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Re: Clinical significance

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  • Re: Clinical significance

    I agree with Mr. DeWitt that NNT might be a more useful clinical
    assessment. I think of greater impact is seeing the potential affect of
    interventions using a freeware piece of software called Visual Rx. This
    can be downloaded from

    As I recall from my stats classes, using the t-test to test the
    hypothesis that the experimental treatment is different than the control
    as described would require doing 4 separate t-tests. One would need to
    show that the groups are the same at the start of the study and
    different at the end. Thus, t-tests would need to be calculated to
    compare within each group, one pre and post and one t-test each to
    compare between groups pre and post treatment. Having thus tested ones
    hypothesis with the four t-tests the true probability of Type 1 error
    would now be approximately the sum of the four values calculated as the
    probability of Type 1 error by each of the four t-test. Instead one
    should have used an ANOVA or other model. One could "get away" with a
    single t-test if one tested using one score from both groups, delta (pre
    minus post) scores and then an unpaired t-test. Could someone with a
    better knowledge of statistics correct me if I am wrong. (And there is
    a good probability of that!)

    Finally regarding clinical significance I think a better way to describe
    this is that the difference matters from point of view of the
    patients/subjects. As an example, an intervention that is intended to
    increase knee joint range of motion post-op, needs to result in a
    difference that matters to a patient not to the researcher. If one
    intervention improved range of motion, to a statistically significant
    degree over another by 5 degrees, such a small increase in knee range of
    motion would not be considered, from a patient's viewpoint, as
    clinically important. It might make walking a tad easier but not that it
    would be really important to them.

    Dr. Stephen Perle

    DEWITT, JOHN K. (JSC-SK) (WLS) wrote:

    >Dr. Etnyre provided a dental example where there was an 80% success rate. If
    >the dentist were doing the treatment and wanted to determine if the results
    >were statistically significant, he could set up an experiment with the
    >H0 (null): there will be no difference in the condition of TMJ with the
    >application of a splint compared to no treatment
    >H1 (alternative): there will be a difference
    >He would then need a measure (I am not a dentist, but lets just say it is
    >the peak force generated during a biting task). To test his hypothesis, he
    >could take before and after measures from the same group, or compare the
    >test group to a matched group of controls. This would involve a t-test
    >(paired or two sample, depending upon the study design). Since a t-test
    >compares means, a large enough change in 80% of the patients could drive the
    >group mean to a value that is statistically significant. The fact that there
    >was a statistically significant difference, however, would not necessarily
    >mean that there is a clinically significant difference. The 80% success rate
    >does not figure into the calculation.
    >In the case of this example, using an epidemiological measure like number
    >needed to treat (NNT) might be better to help interpret if the treatment was
    >useful. The NNT is the number of patients necessary to treat for one
    >incident (in this case, TMJ improvement) to occur. You would need to compare
    >the success rate of the procedure (80%), to the success rate of another
    >procedure. This is good for nominal data, which is how the example was
    >presented. Effect size is another good way to measure the magnitude of the
    >difference to help determine how different means really are.
    >Clinical significance, in my interpretation, is a result that makes a
    >difference in normal, daily life. Sometimes results are deemed significant
    >because of statistical significance that do not make much difference
    >clinically because there was a large sample size. On the contrary, sometimes
    >results may not show statistical significance but have clinical significance
    >(like preventing a highly contagious disease or a death). While the former
    >can be tested mathematically, I think that the latter is due to the
    >interpretation of the researcher (in combination with the test statistics).
    >John DeWitt, M.S., C.S.C.S.
    >Biomechanist - Exercise Physiology Laboratory
    >Space Physiology & Countermeasures
    >Johnson Space Center
    >Houston, TX 77058
    >281-483-8939 / 281-483-4181 (fax)
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    Stephen M. Perle, D.C., M.S.
    Associate Professor of Clinical Sciences
    Adjunct Professor of Mechanical Engineering
    University of Bridgeport
    Bridgeport, CT 06601 USA
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