials.
    • To determine if this analogy leads to accurate or misleading conclusions regarding clinical trial outcomes.

    Main Methods:

    • The study analyzes the conceptual parallels between diagnostic test sensitivity/specificity and clinical trial power/predictive values.
    • It scrutinizes the assumption of equating clinical trial power with diagnostic test sensitivity.
    • It evaluates the feasibility and implications of defining a baseline 'prevalence' for positive clinical trials.

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    Main Results:

    • Equating clinical trial power with diagnostic test sensitivity is problematic, as trial decisions often use a broader definition of 'true positive'.
    • Establishing a meaningful 'prevalence' of positive clinical trials is practically difficult.
    • The analogy between diagnostic tests and clinical trials, while superficially appealing, is based on flawed assumptions.

    Conclusions:

    • The direct application of diagnostic test evaluation metrics to clinical trials can yield misleading conclusions.
    • Researchers should exercise caution when using diagnostic test frameworks for clinical trial interpretation.
    • Alternative or refined methodologies may be necessary for accurate clinical trial outcome assessment.