The C.A.M. Report
Complementary and Alternative Medicine: Fair, Balanced, and to the Point
  • About this web log

    This blog ran from 2006 to 2016 and was intended as an objective and dispassionate source of information on the latest CAM research. Since my background is in pharmacy and allopathic medicine, I view all CAM as advancing through the development pipeline to eventually become integrated into mainstream medical practice. Some will succeed while others fail. But all are treated fairly here.

  • About the author

    John Russo, Jr., PharmD, is president of The MedCom Resource, Inc. Previously, he was senior vice president of medical communications at, a complementary and alternative medicine website.

  • Common sense considerations

    The material on this weblog is for informational purposes. It is not medical advice or counsel. Be smart, consult your health professional before using CAM.

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  • Recent Comments

    How many patients for an acupuncture study?

     It’s an important issue when planning clinical studies of CAM.

    Researchers from the David Geffen School of Medicine at the University of California, Los Angeles address this issue for a study of acupuncture in the relief of post-chemotherapy fatigue in breast cancer patients.

    First, the details.

    • A PubMed search identified 3 uncontrolled studies (no comparison group) reporting the effect of acupuncture in relieving fatigue.
    • A separate search identified 5 randomized controlled trials (treatment vs standard care or placebo).
      • Each used a wait-list control of breast cancer patients receiving standard care and reported data on fatigue.
    • The authors used the data to estimate the number of patients for a study of acupuncture in the relief of cancer-related fatigue.

    And, the results.

    • The authors calculated that an adequately powered study with 2 treatment groups would require at least 101 subjects (52 per treatment group) to detect a strong effect for acupuncture and 235 (118 per arm) if a moderate effect is assumed.
      • Strong effect: greater than 80% chance of detecting a clinically significant effect when one exists).
      • Moderate effect: 50% chance of detecting a clinically significant effect when one exists.

    The bottom line?
    Read the article for more information on the 4-step process of evidence-based effect size estimation.

    • Step 1: draw upon published literature to specify a range of assumptions about the amount of change in the treatment group and the amount of change in the control group.
    • Step 2: make a plausible assumption about the amount of change in the treatment group relative to control based upon evidence gathered in step 1.
    • Step 3: carry out a power analysis to estimate sample size.
    • Step 4: refine assumptions about effect size.

    I’ll add step 5: make friends with a statistician.

    1/18/09 22:15 JR

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