Pediatric Clinics of North America - CIPERJ

300 O’BRIEN
cost-effectiveness and cost-utility analyses. The database describes each
study and appraises study quality, strengths, and weaknesses.
Evaluating the decision analysis literature
The number of published decision analysis articles has increased steadily
over the past 2 decades. Also, the demand for economic evaluations in health
care has increased the use of modeling tools to assess the cost-effectiveness
of new drug therapies and treatment strategies [11]. Therefore, it is important
for all physicians to be able to analyze the results of these studies critically.
An excellent guide to interpreting a clinical decision analysis has been published
by Richardson and Detsky, as part of the Users’ Guides to the Medical
Literature series in the Journal of the American Medical Association [22,34].
This section briefly summarizes those recommendations.
First, readers must be able to assess the validity of a decision analysis
model. The structure of any decision tree should mirror as closely as possible
a real-life clinical dilemma. All available and important clinical strategies
should be considered, and the strategies need to have competing benefits
and risks in order for the dilemma to be meaningful and worth studying.
Just as with a systematic review, the investigators of a clinical decision analysis
should describe how they searched and reviewed the literature to estimate
probabilities, and this methodology should be explicit and reproducible. Analysts
should describe how they judged the quality of the available data and if
and how any data were transformed. For example, 5-year DVT recurrence
rates may have been adjusted to fit a model with a 2-year timeline. Investigators
also should report the source of cost and utility estimates. The most credible
utility ratings come from the following sources: (1) direct measurements
from a large group of patients who had the disease in question or the general
public or (2) published studies of quality-of-life ratings from patients who
had the disease in question. Finally, readers always should assess how the investigators
determined the impact of uncertainty in the model. All probability,
cost, and utility estimates should be tested in at least a one-way sensitivity
analysis, and readers should take note of which variables, if any, altered the
optimal strategy. The more robust the model, the more confident readers can
be that a recommended strategy is in fact the optimal choice.
In the second part of the series, Richardson and Detsky [22] describe how
to interpret the results and generalizability of a clinical decision analysis. In
a decision analysis, any one clinical strategy can be chosen as a preferred
strategy or there may be a toss-up between two or more strategies. Readers
must decide if the difference between strategies is important clinically. Previous
studies have suggested that a gain in life expectancy or QALYs of 2 or
more months can be considered significant [35,36]. Another rule of thumb in
decision analysis, as discussed previously, is that an intervention costing less
than $50,000 to $100,000 per QALY gained typically is considered cost effective.
Finally, readers need to ensure that their patients are similar to the