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On closed test procedures for dose-response analysis

D M Rom1, R J Costello, L T Connell

  • 1Rhône-Poulenc Rorer Central Research, Collegeville, Pennsylvania 19426.

Statistics in Medicine
|August 15, 1994
PubMed
Summary
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This study introduces novel statistical methods for analyzing drug dose-response effects in medical research. These procedures accurately assess increasing/decreasing effects and potential reversals, controlling the family-wise error rate.

Area of Science:

  • Biostatistics
  • Pharmacology
  • Medical Research

Background:

  • Dose-response relationships are crucial in medical studies to understand drug efficacy and safety.
  • Existing methods for testing ordered alternatives may not capture complex dose-response patterns, such as reversals at higher doses.

Purpose of the Study:

  • To develop and validate statistical procedures for testing dose-response effects in medical studies.
  • To address two scenarios: monotonic dose-response and dose-response with potential reversal.
  • To ensure strong control of the family-wise error rate (FWER).

Main Methods:

  • For monotonic effects, a procedure combining Peritz's closure principle with Marcus et al.'s closed testing procedure applied to Tukey et al.'s test was used.
  • For effects with potential reversal, simultaneous examination of contrasts among doses at multiple testing stages was proposed.

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  • Critical values were calculated, incorporating the correlation structure among contrasts.

    • Main Results:

    • Both proposed procedures strongly control the family-wise error rate at the specified alpha level.
    • The methods provide insights into the dose-response curve's shape, which is often missing in standard procedures for ordered alternatives.
    • The procedures were illustrated using two real-world datasets.

    Conclusions:

    • The developed statistical methods offer robust tools for analyzing complex dose-response relationships in medical research.
    • These procedures enhance the understanding of drug effects beyond simple monotonic trends.
    • The methods provide a more comprehensive analysis of dose-response data compared to existing techniques.