Download Use of Imputed Population-based Cancer Registry Data as a

American Journal of Epidemiology
Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2012.
Vol. 176, No. 4
DOI: 10.1093/aje/kwr512
Advance Access publication:
July 25, 2012
Practice of Epidemiology
Use of Imputed Population-based Cancer Registry Data as a Method of
Accounting for Missing Information: Application to Estrogen Receptor
Status for Breast Cancer
Nadia Howlader*, Anne-Michelle Noone, Mandi Yu, and Kathleen A. Cronin
* Correspondence to Nadia Howlader, Data Analysis and Interpretation Branch, Surveillance Research Program, Division of Cancer
Control and Population Sciences, National Cancer Institute, 6116 Executive Boulevard, Suite 504, Bethesda, MD 20892-8315 (e-mail:
[email protected]).
Initially submitted August 1, 2011; accepted for publication December 20, 2011.
The National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program provides a rich
source of data stratified according to tumor biomarkers that play an important role in cancer surveillance research. These data are useful for analyzing trends in cancer incidence and survival. These tumor markers,
however, are often prone to missing observations. To address the problem of missing data, the authors employed sequential regression multivariate imputation for breast cancer variables, with a particular focus on estrogen receptor status, using data from 13 SEER registries covering the period 1992–2007. In this paper, they
present an approach to accounting for missing information through the creation of imputed data sets that can be
analyzed using existing software (e.g., SEER*Stat) developed for analyzing cancer registry data. Bias in ageadjusted trends in female breast cancer incidence is shown graphically before and after imputation of estrogen
receptor status, stratified by age and race. The imputed data set will be made available in SEER*Stat (http://
seer.cancer.gov/analysis/index.html) to facilitate accurate estimation of breast cancer incidence trends. To
ensure that the imputed data set is used correctly, the authors provide detailed, step-by-step instructions for
conducting analyses. This is the first time that a nationally representative, population-based cancer registry data
set has been imputed and made available to researchers for conducting a variety of analyses of breast cancer
incidence trends.
breast neoplasms; imputation; incidence; missing data; receptors, estrogen
Abbreviations: ER, estrogen receptor; FMI, fraction of missing information; PR, progesterone receptor; SE, standard error;
SEER, Surveillance, Epidemiology, and End Results.
Cancer surveillance data provide a window into cancer
incidence and survival trends at the population level. As
our knowledge of cancer increases, it is clear that, in addition to primary tumor site, factors such as stage at diagnosis, histologic type, and molecular subtype play an
important role in understanding cancer risk, prognosis, and
disparities within the population. Therefore, analyzing
cancer trends according to these characteristics plays an important role in cancer surveillance. Often, individual cases
captured in registry data are missing information on these
important variables. The amount of missing information
may vary between subgroups and can change over time. In
recent work, Anderson et al. (1) highlighted the importance
of accounting for missing data when assessing trends and
showed that ignoring missing information can lead to
biased results. In this paper, we describe an approach to
accounting for missing information through creation of an
imputed data set that can be analyzed using existing software (e.g., SEER*Stat) developed for analyzing cancer registry data in conjunction with standard statistical packages
such as SAS (SAS Institute Inc., Cary, North Carolina).
To show how to create imputed data sets and make them
available to researchers for a variety of analyses, we focus
here on breast cancer and examine trends by estrogen
347
Am J Epidemiol. 2012;176(4):347–356
348 Howlader et al.
receptor (ER) status using data from the National Cancer
Institute’s Surveillance, Epidemiology, and End Results
(SEER) Program. Recent trends for ER-positive (ER+) and
ER-negative (ER−) cancers have differed, partially because
of the relation between ER+ breast cancer and the use of
hormone replacement therapy. As use of hormone replacement therapy has declined in the population, so have the
rates of ER+ breast cancer (2). Treatment and prognosis
also differ by ER status, and rates of ER+ disease vary by
race. As a result, cancer rates and trends estimated on the
basis of ER status provide a more complete picture of
breast cancer in the United States. However, ER status is
prone to missing observations, given the nature of the data.
Tissue samples for ER testing are sent to an accredited laboratory that follows specific testing guidelines. This creates
a time lag in obtaining complete information. Therefore,
ER data can easily be missed by tumor registrars who
review medical records during the period when laboratory
test results are not yet available (3).
With registry data, we are unable to assume that information is missing completely at random. When we say that
data are missing completely at random, we mean that the
probability that an observation is missing is unrelated to the
value of the observation or to the value of any other variables (4, 5). Although some investigators have imputed
ER status for specific analyses (1, 2, 6, 7), few studies to
date have carefully examined the reasons why biomarker
data are missing from the population-based cancer registries. In a few studies, however, researchers have reported
that ER data could be missing disproportionately among
certain population subgroups (e.g., black women, persons
of lower socioeconomic status) (6). Under these circumstances, examination of temporal trends could be severely
biased. Because ER is an important tumor biomarker
for breast cancer incidence and survival, it is important to
understand the extent of the missing data problem and
account properly for the missing information to present
the most accurate estimate of rates and trends for the
tumor biomarker.
Our objectives in this paper are to 1) describe a process
for developing and distributing imputed data sets and
2) apply this process to breast cancer incidence data by describing the missing ER status data patterns and imputing
missing data on ER status, along with a suite of key clinical
and demographic variables deemed important for analyzing
breast cancer trends (e.g., tumor size, race, ethnicity). Prior
to our work, a few investigators had imputed missing information on ER status; however, most of these studies
employed imputation techniques to address a particular
analysis. This paper focuses on a unique situation where
missing data in a nationally representative, publicly available data set are imputed and can be used by researchers
for a variety of analyses (e.g., to describe breast cancer incidence trends by molecular subtype, such as combinations
of ER-plus-progesterone receptor (PR) status stratified by
tumor size, and to assess ER− (more aggressive) breast
cancer incidence trends across racial groups stratified by
ecologic measures of county-level poverty and other variables). The utility of the imputed data set is that it allows
investigators to analyze trends by combining variables in
any preferred way (e.g., over different time periods, by any
age group or race/ethnicity, or by tumor attributes). The
imputed data set will be made available in SEER*Stat, software that is used to analyze SEER data (http://seer.cancer.
gov/seerstat/). To ensure that the imputed data set is used
correctly by analysts/researchers, this paper provides detailed, step-by-step instructions for conducting analyses
(see Web Appendix 1, which appears on the Journal’s
website (http://aje.oxfordjournals.org/)).
MATERIALS AND METHODS
Study population
We used population-based data from 13 SEER registries,
which represent approximately 14% of the total US population (8). Females with malignant breast cancer diagnosed
from 1992 to 2007 were included in the analysis. This
yielded a total of 401,741 female patients with malignant
breast cancer. SEER collects information on ER status in 6
categories: 1) test not done, 2) positive (+), 3) negative (−),
4) borderline, 5) test done but results missing, and 6) unknown. Figure 1 shows the distribution of ER status over
time from 1992 to 2007. The majority of the patients were
diagnosed with ER+ tumors, and the distribution of these
tumors seemed to be increasing (from 55% in 1992 to 74%
in 2007). The incidence of ER− tumors remained fairly flat
during this time period, at less than 20% of the overall distribution. Few patients were diagnosed as having an ER
status in other categories such as category 1, 4, or 5, and
the distributions of these tumor categories were fairly
small, at 4%, 2%, and <1%, respectively, with little change
over time.
However, we noticed that the distribution of unknown
ER status (the orange dots in Figure 1) varied drastically
over time. For example, unknown ER status constituted
25% of overall ER status data in 1992; by 2004, unknown
ER status represented only 10% of the distribution. This
gradual decrease in the amount of ER status data reported
Figure 1. Distribution of estrogen receptor status over time for
female patients with malignant breast cancer in 13 Surveillance,
Epidemiology, and End Results registries, 1992–2007.
Am J Epidemiol. 2012;176(4):347–356
Imputed ER Status for Breast Cancer in Registry Data 349
missing by the registries did not occur by coincidence.
During this time period, the staging classification system
used by the cancer registry community changed significantly, and a new staging classification, known as the Collaborative Staging System (9), was proposed in 2004. The
new staging system was designed to include more biologic
and clinical information regarding the extent of disease. In
response to the new system, ER status and many other
breast cancer variables became required data items, to be
collected by all SEER registries. For simplicity of analysis,
we combined the original 6 categories into a 3-level ER
status variable for further analysis: 1) ER+ (categories 2
and 4 above), 2) ER− (category 3 above), and 3) missing
ER status (categories 1, 5, and 6 above). Distributions of
these new ER status categories during 1992–2007 were:
ER + , 56%–74%; ER − , 18%–19%; and missing ER
status, 7%–25%.
Demographic variables considered important for assessing their relation with ER status included age at diagnosis (in 5-year age groups), SEER registry (San Francisco,
California; Connecticut; Detroit, Michigan; Hawaii; Iowa;
New Mexico; Seattle, Washington; Utah; Atlanta, Georgia;
San Jose-Monterey, California; Los Angeles, California;
Alaska; or rural Georgia), year of diagnosis (one of the 16
years 1992–2007), race (white, black, American Indian/
Alaska Native, Asian/Pacific Islander, or other), and ethnicity (Hispanic vs. non-Hispanic). Important clinical variables included PR status ( positive, negative, or unknown),
tumor size (≤1, 1.1–2.0, 2.1–3.0, 3.1–4.0, 4.1–5.0, or
>5.0 cm), tumor histologic type (ductal, lobular, mixed, or
other), lymph node status ( positive vs. negative), tumor
grade (I, II, III, or IV), and metastasis at diagnosis (yes vs.
no). Poverty data (obtained from 2000 US Census data)
collected at the county level were used as a surrogate for
socioeconomic status. Cutpoints based on empirical research and policy relevance (10, 11) were used to create a
2-level poverty variable (i.e., <10.0% for high socioeconomic status, 10%–100.0% for low socioeconomic status).
Web Table 1, which appears on the Journal’s website (http://
aje.oxfordjournals.org/), presents descriptive statistics for the
study population by ER status.
Multiple imputation
Multiple imputation has emerged as an appropriate and
flexible way to address the issue of missing data (12). We
have employed a method known as sequential regression
multivariate imputation (13), which includes a module for
imputing categorical variables (5, 13) such as ER status
and other breast cancer variables in the data set. A key assumption when using this imputation method is the
missing-at-random assumption, which states that the probability of missingness depends only on the associated observed variables (4). Inspection of the missing data patterns
(Web Figure 1) suggests that the missing-at-random assumption has been met because the varying degrees of
missingness seem to be explained by the different covariates. (Detailed descriptions of the missing data patterns are
provided in the Results section.) However, these are theoretical concepts that cannot be tested empirically (5).
Am J Epidemiol. 2012;176(4):347–356
The sequential regression multivariate imputation
method uses all available observations and variables specified a priori to perform imputation. The idea behind this
method is fairly simple: Model each variable with missing
observations conditional on the remaining variables in the
data set until no variable remains with missing observations. Thus, the final imputed data set would contain not
only imputed ER status but also other imputed breast
cancer variables with missing observations (e.g., tumor
size, node). The imputations themselves are values predicted from regression models, with the appropriate random
error included (13).
The procedure we followed to impute values for SEER
breast cancer data is as follows: The variable with the least
missingness (variable 1) was imputed conditional on all
variables with no missingness. We first imputed age at diagnosis (0.01% missing), conditional on SEER registry,
year of diagnosis, and ethnicity (variables with no missing
values). The variable with the second-least missingness
(county poverty, 0.02% missing) was then imputed conditional on the variables with no missing values and variable
1, and so on (i.e., until all of the variables with missing
information had been cycled through in this way and there
were no longer any missing values in the data set). Each
variable was imputed by using a model tailored to its distribution (14). For example, logistic regression was used to
impute binary variables, and polynomial regression was
used for variables with more than 2 categories (e.g., tumor
size). Studies have shown this imputation procedure to be
fairly efficient when more than 5–10 imputations yield little
added benefit (12). The data set we produced contains 5
possible values for the missing data, based on our having
run the imputation 5 times using the IVEware macro,
version 0.2 (15), in SAS, version 9.1.
To provide evidence of the reliability of the imputation
model, we conducted a simulation study that compared the
predicted ER status with the true ER status. Details of this
simulation study are presented in the Results section.
Statistical analysis of breast cancer incidence trends
Each imputed data set was used to obtain age-adjusted
rates calculated per 100,000 persons, based on the 2000 US
standard population, using SEER*Stat software (16). A
final age-adjusted rate and standard error were obtained by
combining the age-adjusted rate and standard error obtained
from each multiply imputed data set using Rubin’s rule (4).
Trends in observed and imputed age-adjusted cancer incidence rates were analyzed using the Joinpoint Regression
Program (version 3.5) (17), which involves fitting a series
of joined straight lines on a logarithmic scale to the trends
in the annual age-adjusted rates. We allowed a maximum
of 2 joinpoints in models for the period 1992–2007. We
present trends in incidence using annual percent changes,
that is, the slope of the line segment based on observed
data. Kim et al. (18) provide more details about the joinpoint model used. The imputed data sets were implemented in SEER*Stat software and can be made available
to interested researchers. The data sets are available via
different SEER*Stat software sessions, including the
350 Howlader et al.
Table 1. Breast Cancer Incidence Trends for US White Women, by Age and Estrogen Receptor Status, 1992–2007
Joinpoint Trend 1
Age Group (Years) and ER Status
Year Range
Joinpoint Trend 2
APC
Joinpoint Trend 3
Year Range
APC
Year Range
APC
3.3*
1999–2007
−0.3
2001–2004
−5.3
2004–2007
1.3
2000–2003
−2.0
2003–2007
2.3*
All ages
ER+ a, observed
1992–1999
ER+ , imputed
1992–2001
1.6*
ER− , observed
1992–2007
−0.6*
ER− , imputed
1992–2007
−2.1*
ER+ , observed
1992–2007
2.1*
ER+ , imputed
1992–2000
1.6*
ER− , observed
1992–2007
−2.0*
ER− , imputed
1992–2007
−3.1*
40–49
50–59
ER+ , observed
1992–1999
4.8*
1999–2007
−1.8*
ER+ , imputed
1992–2000
3.1*
2000–2004
−5.5*
2004–2007
−0.04
ER− , observed
1992–2007
−1.0*
ER− , imputed
1992–2007
−2.4*
ER+ , observed
1992–2001
3.4*
2001–2004
−4.9
2004–2007
3.6
ER+ , imputed
1992–2001
2.3*
2001–2004
−6.7
2004–2007
2.3
ER− , observed
1992–2007
0.3
ER− , imputed
1992–2007
−1.3*
ER+ , observed
1992–1998
3.1*
1998–2007
−0.4
ER+ , imputed
1992–1999
1.4*
1999–2007
−2.4*
ER− , observed
1992–2007
0.4
ER− , imputed
1992–2007
−1.7*
60–69
≥70
Abbreviations: APC, annual percent change; ER, estrogen receptor.
* P < 0.05.
a
ER + , estrogen receptor-positive; ER − , estrogen receptor-negative.
frequency and rate sessions. This will enable users to
conduct different types of analyses, depending on the research question.
RESULTS
Assessing missing data patterns
Overall, 17% of patients had missing data on ER status
(Web Table 1). However, the distribution of missing ER
status varied over time (Figure 1). We explored the relation
between ER status and important covariates to better understand the extent of the missing data problem. Web Figure 1
shows that the percentage of missing data on ER status was
not constant over time for these variables, and the amount
of missingness depended on the covariates and levels
within each covariate. For example, missing ER status increased with increasing age at diagnosis (e.g., from 24% in
1992 to 6% in 2007 for the age group 50–59 years; from
27% in 1992 to 7% in 2007 for the age group 70–79
years). The distribution of missing ER status data decreased
over time for the majority of the registries, with the exception of the New Mexico registry. Blacks had a higher percentage of missing ER status data than whites and Asians/
Pacific Islanders (21% for blacks vs. 17% for whites and
15% for Asians/Pacific Islanders). Similarly, Hispanics had
a higher percentage of missing ER data than non-Hispanics
(33% in 1992 to 10% in 2007 for Hispanics vs. 25% in
1992 to 7% in 2007 for non-Hispanics).
Our data showed a strong correlation between ER status
and related tumor characteristics with respect to missingness. For example, if a patient was missing data on ER
status, information on other related tumor attributes (PR
status, tumor size, node, grade, etc.) for that patient was
also likely to be missing. This pattern is evident in Web
Figure 1. In addition, patients with larger tumors or highergrade tumors were slightly more likely to have missing ER
status data than patients with smaller tumors or lower-grade
tumors (29% in 1992 to 7% in 2007 for ≥5-cm tumors vs.
18% in 1992 to 4% in 2007 for 1- to 2-cm tumors). Similarly, patients diagnosed with more advanced disease were
likely to have missing ER status (the distribution of
Am J Epidemiol. 2012;176(4):347–356
Imputed ER Status for Breast Cancer in Registry Data 351
unknown ER status was 30% for patients whose disease
was found to have metastasized at diagnosis compared with
15% for those whose disease did not metastasize). The
final variable we explored to better understand missing ER
status patterns was county-level poverty data. We found a
positive association between missing ER status and poorer
counties (missing ER status varied from 28% in 1992 to
9% in 2007 for poorer counties vs. 22% in 1992 to 5% in
2007 for less poor counties). The observed missing data
patterns revealed that ER status was not missing completely
at random. Therefore, analysis of ER data trends according
to any selected variables would be biased if the missing
observations were simply omitted.
Breast cancer incidence trends before and after
imputation
Trend analyses before and after imputation of ER status for
white females and black females are presented in Figure 2
and Table 1 (for white females) and Figure 3 and Table 2
(for black females), respectively, for all ages combined
and according to 10-year age group for patients over age
40 years. As would be expected, age-adjusted rates based
on imputed ER status were higher than those based on
observed (i.e., unimputed) ER status because cases with
missing ER status were allocated to an ER+ or ER−
category after imputation. For example, age-adjusted rates
Figure 2. Breast cancer incidence trends for US white women according to estrogen receptor (ER) status (observed vs. imputed), 1992–2007.
A) All age groups; B) ages 40–49 years; C) ages 50–59 years; D) ages 60–69 years; E) ages ≥70 years. Blue dot, ER-positive observed rate;
red dot, ER-positive imputed rate; blue triangle, ER-negative observed rate; red triangle, ER-negative imputed rate. The blue line denotes the
rate modeled by the Joinpoint Regression Program for the ER observed trend; the red line denotes the Joinpoint-modeled rate for the ER
imputed trend.
Am J Epidemiol. 2012;176(4):347–356
352 Howlader et al.
Figure 3. Breast cancer incidence trends for US black women according to estrogen receptor (ER) status (observed vs. imputed), 1992–2007.
A) All age groups; B) ages 40–49 years; C) ages 50–59 years; D) ages 60–69 years; E) ages ≥70 years. Blue dot, ER-positive observed rate;
red dot, ER-positive imputed rate; blue triangle, ER-negative observed rate; red triangle, ER-negative imputed rate. The blue line denotes the
rate modeled by the Joinpoint Regression Program for the ER observed trend; the red line denotes the Joinpoint-modeled rate for the ER
imputed trend.
in 1992 for black females with ER+ tumors were 87.5 per
100,000 (standard error (SE), 3.1) for observed ER status
versus 124.0 per 100,000 (SE, 4.0) for imputed ER status.
Similarly, ER− tumor rates were 58.3 per 100,000 (SE,
2.5) for observed ER status versus 83.3 per 100,000 (SE,
3.4) for imputed ER status. In addition, there were smaller
differences between the observed and imputed incidence
rates for the younger females as compared with older
females (because of fewer missing data in younger age
groups). For example, for white females diagnosed in 1992
with ER+ tumors, the relative difference between the
observed and imputed rates was 29.6% for the age group
40–49 years as compared with 38.4% for the age group
≥70 years. The relative difference in rates between the
younger and older groups persisted in varying magnitudes
across race, ER tumor status, and time.
Figure 2 shows results from trend analysis using observed and imputed ER status for white female breast
cancer patients, stratified by age at diagnosis. The annual
percent change estimate for each trend segment is shown in
Am J Epidemiol. 2012;176(4):347–356
Imputed ER Status for Breast Cancer in Registry Data 353
Table 2. Breast Cancer Incidence Trends for US Black Women, by Age and Estrogen Receptor Status, 1992–2007
Joinpoint Trend 1
Age Group (Years) and ER Status
Year Range
Joinpoint Trend 2
APC
Joinpoint Trend 3
Year Range
APC
Year Range
APC
1999–2005
−1.2
2005–2007
4.8
8.9*
All ages
ER+ a, observed
1992–2007
2.7*
ER+ , imputed
1992–1999
1.6*
ER− , observed
1992–2007
1.4*
ER− , imputed
1992–2007
−1.0*
ER+ , observed
1992–2002
1.1
2002–2007
7.8*
ER+ , imputed
1992–2002
−0.6
2002–2007
4.4*
ER− , observed
1992–2007
−0.2
ER− , imputed
1992–2007
−2.2*
2001–2005
−3.7*
2005–2007
2001–2004
11.7
2004–2007
40–49
50–59
ER+ , observed
1992–2007
ER+ , imputed
1992–2007
ER− , observed
1992–2007
ER− , imputed
1992–2007
1.9*
−0.2
1.3*
−0.8
60–69
ER+ , observed
1992–2007
2.9*
ER+ , imputed
1992–2001
1.9*
ER− , observed
1992–2007
2.7*
ER− , imputed
1992–2007
−0.04
ER+ , observed
1992–2007
2.8*
ER+ , imputed
1992–2007
0.2
ER− , observed
1992–2001
0.7
ER− , imputed
1992–2007
−0.5
≥70
−2.4
Abbreviations: APC, annual percent change; ER, estrogen receptor.
* P < 0.05.
a
ER+ , estrogen receptor-positive; ER− , estrogen receptor-negative.
Table 1. A summary of the results for ER+ trends follows.
Observed ER+ tumor incidence rates for all ages combined
increased by 3.3% ( per year) from 1992 to 1999, followed
by a non-statistically significant decrease of 0.3% ( per
year) from 1999 to 2007. In contrast, imputed ER+ tumor
incidence rates showed a different trend during this time
period: a 1.6% ( per year) increase from 1992 to 2001, followed by a nonsignificant decrease of 5.3% ( per year) from
2001 to 2004 and a non-statistically significant increase of
1.3% ( per year) during the most recent time period (2005–
2007). For the age group 50–59 years, we noticed an increasing observed ER+ trend (4.8% per year) up to 1999,
followed by a decreasing trend (−1.8% per year) from 1999
to 2007. The imputed ER+ trend is similar to the observed
trend in the first segment (3.1% from 1992 to 2000).
However, with imputed ER status, a new joinpoint was detected in 2000. This new joinpoint splits the last segment
of 1999–2007 into 2 segments, showing a statistically significant decrease of 5.5% ( per year) from 2000 to 2004,
followed by a non-statistically significant decrease of
0.04% ( per year) from 2004 to 2007. The detection of a
Am J Epidemiol. 2012;176(4):347–356
new joinpoint in this age group is important because it captures a well documented (2, 19–22) change in breast cancer
incidence around 2002, after results from the Women’s
Health Initiative (23) were published. Without correcting
for the changing distribution of missing ER status over
time, we would not have been able to detect this important
phenomenon for this group of women between the ages of
50 and 69 years. For women aged ≥70 years, the observed
ER+ trend for 1998–2007 went from a non-statistically significant decrease of 0.4% ( per year) to a rapid statistically
significant decrease of 2.4% ( per year) with the imputed
ER+ trend in the most recent period. We also found modest
differences between the imputed ER+ trend and the observed ER+ trend for the age groups 40–49 and 60–69
years.
Comparing the ER− trends in the observed and imputed
data for all ages also showed some modest differences
(Figure 2). The ER− observed incidence trend decreased
slowly, by 0.6% ( per year), whereas the imputed trend decreased much faster, by 2.1% ( per year) over the entire
period (1992–2007; Table 1). A similar pattern between the
354 Howlader et al.
observed and imputed trends was observed for the age
groups 40–49 and 50–59 years. Interesting differences
between the observed and imputed ER− trends were noted
for the older ages. For example, for women aged ≥70
years, the observed ER− trend increased nonsignificantly
by 0.4% ( per year), whereas the ER− negative imputed
trend decreased rapidly over this period by 1.7% ( per year).
The decreasing ER− trend for older women would not
have been detected without accounting for missing ER
status. A similar trend was observed for the ER− trend in
the age group 60–69 years. A similar decreasing trend with
the redistributed ER− tumors was reported in a recent
study (1).
Figure 3 and Table 2 show the trend analysis for black
female breast cancer patients. The ER+ observed trend increased during the entire study period, whereas the imputed
ER+ trend showed more variability (see Table 2). The observed ER− trend increased by 1.4% ( per year), whereas
the imputed ER− trend decreased by 1.0% ( per year). The
decreasing trend for imputed ER− data can partially be explained by the reallocation of the missing ER values.
Because there was a much higher percentage of missing
ER status data in the earlier period, the reallocation of a
certain proportion of missing ER data to ER− inflates the
earlier part of the trend, whereas the later part of the trend
is little affected, as the quantity of missing data in recent
years was smaller.
Simulation study
To provide evidence of reliability for our imputation
model, we performed a simulation study using a subset of
data for which the ER status was known. We then generated
missing-at-random data with the same pattern of missingness as the original data set to preserve the relations
between other variables and ER status. To reduce computation time, we randomly sampled 1% of the missing-atrandom data set and produced 100 training data sets on
which we conducted sequential regression multivariate imputations separately. We then compared the concordance
between imputed and true ER status after performing the
imputation for each data set using the area under the receiver operating characteristic curve. The average area under
the curve across these 100 data sets was estimated to be
0.92. Because there was good agreement between imputed
and true ER status, we feel confident in our model prediction. In addition, our model fit was good ( pseudo-R 2 =
0.69). We also computed the fraction of missing information (FMI), a positive number between 0 and 1 that is calculated as a ratio of between-imputation variance and total
variance (4). The FMI is used to reflect statistical uncertainty due to missing data in results across the imputed data
sets. The average FMI based on the imputation model for
ER+ tumors was 6%; for ER− tumors, it was 12%. Here,
the FMI is specific to the ER status category being estimated. The differing FMI for ER+ and ER− tumors reflects the
differing amounts of imputation in these groups. The relatively small value of the average FMI implies that the prediction was stable across the different imputations.
DISCUSSION
Missing values can present a serious problem in the analysis of cancer registry data. Several techniques can be used to
address the problem. The appropriateness of the chosen
method depends on the nature of the missing data and how
well the assumptions can be justified. Modeling missing data
with imputation requires making certain assumptions about
the missing data mechanism. A second important concern
with regard to modeling missing data relates to how well the
model is able to predict missing observations. In our analysis,
we observed that missing ER status on average was being allocated approximately 75% to ER+ status and approximately
25% to ER− status (data not shown). Age at diagnosis is one
of the most important predictors of ER status; increasing age
is associated with ER+ tumors, and decreasing age is associated with ER− tumors. In Web Table 1, we show that as age
increased, missing data on ER status increased as well. For
example, overall missing ER status varied from 15.3% for
women aged <50 years to 16.9% for those aged 65–74 years
and 22.4% for those aged ≥75 years. We examined groups
stratified by age, race, and year of diagnosis and found that
expected distributions to ER tumors for the subgroups were
similar to the overall model.
Another consideration when developing an imputation
model is to ensure the inclusion of all of the important
predictors of the outcome (5). Because we were using population-based cancer registry data, we did not have information
on several important risk factors for ER status (e.g., duration
of hormone therapy, nulliparity, late age at first pregnancy,
postmenopausal obesity) (6). Also, as noted by many missingdata experts (12), an imputation method relies on inherently
untestable assumptions, and the accuracy of the specified conditional distributions is uncertain. Therefore, some degree of
caution should be exercised when using these imputed data
sets for making inferences with respect to trends.
No evidence-based clinical practice guidelines for the
use of tumor markers in the prevention, screening, treatment, and surveillance of breast cancer were available
before 1996 (24). Therefore, it is possible that physicians
did not ask all patients with invasive breast cancer to have
their tumors tested for estrogen and progesterone receptors
prior to that time. This could explain in part the larger
amount of missing ER/PR data in the early 1990s. As time
progressed and testing for these breast cancer tumor
markers became part of standard care, the completeness of
ER/PR data improved. Concurrently, new guidelines were
developed that established cutoff values for determining
ER/PR positivity (25). In our analysis, we were not able to
account for changes in ER/PR testing over time.
In summary, we have carefully examined and addressed
missing data for several important tumor characteristics that
often are used to describe current breast cancer trends in
the United States. In reporting cancer trends, a change of as
little as 1% per year demonstrates improvement or prompts
alert in cancer control efforts. Such changes could easily
be obscured without proper adjustment for any missing
data. More importantly, because data collection on several
clinical and molecular factors such as human epidermal
growth factor receptor 2/neu is well under way (26), it will
Am J Epidemiol. 2012;176(4):347–356
Imputed ER Status for Breast Cancer in Registry Data 355
be even more important to be able to develop and distribute
these imputed data sets, as the initial years of data collection for these variables will probably include many missing
observations. With the aid of these imputed data sets, we
can provide researchers with tools to better understand the
molecular and genetic alterations in breast cancer incidence
and report trends in the most accurate manner.
ACKNOWLEDGMENTS
Author affiliation: Data Analysis and Interpretation
Branch, Surveillance Research Program, Division of Cancer
Control and Population Sciences, National Cancer Institute,
Bethesda, Maryland (Nadia Howlader, Anne-Michelle
Noone, Mandi Yu, Kathleen A. Cronin).
This work was supported by the Surveillance Research
Program, Division of Cancer Control and Population Sciences, National Cancer Institute.
The authors thank Drs. Brenda K. Edwards, Eric J Feuer,
and Minjung Lee for their helpful comments on the
manuscript.
Conflict of interest: none declared.
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