Georgetown University
Decision Analysis in Healthcare


Probability of Rare Events

In probabilistic risk analysis, the analyst often faces situations where the event of interest is quite rare (less than 5% chance of occurrence); perhaps it has happened only once in a decade.  This review focuses on how to accurately assess probabilities of rare events. 

In general there are two ways of assessing probabilities, both of which are not reasonable for assessment of rare probabilities.  The most common approach is to rely on observed frequency of the event.  This method cannot be applied to rare events as by definition rare events do not occur often and one has to accumulate a large data set before reliable estimates can be made.  For an event that occurs once a decade, one has to collect several decades of data before a reliable estimate can be obtained.

Alternatively, many rely on experts to assess probability of events.  But human beings are notoriously ill equipped to distinguish among very small probabilities.  In estimating rare probabilities, sometimes orders of magnitudes are missed; and probability of 1 in million is estimated as 1 in thousand.   An alternative is needed that overcomes difficulties in estimation of rare probabilities.


Before proceeding, it is important to clarify how would anyone know if the assessed probability of a rare event is accurate.  In general, accuracy of probabilistic forecasts are verified by calibration:  in numerous occasions in which the same probability is forecasted, the frequency of occurrence of the event is compared to the estimated probability.   For example, suppose a weather forecaster predicts that there is 80% chance of rain.  If it rains tomorrow, is this a reasonable forecast?  If it does not rain, is the forecast erroneous?  Neither of these questions can be answered.  In most situations, a single event cannot tell us much about the frequency of that event.  The accuracy of the forecast can only be established if in a large number of days, say 100 days, in which the weather forecaster has predicted 80% chance of rain, it does indeed rain for 80 days.  Only then we can claim that weather forecaster is well calibrated and accurate.

Obviously, the requirement to observe a large number of similar forecast makes it difficult to verify calibration of forecast of rare events: There are not enough such forecasts or observation of the event to compare the two.  So how could one assess the reasonableness of probability estimates for rare events?   In the case of rare events, it may be possible to assess accuracy of the probability of the rare event with a single observation to the contrary.  If a rare event occurs more frequently, we may have to revise our assessment of it.  If an event is expected to occur once every 100,000 occasions, then observing the occurrence of the event after 25 occasions will signal a problem with the estimate.  For example, Cooke (1991) reports that administrators of NASA had predicted the probability of shuttle failure at one in every 100,000 occasions.  Colglazier and Weatherwax (1983) had predicted such failure at one in every 35 flights.  When the Challenger Space Shuttle failed after only 25 flights, that NASA administrators were wrong in assuming shuttle failures would be very rare. 


In recent years, there have been many occasions in which risks of rare events have been assessed and subsequent events have helped confirm the accuracy of the risk analysis or improve aspects of the analysis.  Probabilistic risk analysis originated in aerospace industry.  One of the earliest comprehensive studies was started after the loss of life due to a fire in Apollo flight AS-204 in 1967.  In 1969, the Space Shuttle Task Group in the Office of Manned Space Flight of NASA suggested that the probability of loss of life should be less than 1 percent.  Colglazier and Weatherwax (1983) conducted a probabilistic risk analysis of shuttle flights.  But overtime, NASA administrators abandoned numerical forecast of risks as the projected risks were so high to undermine the entire viability of the operations.  Cooke (1991) and Bell and Esch (1989) report that NASA administrators "felt that the numbers could do irreparable harm."  But subsequent shuttle accidents returned the emphasis to probabilistic risk analysis.  Today almost all components of space shuttle go through independent risk analysis (Safaie 1991, 1992, 1994; Hoffman 1998; Planning Research Corporation, 1989, Science Applications International Corporation, 1993, 1995).  A good example of such risk analysis can be found in the work of Pate-Cornell and Fischbeck (1993, 1994), where they assessed the risk of tiles breaking away from the shuttle.  In this award winning study, the authors linked management practices to risks of various tiles on the shuttle. 

In nuclear safety, several studies have focused on reactor safety.  The first such study was the Reactor Safety Study (1975).  The study was followed by a series of critical reviews (Environmental Protection Agency, 1976; Union of Concerned Scientists, 1977, American Physical Society, 1975), including in 1997 a Congressional bill to mandate a review panel  to examine the limitations of the study.  The near failure of reactor core at Three Miles Island, however, proved that the scenarios anticipated in the study were indeed correct, though the probability of human failures were underestimated.   Not surprisingly, reviews of Three Miles Island re-emphasized the need for conducting probabilistic risk analysis (Rogovin and Frampton, 1980; Kemeny et al. 1979).  Kaplan and Garrick (1981) conducted a study of probability of reactor melt down.  In 1983, the U.S. Nuclear Regulation Commission issued a manual for how to conduct Probabilistic Risk Analysis.     Probabilistic risk analysis has also been used by the energy firms not focused on nuclear power to predict catastrophic events (Cooke, Jager 1998; Rasmussen, 1981; Ortwin, 1998). 

Probabilistic risk analysis has been applied to a variety of natural disasters including earthquake predictions (Chang, Shinozuka, Moore 2000), predicting floods and coastal designs (Voortman, van Gelder, Vrijling 2002; Mai, Zimmermann, 2003; Kaczmarek 2003 ), environmental pollution (Slob, Pieters 1998; Moore, Sample, Suter, Parkhurst, Scott, 1999).  A large number of studies focus on waste disposal and environmental health (Ewing, Palenik, Konikow 2004; Sadiq, Husain, Veitch, Bose. 2003; Cohen 2003; Garrick, Kaplan 1999).   In health care probabilistic risk analysis has focused on analysis of root causes of sentinel adverse events such as wrong side surgery or failure mode and effect analysis of near catastrophic events (Bonnabry, et. al 2005).   Amgen pharmaceutical has also used the procedure for deciding on new product development (Keefeer, 2001).  In failure mode analysis within health care most often the rank order of rare probabilities are assessed and the magnitude of the probability is ignored (DeRosier, Stalhandske, Bagian, Nudell 2002).  

The application to terrorism is new.  Taylor, Krings and Alves-Foss (2002) have applied probabilistic risk analysis to assessment of cyber terrorism risks.  Others have suggested the use of these techniques in assessment of terrorism ( Apostolakis, Lemon 2005; Haimes, Longstaff 2002).

Alternative Approaches to Assessing Probability of Rare Events

There are a number of methods available for assessing probability of rare events.  This review discuses four approaches:  use of fault trees, similarity judgments, importance sampling, and time to the event.  Each of these approaches are further discussed below. 

Fault Trees

The concept of fault trees and reliability trees has a long history in space and nuclear industry.  Several books (Krouwer, 2004) and papers describe this tool (Marx and Slonim, 2003).  The first step in conducting fault trees is to identify the sentinel adverse event that should be analyzed.  Then all possible ways in which the sentinel event may occur is listed.  It is possible that several events must co-occur before the sentinel event may occur.  For example, in assessing the probability of an employee providing information to outsiders, several events must co-occur.  First the employee must be disgruntled.  Second, information must be available to the employee.  Third, outsiders must have contact with the employee.  Fourth, the employee must have a method of transferring the data.  All of these events must co-occur before hospital data is sold to an outside party.  None of these events are sufficient to cause the sentinel event.  In a fault tree, when several events must co-occur, we use an "And" gate to show it.  Each of these events can, in part, depend on other factors.  For example, there may be several ways to transfer the data:  on paper, electronically by email, or electronically on disk.  Any one of these events can lead to transfer of data.  In fault tree when any one of a series of events may be sufficient by themselves to cause the next event to occur, we show this by an "Or" gate.  Fault tree is a collection of events connected to each other by "and" and "Or" gates.  Each event depends on a series of other related events, providing for a complex web of relationships.  A fault tree suggests a robust work process when several events must co-occur before the catastrophic failure occurs.  The more "And" gates are in the tree structure, the more robust the work process modeled.  In contrast, it is also possible for several events by themselves to lead to catastrophic failure.  The more "Or" gates in the path to failure, the less robust the work process. 

The second step is to estimate probabilities for the fault tree.  Since the catastrophic failure is rare, it is difficult to asses this probability directly.  Instead, the probability of various events leading to this failure are assessed.  For example, the probability of a finding a disgruntled employee can be assessed.  The probability of an employee having access to large data sets can be assessed by counting employees who have such access during the course of their work.  The probability of an employee being approached by someone to sell data can be assessed by providing an expert data on frequency of reported crimes and asking him/her to estimate the additional unreported rate.  In short, through objective data or subjective opinions of experts various probabilities in the fault tree can be assessed.   The fault tree can then be used to assess the probability of the catastrophic and rare event using the following formula:

P catastrophic failure = ij pi,j      

In the above formula, "j" represents all events that are related to each other through an "And" gate and "i" represents all events that are related to each other through an "Or" gate.  

Similarity Judgments

Sometimes, we are trying to predict an event that has no precedence but in some way and shape is similar to a previous rare event.  For example, prior to September 11th  attack on skyscrapers in New York city, terrorist tried to attack Eiffel tower by driving a hijacked plane into it.   The two incidences are similar in the sense that both are tall building, which have important symbolic values.  Both were attacked by a passenger jet, hoping that the jet fuel will lead to additional destruction.  They are of course also different incidences occurring for different reasons at different times in different places.  Should an analyst deduce from the attack on Eiffel tower that other similar attacks are likely? 

Consider another situation.  Recently, there has been an attack by terrorists on a school, where children were taken hostage and and surrounded by bombs.  Is it possible that a similar attack may occur in a hospital in United States and if so what is the probability of the attack.    The answer to this question depends on two factors.  First, what is the probability of an attack on a school?.  Second, how similar is the hospital scenario to the school situation? 

Similarity judgments can be used to extend probability of known rare events to new situations.  Psychologists have conducted numerous experiments showing that similarity of two situations will depend on features they share and features unique to each case (Mobus, 1979; Siegel, McCord, Crawford 1982; Schwarz, Tversky 1980; Catrambone, Beike, Niedenthal 1996).  In 1997, Tversky summarized the research on similarity and provided a mathematical model for judgments of similarity.  According to procedure suggested by Tversky similarity of two situations "i" and "j" can be assessed by listing the following three categories of features: 

  1. Features in the index case but not in the comparison case, fi, not j.
  2. Features in the comparison case but not in the index case, fnot i, j.
  3. Features in both cases, fi,j

Then similarity can be measured as the count of shared and not shared features using the following formula:

Sij = fi,j / [fi,j + a (fi, not j) + b (fnot i, j)]

In above formula, the constant "a" and "b" add up to 1 and are set based on whether the index case is defining prototype.  These constants if different from .5 allow the comparison case to be more like the index case than vice versa.  For example, they allow a child to be more like father than the father like the child.  For example, consider the similarity between the attack on a hospital in America and attack on the school in Russia.  First, we list features shared or unique across the two situations:

  1. Features in the index case (attack on school) and not in the comparison case (attack on hospital)
    • No proximity defense, easy access
    • No communication system available between rooms allowing terrorist time to collect large number of people
    • School age children.
  2. Features in the hospital attack scenario and not in the school case.
    • Difficulty in gathering the population into one central location
    • Availability of security officers
  3. Features shared in both
    • Focused on vulnerable population
    • An ongoing war leading to occupation of the region

While this list is brief, it highlights the procedure.  Once the list has been created the similarity of the two situations can be measured using the formula.  Assume that we let the constant "a" be 0.80 and the constant "b" be 0.20.  Then the similarity of the hospital situation to the school is calculated as:

 Similarity school, hospital = 2 / [2 + 0.80 (2) + 0.20 (3)] = 0.5

Please note that this is not the same as similarity of the school to the hospital, which is:

Similarity school, hospital = 2 / [2 + 0.80 (3) + 0.20 (2)] = 0.4

Based on this calculation, if we think that the probability of an attack on the school is, lets say 1 in 10,000; then the probability of attack on the hospital is:

Probability of attack on hospital = (1/10000) * 0.5 ≈ 5 in 100,000

Importance Sampling

One method of improving accuracy of estimates of rare events, is to purposefully examine the event in artificially constructed samples where the event is not rare (Heidelberger 1995, Glynn, Iglehart 1989; Srinivasan, 2002).  Then the frequency of the event in the sample can be extrapolate to the remaining situation proportional to how narrowly the sample was drawn.  The procedure is generally known as importance sampling and involves sampling data from situations where we expect to find the rare event.  Assume that we have taken "M" narrowly defined samples and sample "i" represents Wi cases in the population of interest.  If Pi is the probability of the event in the narrowly defined sample, then probability of the rare event, P,  can be calculated as: 

P = (i=1, …, M Wi Pi)/ i=1, …, M  Wi

An example may demonstrate this concept.  Suppose we want to estimate the probability of a successful theft of data by overcoming password protection in a computer.  For most organization such an attack is rare, but the attack is more likely to be seen in computers that are infected by a virus.  Suppose in an organization 1 in 100 computers have a major virus.  Also suppose that examination of data trails in these infected computers show that 0.3% involve loss of data.  What is the probability of loss of data anywhere in the organization?  This probability is calculated by weighting the narrow sample of infected computers to reflect the proportion of these computers inside the organization:

P = (1/100) * 0.003 + (99/100) * 0

Note that in this calculation we have assumed that loss of data does not occur in computers without virus infection.  This may be wrong but as a first approximation may be a reasonable step as we have anticipated that most data loss occurs among infected computers.   The importance weighting procedures requires us to know a priori, with high level of certainty, both the conditions under which the rare event are more likely to occur and the prevalence of the conditions. 

Time to Event

A method that can allow us to examine rare events directly is through examination of time to the event.  If we assume that an event has a Bernoulli distribution (i.e. the event either happens or does not happen, it has a constant probability of occurrence, and the probability of the event does not depend on prior occurrences of the event); then number of consecutive occurrences of the event has a Geometric distribution.  In a geometric distribution, probability of a rare event, p, can be estimated from the average time to the event, t, using the following formula:

p = 1 / (1+t)

Table 1 shows how this relationship can be explored to calculate rare probabilities.  The expert is asked to provide the dates for the last few times the event has occurred in the last year or decade.  The average time to reoccurrence is calculated and the above formula is used to estimate the probability of the event. 

ISO 17799 word

Frequency of event


Rare  probability


Once in a decade



Very low

2-3 times every 5 years




<= once per year




<= once per 6 months




<= once per month



Very high

=> once per week




=> one per day



Table 1:  Estimating probabilities from time between events

For example, suppose we want to know what is the probability of an a terrorist attack in city of Washington DC.  To calculate this probability, we need only to record the dates of the last attacks in the city and average the time between the attacks.  This average time between the reoccurrence of the event can then be used to estimate the probability of another attack.

For another example, suppose we do not know the frequency of medication errors in our hospital.  Furthermore, suppose that last year there were two reports of medication errors, one at start of the year and one in the middle of the year.  The pattern of medication error suggests 6 months time between errors.  Average time between errors allows us to estimate the daily probability of medication error:

P( Error) = 1 / (1+6*30) = 0.0056

Validity of Low Probability High Consequence Risk Analysis

Since there are no practical ways of observing very low probability events, it is difficult to evaluate the accuracy of our estimates.  Obviously, it is possible, that a contrary event (for example accidents occurring with more frequency than expected) will point out the inaccuracies in our estimation procedure. But in the absence of these contrary events, it is difficult to validate the probabilistic risk analysis findings.  To improve confidence in the assessment, any or all of the following additional steps can be taken:

  • Check the assumptions of the model. 

    For example, in fault tree often a series of events are linked to each other.  One could check that this is a reasonable link by examining the conditional independence among these serially linked events.  In an non-cyclical path if A is shown to affect B and B is shown to affect C, then C should be independent of A given a specific value for B.   Conditional independence can be checked by examining partial correlation between A and C, by querying the expert, or by examining causal graphs drawn by experts.  

  • Check the accuracy of the parameters of the model

    While it is not easy to measure the catastrophic event, it is possible to observe the probability of various events used in the model.  If these estimates are accurate, we have more confidence in the resulting prediction of the model.


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What Do You Know?

Advanced learners like you, often need different ways of understanding a topic. Reading is just one way of understanding. Another way is through writing. When you write you not only recall what you have written but also may need to make inferences about what you have read.  Please complete the following assessment:

1. What is the probability of an event that has occurred once in the last year?

2. What is the probability of an event that last occurred 3 months ago? 

3. What assumption did you make in answering question 2?

Your Contact Information

Please select your  name from following list.  If your name does not appear here, you are not currently on the student roster and need to contact your instructor.



To assist you in reviewing the material in this lecture, please see the following resources:

  1. Listen to lecture on subjective probabilities

  2. Listen to the lecture on independence & Bayes odds form

  3. Listen to the lecture on assessing rare probabilities through time to the event

  4. See the slides for all three lectures. 

Narrated lectures require use of Flash.

Recently Asked Questions

  Get answers to questions about decision analysis

In this section, you will find answers to questions asked by you or others.

Question:  When asking two experts to evaluate scenarios, how much detail must be given in each scenario? Could you elaborate, and give me other examples?  Answer: Scenarios should be realistic, and should include at least attributes that are used by your model. For example, if a decision is to select/rank purchases, the expert should be provided with what is normally available in such a case.

Question:  how can you measure if an event is rare or not?  Answer: Most people will consider a probability of less than 5% as indication of a rare event

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Copyright 1996 Farrokh Alemi, Ph.D. Created on Tuesday, September 17, 1996. Sunday, October 06, 1996 4:20:30 PM Most recent revision 09/29/2008. This page is part of a course lecture on Assessing Probability of Rare Events.