"Statistical considerations of the histomorphometric test protocol" John E. Byrd, Ph.D. D-ABFA Maria-Teresa Tersigni-Tarrant, Ph.D. Central Identification Laboratory JPAC

1. Statistical Considerations of the
Histomorphometric Test Protocol for
Determination of Human Origin of
Skeletal Remains
John E. Byrd, Ph.D. D-ABFA
Maria-Teresa Tersigni-Tarrant, Ph.D.
Central Identification Laboratory
JPAC

2. human vs. non-human
• Fundamental question in forensic
anthropology
• Relevant to medicolegal significance
• Results needed quickly
• Typically determined by macroscopic
observations
• Small fragments present special
problems

3. Histomorphology as a solution
• Some patterns are
decisively nonhuman
(e.g. plexiform bone)
• Presence of primary and
secondary osteons is a
hallmark of human
bone, but not unique
• This allows us to REJECT
human as the origin, but
not ACCEPT!
[Note the assymmetry]

4. Histomorphometrics
• Metrics present a more powerful approach to
recognizing human remains due to the ability
to deal with bone showing only circular
osteons
• Osteon area is the preferred measurement
• Use of metrics requires statistical approach
• This presentation will describe a test protocol
for using osteon area to segregate human
from non-human bone

5. Test Protocol
1. Section case specimen, embed in resin, mount
on slide, capture digital image with scale
2. Import image into Image J©
3. Select random sample of 30 osteons
4. Measure the osteon area of the 30 osteons and
calculate sample mean
5. Test null hypothesis that remains have same
mean as human reference sample
6. Assess the strength of evidence in results

6. How good is the test?
• We compared the human osteon area data to
osteon areas from 17 chimpanzees, our
closest living relative. Chimp data was
obtained from slides in possession of AFIP
Museum.
• We ran the test on 37 specimens from 35
known humans (independent of reference
sample) to check performance

8. Comparison to chimpanzees
010
P
Chimp:
Osteon Area 25377 micron2
Standard Dev of mean 2692
Human:
Osteon Area 37365 micron2
Standard Dev of mean 2728
These statistics were derived from a bootstrap procedure involving 1000 samples of N=30.
Histograms depict the 1000 means. Note that the standard deviation is analogous to the
standard error in parametric statistics.

9. Application to test sample
• 37 specimens from 35 different known
individuals were tested using this protocol
• Tests included no erroneous results
Cutoff (p < 0.05) = < 32,839

10. Validation?
• Chimpanzees show similarly large osteon
areas, yet are shown to be statistically
separable. Power of test versus chimp = 0.90.
• Application of test to known human test
sample revealed no errors.
Presumably, future applications will mis-fire
according to the p-value chosen (e.g.
0.1, 0.05, 0.01, etc.)
• Small overlap in distribution of chimp means
versus human means is very encouraging

11. Example
• Known sample (#02H) from dog
• Protocol is followed to obtain a sample mean
value of 17980 micron2
• Since this is below the cutoff (p<0.05) of
32,839 micron2, we reject the null hypothesis

12. Post-hoc Evaluation of a Result
• Concept proposed by Karl Popper
• A hypothesis (interpretation) should be accepted
just to the extent that it has survived a severe test
• Mayo (1996) has operationalized the concept into
a statistic that can be applied to test results post-
hoc
• Not the same thing as power of the test
Severity!

13. Severity
• Operationalizes the idea of the severe test
• In the case where we do NOT reject the null
hypothesis, we are interested in the probability
that if an opposing hypothesis (alternative “A”)
were true, our test result would have indicated so
(by a more significant departure)
• If this probability is high it means
P(test result more sig than observed; A) = high
• Thus, we can take the observed statistic as
indicating “not-A” with severity. We would
usually relate this to the parameter, as in there is
evidence µ > µ’ (for some µ’ alternative to null)

14. Severity cont’d
• For the case we DO reject null hypothesis—
• We are interested in the probability that if the
null were true, we would have seen a less
impressive departure from the null
• If this probability is high, it means that
P(test result less sig; null) = high
• This is (1-p). With small p-value, we can infer
with severity (1-p) evidence for some
discrepancy from the null

15. Severity example: Dog sample
• Human reference standard from bootstrap runs: Mean
37365.3, std of mean 2727.7
• Test: H0: sample is from population with µ ≥ 37365.3;
reject iff T < T* (where T is test result and T* is cutoff)
• Dog sample: Mean 17980
• T = 37365.3-17980/2727.7; P = 0.000000040
Reject!
• Severity = 1 - p = >99%
• Read this number as, “There is a more than 99%
chance that if this sample were human, we would
have obtained a different (larger) result.”

16. Severity example: Human sample
• Human reference standard from bootstrap runs:
Mean 37365.3, std of mean 2727.7
• Test sample Individual #33 humerus sample
Mean 44205.9
• Test: H0: sample is from population with µ ≥
37365.3; reject iff T < T* (where T is test result
and T* is cutoff)
• P = 0.99 Accept! But, how sure am I that
this specimen is human, not other animal?
• Bear in mind that all of the other species we have
measured, have smaller osteons…

17. • We can calculate the severity in multiple ways
that address varying concerns (varying
alternatives)—
• If my concern is that I will mistake a chimpanzee
sample for human with this test: Use chimp
sample mean and std as the basis for the severity
estimate.
• Chimp Mean 25377.6, std 2692.1
• Distance stat given the test sample result of
44205.9 is T = (44205.9-25377.6)/2692.1
• Using normal distribution,
P(T < observed T; chimp) = 0.99999
• Severity = 0.99999

18. Severity curve for test sample result--