Page 337

Technical Details of Power Calculations and Meta-Analyses

Many of the adverse events related to pertussis and rubella
vaccines share the characteristic of rareness. Most of the events
in question are infrequent to begin with, and the excess risk that
may be associated with vaccination is small. As mentioned in
Chapter 3, it is often difficult in such situations to distinguish
scientifically between *no excess risk* and *no detected
excess risk.* Because of the committee's focus on fairly
characterizing the uncertainty in the available data, special
attention was given to power analysis, a statistical tool that can
help to distinguish between these two possibilities. This appendix
describes and illustrates the power calculation methods used by the
committee to take account of the diverse statistical methods used
in the studies on which the analyses are based.

The results of epidemiologic studies are generally reported in
terms of *relative risks* (RRs) or *odds ratios.* Because
the odds ratio was considered to be an estimate of the RR in the
context of this report (see Chapter 3), the term RR is used in the
descriptive text to refer to both measures in the report of power
analyses. For the purpose of these calculations, it was assumed
that, in every study, the sampling distribution of the logarithm of
the odds ratio or RR (noted as *Y)* has a normal distribution
with a standard deviation equal to the estimated standard error
(Fleiss, 1981, pp. 61-67). In order to calculate power statistics
from published results, the committee took the following two
steps.

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D
Technical Details of Power Calculations and Meta-Analyses
POWER CALCULATIONS
Many of the adverse events related to pertussis and rubella
vaccines share the characteristic of rareness. Most of the events
in question are infrequent to begin with, and the excess risk that
may be associated with vaccination is small. As mentioned in
Chapter 3, it is often difficult in such situations to distinguish
scientifically between no excess risk and no detected
excess risk. Because of the committee's focus on fairly
characterizing the uncertainty in the available data, special
attention was given to power analysis, a statistical tool that can
help to distinguish between these two possibilities. This appendix
describes and illustrates the power calculation methods used by the
committee to take account of the diverse statistical methods used
in the studies on which the analyses are based.
The results of epidemiologic studies are generally reported in
terms of relative risks (RRs) or odds ratios. Because
the odds ratio was considered to be an estimate of the RR in the
context of this report (see Chapter 3), the term RR is used in the
descriptive text to refer to both measures in the report of power
analyses. For the purpose of these calculations, it was assumed
that, in every study, the sampling distribution of the logarithm of
the odds ratio or RR (noted as Y) has a normal distribution
with a standard deviation equal to the estimated standard error
(Fleiss, 1981, pp. 61-67). In order to calculate power statistics
from published results, the committee took the following two
steps.

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First, where possible, standard errors for Y were derived from confidence
intervals reported by original investigators. In this way, variance
reduction techniques such as matching of cases and controls are
accurately reflected in the estimated standard error. In
particular, if the reported odds ratio or RR is R, and the
upper limit of its 95 percent confidence interval is U, the
standard error was estimated as s
= ln(U/R)/1.96. For studies in which no confidence interval was
calculated by the original authors, the committee calculated an RR
as appropriate and an associated confidence interval using standard
methods for 2 x 2 tables (Fleiss, 1981, pp. 61-67) and applied the
same procedure described above to estimate the standard
error.
Second, 50 and 80 percent power levels for the RR were calculated as follows. Under the null hypotheses of no association, the expected value of Y,
E(Y), is 0.0 and the critical point for a two-sided test
with a = 0.05 is 1.96s. If E(Y) were equal to 1.96s, there would be a 50 percent chance that the
test would detect an elevated risk; thus, the RR for which there is
50 percent power was calculated as e1.96s. To achieve 80 percent power, E(Y)
must be 0.84s above the critical point,
or 2.80s. Thus, the RR for which there
is 80 percent power was calculated as e2.80s.
To illustrate this approach, Table D-1 shows the results of these calculations for the SONIC study relating DPT use to afebrile seizures (Gale et al.,
1990; see also Table 4-5). The estimated RRs range from 0.5 to 0.8,
and the upper confidence limits range from 1.1 to 1.5. On the basis
of these results, the power calculations show that the SONIC study
had a 50 percent chance of detecting an RR for afebrile seizures
within 7 days of 3.0 and an 80 percent chance of detecting an RR
within 7 days of 4.8. Thus, a relatively large increase in the risk
of afebrile seizures could have gone unde-
TABLE D-1 Power Calculations for the SONIC Study Relating DPT Use to Afebrile Seizures
Powerc
Time
Period
RR
Upper
CIa
sb
50%
80%
Within 7 days
0.5
1.5
0.56
3.00
4.80
Within 14 days
.08
1.5
0.32
1.88
2.45
Within 28 days
0.6
1.1
0.31
1.83
2.38
aCI, Confidence interval.
bs , Standard
error.
c"Power" denotes the probability that a
statistical test based on a sample of the same size as the one in
the study cited would find a statistically significant increased
risk (with alpha = 0.05), given that the true RR in the population
being studied is the number stated in the table. The numbers
tabulated are the RRs such that the powers are 50 and 80 percent,
respectively.

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tected in a study
of this size. The committee could only conclude that this study
provided no evidence of a large effect.
A different
approach was taken to calculate the power of the statistical tests
used in a retrospective epidemiologic study by Shields and
colleagues (1988). As described in Chapters 4 and 5 of this report,
Shields and colleagues ascertained the age distribution of cases of
SIDS and of a number of neurologic disorders in Denmark during two
time periods with different vaccination schedules. During the
1967-1968 time period, DPT was given at ages 5, 6, 7, and 15
months; in 1972-1973, DPT was given at ages 5 and 9 weeks and 10
months. Shields and colleagues recorded the number of adverse
events occurring in the following age intervals: 1 to 3, 4 to 8, 9
to 11, 12 to 14, 15 to 19, and 20 to 23 months. Although Shields
and colleagues tested whether the entire distributions of cases
differed between the two time periods, the committee's power
calculations were based on a comparison of the proportions in two
noncontinuous age groups.1 Group 1 was defined as age 4
to 8 months and age 12 or more months, so that a possible increase
in the number of cases consistent with the 1967-1968 vaccination
schedule could be detected. Group 2 was defined as ages 1 to 3
months and 9 to 11 months, so that a possible increase in the
number of cases consistent with the 1972-1973 vaccination schedule
could be detected.
The power
calculations are based on the assumption that if there is an
increase in the risk of the adverse event shortly following DPT
vaccination, the proportion of cases in the time period in which
most of the vaccinations take place should increase. More
precisely, define pi as the expected number of
cases in age group 1 in time period i, p0 as the expected proportion of
non-vaccine-associated cases in the same group (independent of time
period), and qi as the proportion of
vaccinations in the same age group and time period. Furthermore,
suppose that a proportion k/(1 + k) of the adverse
events in an age-period group are caused by the vaccines so that
the expected value of pi equals (p0 + qi k)/(1 + k). Under
these assumptions, given qi and the number of vaccinations
administered in each time period as reported by Shields and
colleagues (1988), one can calculate the expected difference
between the two time periods in the proportion of cases in age
group 1, p2 - p1, its standard deviation, and thus the power of
the test for a given value of k. Such calculations were performed
for a range of appropri-
1 A more general version of
the power calculations involving more than two groups was developed
by Frederick Mosteller and Elizabeth Burdick of Harvard University
(personal communication, 1991), and formed the mathematical basis
of the simplified model used by the committee. The more complete
model requires computer simulations for evaluation and is, thus,
more complicated to implement. It was found, however, to yield
results similar to those of the simplified model used
here.

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ate values of k, and the values of k for which 50
and 80 percent powers were achieved were reported.
META-ANALYSES
Formal Analysis
As reported in Chapter 3, the committee found nine studies that
offer some information on the timing of SIDS relative to
vaccination. The following section describes in detail the methods
used by the committee to perform a meta-analysis of these data.
Part of the approach relies on standard methods for meta-analysis
of clinical and epidemiologic data developed by DerSimonian and
Laird (1986). But, because only four of the studies correct for the
age pattern of SIDS (which would lead one to expect more SIDS cases
in the first few days after vaccination than a uniform distribution
would predict), an additional step was needed to adjust data from
the poorly controlled studies.
The adjustment for the age pattern of SIDS was based on three
studies that have proper controls for the age pattern of SIDS and
roughly similar divisions of the time between vaccination and
death: Griffin et al. (1988), Solberg (1985), and Walker et al.
(1987). These studies indicate the number of observed and expected
cases in three subdivisions of the (roughly) first month after
vaccination: "early" is 0 to 3 days, "mid" is 4 to 7 days, and
"late" is the rest of the month (which varies from 28 to 30 days).
The study by Hoffman and colleagues (1987) has appropriate controls
but is given in time intervals with breakpoints at 24 hours and 14
days, so it was not used in this part of the analysis.
In order to estimate an adjustment factor for the age pattern of
SIDS, the fraction of expected cases in each period was compared to
the fraction that would be expected if the SIDS deaths were
uniformly distributed over time. The result is as expected: there
are more deaths among the controls in the early and mid periods
than a uniform distribution would predict. The ratios vary across
the three studies, but all are in the predicted direction. The
simple averages of the three ratios are as follows: early = 1.05,
mid = 1.15, and late = 0.96.
These average ratios were then used to correct the five studies
(Baraff et al., 1983; Bernier et al., 1982; Pollock et al., 1984;
Taylor and Emery, 1982; Torch, 1982) that did not have appropriate
controls, as follows. For each study, the number of cases that
would have been expected in each interval under the uniform
distribution was calculated first. Note that the "early" period is
0 to 2 days in one study, and the end of the "late" period ranges
from 21 to 42 days. The average ratios from the first three studies
were then applied to calculate an adjusted expected number of cases
in each period.

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To summarize these results in a meta-analysis, the committee
chose to use the DerSimonian and Laird (1986) approach to analyzing
log odds ratios with appropriate sensitivity analyses for the major
assumptions. The basic steps in this approach include (1)
calculating an odds ratio for each study, comparing the proportion
of deaths in the early period between cases and controls; (2)
calculating a weighted average of the log odds ratios in which the
weights reflect the variance of the individual estimates of the log
odds ratios; and (3) calculating an additional weighted average
based on a random-effects model, that is, assuming that the
observed odds ratios are chosen at random from a population of odds
ratios that would be obtained in similar studies. The DerSimonian
and Laird approach also produces standard errors and confidence
intervals for each weighted average. Because a number of
statistical assumptions are possible, an analysis was performed to
assess the sensitivity of the qualitative results to the
assumptions.
The meta-analysis was based on the odds ratios that compared the
number of deaths in the early period to the number in the early and
late periods combined. Deaths in the mid period were excluded from
the analysis because (1) the study of Hoffman and colleagues had no
mid period, and (2) it was not clear whether mid-period deaths in
the other studies should be aggregated into the early or late
periods.
Because sample sizes in the studies of Taylor and Emery and
Pollock and colleagues are so small (1 and 0 observed cases,
respectively, in the early period), these two studies were not
included in the analysis. Three alternative assumptions were made
about the study of Hoffman and colleagues, which has two different
control groups as well as a very different set of time breakpoints:
(1) the results with both control groups are included as two
separate studies, (2) only the results from the more highly matched
control group B are included in the meta-analysis, and (3) no data
from Hoffman and colleagues are included.
In four of the studies (Baraff et al., 1983; Bernier et al.,
1982; Solberg, 1985; Torch, 1982) the expected numbers of cases are
based on calculated distributions rather than on a sample of
controls. In these cases, the committee assumed that only the
observed proportion in the early period contributed to the standard
error. This means that the confidence intervals from these studies
understate the true uncertainty by an unknown amount. For the other
three studies, the committee calculated standard errors without
taking into account the matching and other variance reduction
techniques that were actually used in the study. This implies that
the resulting confidence intervals overstate the true uncertainty,
again by an unknown amount.
The odds ratios for the individual studies, as shown in Figure
D-l, range from 0.60 to 3.36. As Figure D-1 shows, the 95 percent
confidence intervals for these odds ratios differ markedly from
study to study. Some of the

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FIGURE D-1 Odds ratios and 95 percent
confidence intervals
for the risk of SIDS in the early period postvaccination for
the
seven studies included in the meta-analysis.
confidence intervals do not overlap, suggesting that a
random-effects model is appropriate.
The results of the meta-analyses, shown in Figure D-2a, reflect
the three assumptions about the study of Hoffman and colleagues
laid out above and show the impact of choosing a fixed-effects or
random-effects approach. Both assumptions have an impact on the
calculations, but not enough to change the qualitative results. The
decision to include or exclude the three less well controlled
studies has somewhat more of an impact; if only the well-controlled
studies are included in the meta-analysis, there is an almost
significant inverse association between the vaccine and SIDS in the
early period.
Figure D-2b shows the results of altering the categorization of
deaths by time period. In the three studies that report on deaths
after the first month, these are aggregated into the late period.
The resulting meta-analyses differ little from those represented in
Figure D-2a.
The committee also experimented with different adjustments for
the age pattern of SIDS by varying the E/U ratio for the
early period from 1.0 to 1.2 for the three studies with no internal
controls. This change made very little difference in the
results.
Thus, although the results depend somewhat on the statistical
assumptions, in no case is there a significantly elevated average
odds ratio for the

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FIGURE D-2 a. Meta-analysis results comparing
the estimated
risk of SIDS in the early period postvaccination with that
in
the late part of the first month, under various assumptions:
(1) whether all studies or only well-controlled studies are
included in the meta-analysis, (2) whether a fixed- or a
random-effects model is assumed, and (3) whether the
meta-analysis includes results from the study of Hoffman et
al. (1987) based on age-matched controls (A) and age-,
race-,
and birth-weight-matched controls (B), on B alone, or on
neither (0). For each set of assumptions, the mean and 95
percent confidence interval from the meta-analysis are shown
on a logarithmic scale. b. Similar results comparing SIDS
deaths in the early period with deaths in the late period
plus
deaths after the end of the first month postvaccination.

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early period. Including the less well controlled studies, the
average odds ratio is close to 1.0. Inclusion of only the
well-controlled studies leads to a lower average odds ratio, under
0.6.
Informal Analysis
The committee felt that a formal meta-analysis as described
above was not appropriate for the data on encephalopathy because of
the relatively few cases recorded in studies other than the NCES.
The committee did, however, make the following calculations to
assess the consistency of data on encephalopathy from the other
studies with those from the NCES.
Table D-2 lists the eight studies in which a number of
vaccinated children in a defined population were monitored
subsequent to vaccination. The number of children monitored and the
number of encephalopathies recorded within 2 days (or 1 week, as
explained in footnote a) are shown. The estimate of the total
incidence in the 2 days postvaccination based on the data in all
eight studies is 6.57 cases/864,041 children = 7.6 cases per
million vaccinated children.
To determine the relative and attributable risks of
encephalopathy following DPT immunization, the background incidence
rate was estimated as follows. The four studies listed in Table 4-4
provide information on the total number of encephalopathy cases
occurring in children of various ages.
TABLE D-2 Pooled Data for Encephalopathy
Calculation
Reference
No. Children
No. Cases
Studies in defined populations
Cody et al. (1981)
15,752
0
Pollock and Morris (1983)
Self-reports
134,700
4
Hospital reports
17,000
0
Pollock et al. (1984)
6,004
1
Strom (1967)
516,276
1
Long et al. (1990
538
0
Controlled studies
Walker et al. (1988)
26,600
0
Griffin et al. (1990)
38,171
0
Gale et al. (1990)
109,000
0.57a
Total
864,041
6.57
a There were two
cases reported within I week of vaccination. Assuming a uniform
distribution over the week, 2 x 2/7 = 0.57 cases were estimated to
have occurred in the first 2 days.

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Because Beghi and colleagues (1984) found that the incidence of
encephalopathy was higher in the first year of life than in the
second or third years, these data could not be combined without
taking into account the differences in the ages of the children.
Assuming that the ratio of incidence in the first year life to the
incidence in the second and third years as estimated in the study
by Beghi and colleagues (22/15.8, or 1.4) is correct, each year of
observation beyond the first birthday is equivalent to 1/1.4, or
approximately 0.7 years of experience before the first birthday. By
using this figure, adjusted background incidence rates were
calculated for each study in Table 4-4 and for the four studies
together by dividing the number of cases recorded outside of the
immediate postvaccination interval by the number of
first-year-equivalent years of observation. By pooling the results
of the four studies in Table 4-4, the estimated background
incidence rate for encephalopathy is estimated to be 78 per million
children per year, or 0.43 per million children per 2-day
period.
By comparing the estimated total incidence in the 2 days
postvaccination derived from all eight studies listed in Table D-2
with the estimated background incidence rate during this same
period, the RR in the 2 days postvaccination can be estimated at
7.6 per million divided by 0.43 per million = 17.7. The
attributable risk for encephalopathy is the difference between the
total incidence and the background incidence: 7.6 per million -
0.43 per million = 7.2 per million. Assuming that children, on
average, receive three immunizations, the estimated attributable
risk of encephalopathy is 2.4 per million immunizations.
If the studies of Pollock and Morris (1983) and Strom (1967),
which relied on spontaneous reports for ascertainment, are
excluded, the RR estimate is 17.1 and the attributable risk
estimate is 2.3 per million immunizations. Relying only on the data
in controlled studies of well-defined populations (Gale et al.,
1990; Griffin et al. 1990; Walker et al., 1988), the total
incidence in the week following vaccination is 2 cases per 173,771
children = 11.5 cases per million vaccinated children. Using a
background rate of 0.43 x 7/2 per million = 1.5 per million, the RR
estimate is 7.6 and the attributable risk estimate is 3.3 per
million immunizations.
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