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# 2007 ART Report Appendix A: Technical Notes

How to Interpret a Confidence Interval | Findings from Validation Visits for 2007 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 2007?
No success rate or statistic is absolute. Suppose a clinic performed 100 cycles among women younger than 35 in 2007 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 2007 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 2007 ART Data

Site visits for validation of 2007 ART data were conducted April through June 2009 for 35 randomly selected clinics. During each visit, data reported by the clinic were compared with information documented in medical records. In total, 1,655 cycles were randomly selected for full validation. These included 590 cycles that resulted in a pregnancy of which 475 resulted in a live-birth delivery. In addition, the validation team also reviewed the documentation for every reported cycle resulting in a multiple-fetus pregnancy.

Discrepancy rates are listed on the next page for key data items that were validated for each of the selected cycles. In all, validation indicated that data discrepancy rates were low (<5%; except for the data field for “Diagnosis of infertility”—see Comments for explanation). These discrepancies in a limited number of individual clinic summary tables had a minimal (e.g., by one or two decimal points) affect on statistics other than percentages of live births.

### Discrepancy Rates by Data Fields Selected for Validation

Data Field Name Discrepancy Rate* (Confidence Interval†) Comments
Patient date of birth 2.4%
(1.7–3.1)
Approximately one-third of the discrepancies resulted in a change of Age Group Category (see Clinic Summary Table classification), which mostly differed by one or two age categories.
Diagnosis of infertility 19.1%
(14.2–24.0)
For nearly one-fourth of the 294 discrepancies, multiple causes of infertility were found in the medical record when only a single cause was reported. For 57 discrepancies, multiple causes were reported when only a single cause was found in the medical record.
Type of ART
(i.e., fresh vs. frozen, donor vs. nondonor)
<1%
Use of ICSI <1%
Number of embryos transferred <1%
Outcome of ART treatment
(i.e., pregnant vs. not
pregnant)
1.8%
(0.6–3.0)
For approximately one-half of the 36 discrepancies, clinical pregnancy was documented in the medical record but not reported by the clinic; conversely another one-fifth were incidences of clinical pregnancy reported by the clinic but not documented in the medical record.
Number of fetal hearts on ultrasound 1.9%
(1.0–2.8)
Of the discrepancies, 13 cases resulted in a change in categorization of single- versus multiple-fetus pregnancy (4 from single- to multiple-fetus pregnancy and 9 from multiple- to single-fetus pregnancy).
Pregnancy outcome
(i.e., miscarriage, stillbirth,
and live birth)
4.4%
(2.1–6.6)
In most of these discrepancies, there was no information on pregnancy outcome in the medical record and more than one-half of the spontaneous abortions were reported as induced.
Number of infants born 3.5%
(1.4–5.6)
For almost all discrepancies, the medical record lacked information on the number of infants born. Other discrepancies included two singletons and two twin births that were found in medical records but were not reported by clinics. Also, one twin birth was incorrectly reported as a singleton.
Cycle cancellation <1%

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 those from smaller practices.

† This table shows a range, called the 95% confidence interval, that conveys the reliability of the discrepancy rate. For more information, see explanation of confidence intervals.