From Alzheimer’s to Zebrafish: Eclectic Science and Regulatory Stories 80
The works of Cardano, Pascal, de Fermat and Huygens were augmented by other
famous individuals in the late 17th and early 18th centuries. John Graunt (1620–74),
William Petty (1623–87)and Edmund Halley (the famous astronomer, 1656-1742) each
applied the concept of probability to the analysis of raw data.16 Graunt was the innovator
of sampling theory and his efforts laid the foundation for the science of statistics.17 His
analysis today is known as statistical inference—inferring a global estimate for a sample
of data. It was Graunt who suggested key theoretical concepts needed to make decisions
under conditions of uncertainty. William Petty helped Graunt with his work on popula-
tion statistics. Halley carried the analysis even further while analyzing life expectancies
and annuities.18
Other mathematicians contributed to the development of probability theory over the
intervening years. The Bernoulli family (eight members in all) distinguished themselves,
and three made major contributions. Jakob (1654–1705), derived the law of large num-
bers, which states that the relative frequency of an event will more accurately predict the
likelihood of its occurrence. Some years later, Jakob Bernoulli’s ideas were taken up by
Abraham de Moivre (1667–1754), a French mathematician. De Moivre showed how a col-
lection of random observations would distribute themselves around their average value
(the normal distribution). His measure allows users to judge whether a set of observations
is sufficiently representative of the entire population.19
Today, we can use probability to measure the likelihood of being correct when
predicting one particular event. This ingenious and powerful mathematical formula was
developed by an obscure eighteenth century minister in England, Thomas Bayes (1702–
61). Like Cardano, Bayes published no original mathematics during his life. When he
died, he bequeathed his papers to a friend. One document, entitled “Essay towards solving
a problem in the doctrine of chances,” outlined a radically new way to approach and com-
pute probabilities. Bayes’ method, however, was largely ignored by statisticians. This all
changed in the 1970s because of the availability of powerful computers and the ability to
perform iterative processes.20 FDA finally recognized the value of Bayesian statistics and
has issued guidance for its use in clinical trials.21
Final Thoughts
Many of us are innumerate, or not mathematically inclined, a fact that should make us
even more appreciative of pioneering individuals who were. These scientists and lay peo-
ple from earlier centuries helped us develop the theory of risk management—a concept
that influences all of the processes in our daily and professional lives. Their seminal work
involved a process to effectively identify, analyze and control risks. This process incorpo-
rated in the term “risk management” is the primary consideration for all of us involved in
healthcare delivery.
References
1. Federal Register Vol. 61, No. 195, Monday, 7 October 1996.
2. Design Control Guidance for Medical Device Manufacturers. 11 March 1997.
3. ISO 14971 Medical Devices—Application of risk management to medical devices. Second Edition 2007-03-01.
4. GHTF Final Document—Implementation of risk management principles and activities within a Quality Management
System. 20 May 2005.
5. Kimmelman E. “The challenges of integrating risk management into a compliant quality management sys-
tem.” RAJ Devices, Jul/Aug 2006:207–214.
6. Medical Device Use—Safety: Incorporating Human Factors Engineering into Risk Management. US Food and Drug
Administration, Center for Devices and Radiological Health, Division of Device User Programs and Systems
Analysis, Office of Health and Industry Programs.18 July 2000.
7. Managing the Risks from Medical Product Use—Report to the Commissioner from the Task Force on Risk
Management, Food and Drug Administration, May 1999.
8. Op cit 5.
9. Bernstein PL. Against the Gods—The Remarkable Story of Risk. John Wiley and Sons, New York, 1996.
10. Wilson R and Crouch EAC. Risk-Benefit Analysis. Harvard University Press, Cambridge, MA, 2001.
The works of Cardano, Pascal, de Fermat and Huygens were augmented by other
famous individuals in the late 17th and early 18th centuries. John Graunt (1620–74),
William Petty (1623–87)and Edmund Halley (the famous astronomer, 1656-1742) each
applied the concept of probability to the analysis of raw data.16 Graunt was the innovator
of sampling theory and his efforts laid the foundation for the science of statistics.17 His
analysis today is known as statistical inference—inferring a global estimate for a sample
of data. It was Graunt who suggested key theoretical concepts needed to make decisions
under conditions of uncertainty. William Petty helped Graunt with his work on popula-
tion statistics. Halley carried the analysis even further while analyzing life expectancies
and annuities.18
Other mathematicians contributed to the development of probability theory over the
intervening years. The Bernoulli family (eight members in all) distinguished themselves,
and three made major contributions. Jakob (1654–1705), derived the law of large num-
bers, which states that the relative frequency of an event will more accurately predict the
likelihood of its occurrence. Some years later, Jakob Bernoulli’s ideas were taken up by
Abraham de Moivre (1667–1754), a French mathematician. De Moivre showed how a col-
lection of random observations would distribute themselves around their average value
(the normal distribution). His measure allows users to judge whether a set of observations
is sufficiently representative of the entire population.19
Today, we can use probability to measure the likelihood of being correct when
predicting one particular event. This ingenious and powerful mathematical formula was
developed by an obscure eighteenth century minister in England, Thomas Bayes (1702–
61). Like Cardano, Bayes published no original mathematics during his life. When he
died, he bequeathed his papers to a friend. One document, entitled “Essay towards solving
a problem in the doctrine of chances,” outlined a radically new way to approach and com-
pute probabilities. Bayes’ method, however, was largely ignored by statisticians. This all
changed in the 1970s because of the availability of powerful computers and the ability to
perform iterative processes.20 FDA finally recognized the value of Bayesian statistics and
has issued guidance for its use in clinical trials.21
Final Thoughts
Many of us are innumerate, or not mathematically inclined, a fact that should make us
even more appreciative of pioneering individuals who were. These scientists and lay peo-
ple from earlier centuries helped us develop the theory of risk management—a concept
that influences all of the processes in our daily and professional lives. Their seminal work
involved a process to effectively identify, analyze and control risks. This process incorpo-
rated in the term “risk management” is the primary consideration for all of us involved in
healthcare delivery.
References
1. Federal Register Vol. 61, No. 195, Monday, 7 October 1996.
2. Design Control Guidance for Medical Device Manufacturers. 11 March 1997.
3. ISO 14971 Medical Devices—Application of risk management to medical devices. Second Edition 2007-03-01.
4. GHTF Final Document—Implementation of risk management principles and activities within a Quality Management
System. 20 May 2005.
5. Kimmelman E. “The challenges of integrating risk management into a compliant quality management sys-
tem.” RAJ Devices, Jul/Aug 2006:207–214.
6. Medical Device Use—Safety: Incorporating Human Factors Engineering into Risk Management. US Food and Drug
Administration, Center for Devices and Radiological Health, Division of Device User Programs and Systems
Analysis, Office of Health and Industry Programs.18 July 2000.
7. Managing the Risks from Medical Product Use—Report to the Commissioner from the Task Force on Risk
Management, Food and Drug Administration, May 1999.
8. Op cit 5.
9. Bernstein PL. Against the Gods—The Remarkable Story of Risk. John Wiley and Sons, New York, 1996.
10. Wilson R and Crouch EAC. Risk-Benefit Analysis. Harvard University Press, Cambridge, MA, 2001.