EPI-820 Evidence-based Medicine
Clinical Decision Analysis – Background
David R. Rovner MD
OVERVIEW: Questions having to do with uncertainty and probability are deeply
ingrained in the process of patient care and the fabric of clinical medicine. They arise from
several sources: the ambiguity of clinical data and variations in interpretations; uncertainty
about relations between clinical information and the presence of disease; uncertainty about
the effects of treatment; and errors in clinical data. Decision analysis is a systematic way of
making decisions under conditions of uncertainty. It organizes alternatives and choices to
incorporate both the probability of an outcome and the value or utility of that outcome.
Decision analysis is often the organizing foundation to performing cost-effectiveness and
cost-utility analysis. It often results in a “decision tree”.
RATIONALE FOR USE: Several heuristics and biases limit human’s ability to
interpret uncertain data. Among them are representativeness, anchoring and adjusting,
regression toward the mean, and conditional probability. Expected utility theory attempts
to help individuals to overcome these biases.
GOAL: To understand the analytic framework of decision analysis and it’s use in
clinical decision making.
OBJECTIVES:
 e familiar with terminology used in decision analysis.
 Understand the four steps of Clinical Decision Analysis (CDA).
 Understand how to define a question answerable by CDA.
 Be able to identify the information needed for CDA. Know the sources of data.
Understand the role of incidence, prevalence, ethnicity, prior patient history, and
personal sense of value or worth (utility) in constructing a decision tree.
 Be able to appropriately place the probabilities and utilities obtained on a decision
tree.
 Be able to calculate expected utility and understand its usefulness.
 Understand the concept and application of sensitivity analysis.
 e able to apply decision analysis techniques to a clinical problem.
TOOLS
Terminology
Decision node: A branch point on a decision tree where a decision must be made.
Chance node: A branch point on a decision tree where the results are governed by
probabilities or likelihood rather than choices. The sum of all probabilities emanating
from the chance node must sum to 1.0.
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Probability: May be thought of as either the frequency with which an event occurs in a
population or a measure of the strength of belief that an event will occur or that a state of
the world is true.
Odds: A ratio of the probability of an event happening divided by the probability that it
will not.
Utility: The value or expected worth of a particular outcome.
Expected outcome: The expected result of following a specific pathway in the tree.
Expected utility: The multiplied product of probability and utility. The expected utility
may apply to any chance or decision node.
Markov Analysis: A method of evaluating decision trees that uses time as a variable. It
can calculate expected utility at intervals chosen by the analysts.
Monte Carlo: A method of analysis that evaluates the decision tree by allowing a large
number of trips down the tree all determined by chance alone.
The article by Kattan et al. shows an up-to-date decision analysis about a subject that is
not settled by a large randomized control trial. The steps of the analysis are very similar
to those of the example appended below, except that the Markov method of analysis is
used. For this analysis computer techniques are almost mandatory.
Steps of CDA
1. Identify and bound the question:
What is the decision at hand. What should be considered including:
a. Which aspects of the patient's health are of particular concern.
b. Alternative actions - frequently not given enough thought.
c. What clinical information will be available to make the decision.
d. Possibly in this age, cost or more accurately benefit/cost.
Usually results in a list.
2. Structure in time
General decision tree. Includes boxes for decision points.
Circles for chance outcomes of decisions made. Line
between the former 2. It is read from left to right
Called a tree because main trunk and branches remind
people of a tree.
3. Obtain the data
a. Probability - Chance of a clinical event occurring. Made
up of baseline population values as modified by personal
factors.
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b. Utilities - Sense of worth or value as evaluated by a
particular individual. We suggest strongly that the patient
be the one to express this utility.
4. Choose the best outcome by evaluating the whole tree at one time by
maximizing expected utility.
Synthesizes the information
Requires nothing more than simple multiplication.
The mechanics of “solving” a decision tree include the following.
1. The decision tree must include all relevant alternatives and their outcomes.
2. The probabilities of the outcomes that could result from a decision must encompass
all possibilities. That is they must sum to 1. For example if patients who receive
surgery have a probability of dying of 0.1, then the probability of surviving must be
0.99 (thus .01 + .99 = 1).
3. To calculate an expected outcome, multiply the probabilities assigned to branches
extending from the chance node.
4. The value or utility of an outcome can be obtained. Utility is expressed as a number
between 0 and 1. In the medical literature several formal methods are used to
determine the utility of a health state. Among these are Standard Gamble, Time
Trade-Off and Visual Analog Scale. Of these the Visual Analog Scale or “Feeling
Thermometer” is the most direct and easiest to use, although not the most accurate.
Utilities can be assigned by the physician, patient, caregiver, or typical members of
society. If the Clinical Decision Analysis is for an individual patient the patient
should (in our opinion) give his or her Utility. Utilities for different outcomes do not
have to sum to 1.
5. To calculate an EXPECTED UTILITY of a chance node, multiply the probabilities
assigned to branches extending from the chance node by the utility of each outcome
and sum. At the decision node, pick the branch with the highest expected utility as the
one that a “rational” person would use as the best decision.
6. Perform a sensitivity analysis on probabilities and utilities because uncertainty always
exists. This is done by varying the literature or personally measured values over the
plausible range. If the decision changes because of a different hierarchy of expected
utilities, the decision is said to be sensitive to the particular values used. Using a hand
calculator one can easily calculate the expected utility of each branch at the extremes
of the probability range. One is better served by a computer program to calculate the
overall relationship between probabilities and expected utility, especially if one
wishes to graph the results.
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