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2006 Assisted Reproductive Technology (ART) Report: Appendix A |
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How to
Interpret a Confidence Interval |
Findings from
Validation Visits for 2006 ART Data |
Discrepancy Rates by Data Fields Selected for Validation
How to Interpret a
Confidence Interval
What is a confidence interval?
Simply speaking, confidence intervals are a useful way to consider margin
of error, a statistic often used in voter polls to indicate the range
within which a value is likely to be correct (e.g., 30% of the voters
favor a particular candidate with a margin of error of plus or minus
3.5%). Similarly, in this report, confidence intervals are used to
provide a range that we can be quite confident contains the success rate
for a particular clinic during a particular time.
Why do we need to consider confidence intervals if we already know
the exact success rates for each clinic in 2006?
No success rate or statistic is absolute. Suppose a clinic performed 100
cycles among women younger than 35 in 2006 and had a success rate of 20%
with a confidence interval of 12%–28%. The 20% success rate tells us
that the average chance of success for women younger than 35 treated at
this clinic in 2006 was 20%. How likely is it that the clinic could
repeat this performance? For example, if the same clinic performed
another 100 cycles under similar clinical conditions on women with
similar characteristics, would the success rate again be 20%? The
confidence interval tells us that the success rate would likely fall
between 12% and 28%.
Why does the size of the confidence interval vary for different
clinics?
The size of the confidence interval gives us a realistic sense of how
secure we feel about the success rate. If the clinic had performed only
20 cycles instead of 100 among women younger than 35 and still had a 20%
success rate (4 successes out of 20 cycles), the confidence interval
would be much larger (between 3% and 37%) because the success or failure
of each individual cycle would be more significant. For example, if just
one more cycle had resulted in a live birth, the success rate would have
been substantially higher — 25%, or 5 successes out of 20 cycles.
Likewise, if just one more cycle had not been successful, the success
rate would have been substantially lower — 15%, or 3 out of 20 cycles.
Compare this scenario to the original example of the clinic that
performed 100 cycles and had a 20% success rate. If just one more cycle
had resulted in a live birth, the success rate would have changed only
slightly, from 20% to 21%, and if one more cycle had not been
successful, the success rate would have fallen to only 19%. Thus, our
confidence in a 20% success rate depends on how many cycles were
performed.
Why should confidence intervals be considered when
success rates from different clinics are being compared?
Confidence intervals should be considered because success rates can be
misleading. For example, if Clinic A performs 20 cycles in a year and 8
cycles result in a live birth, its live birth rate would be 40%. If
Clinic B performs 600 cycles and 180 result in a live birth, the
percentage of cycles that resulted in a live birth would be 30%. We
might be tempted to say that Clinic A has a better success rate than
Clinic B. However, because Clinic A performed few cycles, its success
rate would have a wide 95% confidence interval of 18.5%–61.5%. On the
other hand, because Clinic B performed a large number of cycles, its
success rate would have a relatively narrow confidence interval of
26.2%–33.8%. Thus, Clinic A could have a rate as low as 18.5% and Clinic
B could have a rate as high as 33.8% if each clinic repeated its
treatment with similar patients under similar clinical conditions.
Moreover, Clinic B’s rate is much more likely to be reliable because the
size of its confidence interval is much smaller than Clinic A’s.
Even though one clinic’s success rate may appear higher than
another’s based on the confidence intervals, these confidence
intervals are only one indication that the success rate may be better. Other
factors also must be considered when comparing rates from two
clinics. For example, some clinics see more than the average number of
patients with difficult infertility problems, whereas others discourage
patients with a low probability of success. For more information see
important factors to consider when using the tables to
assess a clinic.
Findings
from Validation Visits for 2006 ART Data
Clinic site visits for validation of 2006 ART data were
conducted April through June 2008. During each visit, data reported by
the clinic were compared with information recorded in patients’ charts.
Records for 1,644 cycles at 35 clinics were randomly selected for
validation. These selected
cycles included 533 cycles that resulted in a pregnancy and 434 cycles
that resulted in a live-birth delivery.
Discrepancy rates are listed below for key data
items that were validated for each of the selected cycles. Review of the
discrepancies indicated that in the majority of cases, the error did not
affect the success rates (included in the national summary table and in the
individual clinic tables). In addition to fully validating data for the
randomly selected 1,644 cycles, during each visit the validation team also
reviewed the documentation for every live birth that had been reported to
CDC. There were no cases found in which a live birth had been reported
erroneously. In all, validation indicated that the clinic success rates
presented in this report are valid.
Discrepancy Rates by Data Fields Selected for Validation
Data
Field Name |
Discrepancy
Rate*
(Confidence Interval†) |
Comments |
Patient date of birth |
2.0%
(1.4–2.6) |
Nearly one-third of the discrepancies resulted in a change of Age
Group Category (see Clinic Summary Table classification) and
differed only by one age category. |
Diagnosis of infertility |
16.7%
(13.2–20.2) |
For approximately one-third of the total 283 discrepancies, multiple
causes of infertility were found in the patient’s chart, but only a
single cause was reported. For 44 discrepancies, multiple causes
were reported but only a single cause was found in the patient’s
chart. |
Type of ART (i.e., fresh
vs.
frozen; donor vs. nondonor) |
<1% |
|
Use of
ICSI |
1.6%
(0.8–2.3) |
For about three-fourths of these discrepancies, use of ICSI was
indicated in the patient’s chart but was not reported. |
Number of
embryos
transferred |
1.6%
(0.6–2.7) |
Nearly all discrepancies differed by one or two embryos. |
Outcome of ART treatment
(i.e., pregnant vs. not pregnant) |
<1% |
|
Number of fetal hearts on
ultrasound |
1.3%
(0.5–2.1) |
Of the discrepancies, six cases resulted in a change in
categorization of single- versus multiple-fetus pregnancy. |
Pregnancy outcome
(i.e., miscarriage, stillbirth, and live birth) |
1.1%
(0.5–1.8) |
In most of these discrepancies, there was no information on
pregnancy outcome in the patient’s chart, or spontaneous abortion
was reported as induced abortion, or vice versa. |
Number of infants born |
1.1%
(0.7–1.4) |
In more than half of the discrepancies, there was no information on
the number of infants born in the patient’s chart. In one case, a
twin delivery was recorded in the patient’s chart when a singleton
delivery was reported. |
Cycle cancelation |
<1% |
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Notes: ART = assisted
reproductive technology; ICSI = intracytoplasmic sperm injection.
* Discrepancy rates estimate the proportion of all treatment cycles
with differences for a particular data item. The discrepancy-rate
calculations weight the data from validated cycles to reflect the
overall number of cycles performed at each clinic. Thus, findings
from larger clinical practices were weighted more heavily than
findings from smaller practices.
† This table shows a range, called the 95% confidence
interval, that conveys the reliability of the discrepancy rate. For a more
information, see
explanation of confidence intervals. |
Previous ART Reports
Implementation of the Fertility
Clinic Success Rate and Certification Act of 1992 Page last reviewed: 12/3/08
Page last modified: 12/3/08
Content source: Division
of Reproductive Health,
National Center for Chronic Disease
Prevention and Health Promotion
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