Dewitt, John K. (jsc-sk) (wls)

01-26-2005, 07:25 AM

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

hypotheses:

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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the dentist were doing the treatment and wanted to determine if the results

were statistically significant, he could set up an experiment with the

hypotheses:

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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