Tweet
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)
-----------------------------------------------------------------
To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl
For information and archives: http://isb.ri.ccf.org/biomch-l
-----------------------------------------------------------------
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)
-----------------------------------------------------------------
To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl
For information and archives: http://isb.ri.ccf.org/biomch-l
-----------------------------------------------------------------