Pediatric Clinics of North America - CIPERJ

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contains the values an analyst believes are closest to the actual state of affairs.
Analyzing a decision tree involves comparing the overall benefits expected
from choosing each strategy, defined as the expected utility [22].
For example, in Fig. 1, assume that Medication A has an 80% chance of
success and, therefore, a 20% chance of failure. If the medication succeeds,
the patient has perfect health or a utility of 1. If the medication fails, the patient
remains ill and has a utility of 0.7. The expected utility for this chance
node is the sum of the product of each of the probabilities multiplied by its
utility, or (0.8 1) þ (0.2 0.7) ¼ 0.94. This process is repeated for every
chance node, moving from right to left. The process is called folding back or
rolling back and typically performed using decision analysis software. The
expected utility can be measured using any scale an analyst chooses: number
of blood transfusions, gain in life expectancy, or quality of life (utilities), as
chosen in this example. Details on the rolling back process can be found in
several reviews [10,23,24].
The end result of rolling back is that each clinical strategy in the model is
assigned a final value. In a decision analysis for which morbidity or mortality
is the primary outcome measure, the strategy with the lowest value is the
preferred option. If QALYs are the primary outcome measure, the strategy
with the highest value is preferred. In a cost-effectiveness analysis (the most
common type of decision analysis), the software program presents each clinical
strategy in ascending order by total cost and compares the strategies using
an incremental cost-effectiveness ratio. This ratio is defined as the extra
cost of a strategy divided by its extra clinical benefit as compared with the
next least expensive strategy. Any strategy that costs more but is less effective
than an alternative strategy is considered dominated and removed from
further consideration. Although there is no absolute threshold for costeffectiveness,
incremental cost-effectiveness ratios of less than $50,000 to
$100,000 per healthy life year (QALY) gained typically are considered
cost effective [25]. These proposed ratios, more than 20 years old, however,
have not been adjusted for inflation and have not been considered independently
for the pediatric population [26].
In most decision analysis studies, there is some uncertainty about the inputs
used in model construction. Sensitivity analysis, always performed after
the base-case analysis, is an important tool for handling the uncertainty inherent
in any decision analysis model and evaluates the effect of alternative
assumptions on the final result. In this process, the probabilities, costs, and
utilities of a model can be changed systematically, and the results of the
analysis are recalculated multiple times. For example, in Fig. 1, the probability
of treatment success with Medication A can be changed from 80% to
50%, 90%, or any other number chosen by the analyst. If changing a variable
over a reasonable range of values changes the preferred strategy, the
model is considered sensitive to that variable. Typically, a model is sensitive
to variation of some parameters and insensitive to variation of others. A
model that is insensitive to variation of most parameters is a robust model.