the measured SPF and the claim cannot
exceed 4.99. Figure 3 also shows that
below an SEM of 4, researchers can be
confident with the hypothesis that the
SPF should be greater than the reven-dication. In other words, researchers
can launch the official validity test on
10 volunteers with an acceptable level
of risk. In all other cases, it should be
preferable to improve the formula.
Conclusions
The methods for determining sun
protection factors in vivo are based,
by definition, on a biological response
of the human skin. To overcome the
intrinsic variation of these methods,
it is essential to use a large number
of volunteers and therefore involve
statistics but unless these concepts are
used every day, they often are poorly
understood or worse, misinterpreted.
This article discusses how these values
should be interpreted and explains what
they mean to formulators. For example,
it was remarkable to see the effects of
reducing the number of volunteers
from 10 to 5; according to the normal
distribution law, the average does
not change significantly but the %CI
increases by 73%.
In addition, the authors devised
a method to estimate the maximum
deviation, which is a value that cannot
be detected and thus a point rarely
mentioned in cosmetics. This is a second type of risk that allows researchers
to draw conclusions in the absence of
significant effects.
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References
Send e-mail to marc_pissavini@cotyinc.com.
1. European commission recommendation on the
efficacy of sunscreen products and the claims
made relating thereto, available at http://
eur-lex.europa.eu/LexUriServ/LexUriServ.d
o?uri=OJ:L:2006:265:0039:0043:en:PDF
(Accessed Jan 19, 2011)
2. Colipa recommendations N21–23, available
at: www.co
lipa.eu/publications-colipa-the-european-cosmetic-cosmetics-association/
recommendations.html (Accessed Jan 3, 2011)
3. www.fda.gov/ohrms/dockets/98fr/07-4131.
pdf (Accessed Jan 3, 2011)
4. Colipa website, 2006 International Sun
Protection Factor (SPF) Test Method,
available at www.co
lipa.eu/publications-colipa-the-european-cosmetic-cosmetics-association/ guidelines.html?view=item&
id=21%3Ainternational-sun-protection-
factor-spf-test-method-2006-cd-rom-
included&catid=46%3Aguidelines (Accessed
Jan 3, 2011)
5. A Hald, in Statistical Tables and Formulas,
John Wiley and Sons, New York (1952)
6. RA Fisher and F Yates, in Statistical Tables for
Biological, Agricultural and Medical Research,
Oliver and Boyd, London (1963)
7. F Wilcoxon, Individual comparisons by ranking
methods, Biometrics 1 80–83 (1945)
8. E Morice, Puissance de quelques tests clas-siques effectif d’echantillon pour des risques
alpha fixés, Revue de Statistique Appliquée
16 1 77–126 (1968)
9. M Pissavini et al, Determination of the in vitro
SPF, Cosmet & Toil 118, 63–72 (2003)
10. L Fageon, D Moyal, J Coutet and D Candau,
Importance of sunscreen products spreading
protocol and substrate roughness for in vitro
sun protection factor assessment, Int J Cosmet Sci 31 6 405–418 (2009)
11. PJ Matts et al, The COLIPA in vitro UVA
method: A standard and reproducible measure
of sunscreen UVA protection, Int J of Cosm Sci
32, 35-46 (2010)
Lab Practical:
Statistics Translated
• The arithmetic average of SPF
values indicates what the real
SPF value could be.
• The standard deviation of SPF
values gives an indication of a
pool of heterogeneity between
consumer SPF results and
uncertainty on measurements.
• The CI of SPF values gives an
indication of the area in which
the real SPF is assumed to be.
• During sunscreen product
development, the obtained
SPF value is compared with
the values of previous batches
according to the mean
SPF value and its standard
deviation.
Figure 3. Confidence abacus for comparison between observed SPF and target
estimation for 10 volunteers, using the results from 5 volunteers
184 | Cosmetics & Toiletries® magazine www.CosmeticsandToiletries.com
Vol. 126, No. 3/March 2011
