Download A Checklist for Ascertaining Study Cohorts in Oncology

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METHODOLOGICAL ARTICLES
A Checklist for Ascertaining Study Cohorts in Oncology Health Services
Research Using Secondary Data: Report of the ISPOR Oncology Good
Outcomes Research Practices Working Group
Kathy L. Schulman, MA1,, Karina Berenson, MPH2, Ya-Chen (Tina) Shih, PhD3, Kathleen A. Foley, PhD4, Arijit Ganguli, MBA,
PhD5, Jonas de Souza, MD3, Nicholas A. Yaghmour, MPP3, Alex Shteynshlyuger, MD6
1
Outcomes Research Solutions, Inc., Bolton, MA, USA; 2Covance, Gaithersburg, MD, USA; 3University of Chicago, Chicago, IL, USA; 4Truven Health Analytics,
Philadelphia, PA, USA; 5Abbott Laboratories, Abbot Park, IL, USA; 6Phoenixville Urologic Associates, Phoenixville, PA, USA
AB ST RAC T
Objectives: The ISPOR Oncology Special Interest Group formed a
working group at the end of 2010 to develop standards for conducting
oncology health services research using secondary data. The first
mission of the group was to develop a checklist focused on issues
specific to selection of a sample of oncology patients using a
secondary data source. Methods: A systematic review of the published literature from 2006 to 2010 was conducted to characterize the
use of secondary data sources in oncology and inform the leadership
of the working group prior to the construction of the checklist. A draft
checklist was subsequently presented to the ISPOR membership in
2011 with subsequent feedback from the larger Oncology Special
Interest Group also incorporated into the final checklist. Results: The
checklist includes six elements: identification of the cancer to be
studied, selection of an appropriate data source, evaluation of the
applicability of published algorithms, development of custom
algorithms (if needed), validation of the custom algorithm, and
reporting and discussions of the ascertainment criteria. The checklist
was intended to be applicable to various types of secondary data
sources, including cancer registries, claims databases, electronic
medical records, and others. Conclusions: This checklist makes two
important contributions to oncology health services research. First, it
can assist decision makers and reviewers in evaluating the quality of
studies using secondary data. Second, it highlights methodological
issues to be considered when researchers are constructing a study
cohort from a secondary data source.
Keywords: cohort, ISPOR checklist, oncology, sample selection,
secondary data.
Purpose
common problem in observational studies, the complexity of
oncologic disease and its inexact representation in clinical data
systems may exacerbate both selection and misclassification bias.
The purpose of this article is to both provide researchers who
conduct oncology health services research using secondary data
a list of methodological issues to consider when selecting study
cohorts and assist researchers as well as journal reviewers/readers, payers, and policymakers in evaluating the quality of published studies involving secondary data analyses.
A working group to develop standards for oncology health
services research using secondary data was established through
the ISPOR Oncology Special Interest Group (SIG) to review and
address issues related to this line of research. The first task of our
working group was to develop a checklist for use in selecting
samples of patients with a specific type of cancer from secondary
data sets. We completed this task in two phases: a systematic
literature review followed by development of the draft and final
checklist. The formal review of the literature was conducted to
The use of secondary data sources is an increasingly accepted
approach in oncology health services research, as exemplified by
studies using such data to profile care patterns, measure patient
outcomes, and estimate cancer-related costs [1–6]. Ascertainment
of study cohorts from these data sources, however, can be difficult.
Selection of a sample of cancer patients from secondary data
sources is challenging when coding systems (e.g., International
Classification of Diseases, Ninth Revision, Clinical Modification) used to
identify these patients lack adequate clinical precision (e.g., cancer
stage) or when clinical interventions are masked by payment
system protocols. In these circumstances, the cohort selected
may not be reflective of the larger population with the disease
(poor sensitivity) or may contain large numbers of patients who do
not actually have the disease at all (poor specificity). In either case,
this may lead to inconsistent, spurious, or erroneous results and
compromise the clinical relevancy of study findings. While this is a
Copyright & 2013, International Society for Pharmacoeconomics and
Outcomes Research (ISPOR). Published by Elsevier Inc.
* Address correspondence to: Kathy L. Schulman, Outcomes Research Solutions, Inc., 44 Whitcomb Road Bolton, MA 01740, USA.
E-mail: [email protected].
1098-3015/$36.00 – see front matter Copyright & 2013, International Society for Pharmacoeconomics and Outcomes Research (ISPOR).
Published by Elsevier Inc.
http://dx.doi.org/10.1016/j.jval.2013.02.006
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inform SIG leadership as to the current state of science in the
literature prior to the development of the checklist. It was intended
to complement the knowledge base and experience of the larger
SIG as well as the existing validation literature. It was not conducted, as are many systematic reviews, to support meta-analysis.
SIG consensus was shaped first by discussions regarding the
extent to which the literature review should be structured and
subsequently the development of the elements of the structured
review. SIG members were involved in the construction of search
parameters, in the development of the abstraction form, in the
actual abstraction of selected articles, and of course in the summary of findings. This process, which took place over the course of
a year, included multiple meetings in which the literature search,
its findings, and the potential importance to the checklist were
discussed. It should be noted that disagreement between abstractors was resolved by third-party review. Consensus was also formed
by discussions with the larger ISPOR membership during the
presentation of these results at the annual meeting and also during
dissemination of the draft deliverable to the larger SIG membership.
The literature review resulting from the above efforts reflects
a concerted decision on the part of the SIG leadership to either
confirm or refute a priori assumptions about the current state of
oncology research using secondary data prior to drafting a
checklist. For example, SIG members underestimated the frequency with which claims data were being used in isolation from
other data sources and overestimated researcher reliance on
previously published algorithms. Moreover, the checklist itself
was highly influenced by the degree of difficulty in abstracting
the necessary detail about sample selection in general because
key elements either were missing altogether or were vague or
nonspecific in their presentation. Finally, consensus regarding
key elements of the checklist was inferred on the basis of results
of the review in conjunction with the experience of SIG members,
most notably the importance of identifying the key clinical
elements of the cancer of interest.
The literature search was conducted in PubMed on English
language articles published between January 1, 2006, and December
31, 2010. We chose the most recent 5-year time period at the time
the literature review was performed because it captures the majority of work done with secondary data sources while also reflecting
the most recent methodological thinking in this topic area. Search
terms included ‘‘claims,’’ ‘‘hospital discharge,’’ ‘‘electronic medical
records (EMR),’’ ‘‘registry,’’ and ‘‘administrative’’ in conjunction with
the Mesh term ‘‘Neoplasms.’’ No other restrictions were placed on
the query. Two reviewers (K.S. and K.B.) assessed abstracts for
potential eligibility with subsequent full-text article review by the
larger working group leadership team to determine appropriateness
of inclusion. Findings from the literature review were used to
characterize the use of secondary data sources in oncology, to
identify potential methodological and reporting issues for oncology
health services researchers, and to inform the second phase, in
which a draft checklist was developed by consensus among the
leadership working group. The draft checklist, presented at the
ISPOR international meeting in 2011, and subsequently reviewed by
the larger Oncology SIG, was then modified by incorporating comments and feedback to produce the final checklist.
Review of Current Literature
The literature search yielded 863 abstracts, of which 321 were
eligible for full article review and 294 [6–298] were eligible for
abstraction (Fig. 1).
The literature search demonstrated that secondary data
sources are widely used for oncology research and that the
ascertainment of study cohorts largely relies on registry or
claims-based data. As evidenced in Table 1, 72.8% of the studies
identified in our review relied to some degree on claims data. Of
the 294 articles selected for review, 21.8% (n ¼ 64) were claimsonly studies [7–70], 5.1% (n ¼ 15) were registry-only [71–85], 51%
(n ¼ 150) used claims and registry data together without any
other supplemental data set [6,24,151–298], and the remaining
22.1% (n ¼ 65) used other data sources, primarily chart-based
systems or hospital discharge data sets, either alone or in
combination with claims and/or registry data [86–150]. Chartbased systems included paper or electronic health records (EHRs)
as well as electronic medical record (EMR) databases, programmatically generated subsets of EHRs. The Surveillance Epidemiology and End Results (SEER)/Medicare database was the single
most commonly used (N ¼ 93) data set, representing 62% of
combination claims and registry studies and 31.6% of all eligible
studies.
Abstracts Reviewed
N=863
Potentially Eligible, Full
Article Review
N=320, 37.1%
Eligible
N=294, 91.9%
Ineligible
N=26, 8.1%
Not secondary data source
N=15, 57.7%
Not sample of pts with cancer
N=11, 42.3%
Ineligible
N=543, 62.9%
Not results from a study
N=198, 36.5%
Not sample of pts with cancer
N=168, 30.9%
Not secondary data source
N=153, 28.2%
Other
N=24, 4.4%
Fig. 1 – Abstract review and article selection.
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Table 1 – Secondary data sources used in oncology outcomes research. Registry only
(N ¼ 15)
Cancer, unspecified, n (%)
Breast, n (%)
Colorectal, n (%)
Leukemia, n (%)
Lymphoma, n (%)
Lung, n (%)
Multiple myeloma, n (%)
Prostate, n (%)
Other, n (%)
1
2
7
1
1
2
1
2
7
(6.7)
(13.3)
(46.7)
(6.7)
(6.7)
(13.3)
(6.7)
(13.3)
(46.7)
Claims only
(N ¼ 64)
8
34
13
7
12
19
5
10
20
(12.5)
(53.1)
(20.3)
(10.9)
(18.8)
(29.7)
(7.8)
(15.6)
(31.3)
Registry þ
claims (N ¼ 150)
5
46
45
7
14
27
5
40
45
(3.3)
(30.7)
(30.0)
(4.7)
(9.3)
(18.0)
(3.3)
(26.7)
(30.0)
Other data sources
(N ¼ 65)
13
12
10
2
1
14
1
5
26
(20.0)
(18.5)
(15.4)
(3.1)
(1.5)
(21.5)
(1.5)
(7.7)
(40.0)
All data sources
(N ¼ 294)
27
94
75
17
28
62
12
57
98
(9.2)
(32.0)
(25.5)
(5.8)
(9.5)
(21.1)
(4.1)
(19.4)
(33.3)
Column percentages may exceed 100% because an individual study may reference multiple cancer types.
Studies included in the review were primarily conducted in the
United States or Canada but also included studies using secondary
data from Taiwan, Korea, France, Denmark, England, Italy, Australia,
Sweden, The Netherlands, Finland, Belgium, and Japan. Among the
claims-only studies, there was very little reported use of either a
previously published algorithm (n ¼ 8, 12.9%) [20,27,37,48,49,
58,60,61] or a validated algorithm (n ¼ 4, 6.5%) [48,58,60,61]. Almost
half (n ¼ 31, 48.4%) of claims-only studies relied solely on diagnosis
codes to select their study sample [7,9,12–15,19,20,25,27–29,31,32,34,
363739–4146,47,49,50,56,59,60,63,69,70], with 35.5% (n ¼ 11) of these
studies using a single claim with a cancer diagnosis as a prerequisite for study entry [15,29,32,34,36,37,46,50,63,69,70]. Only 4.8% of
the articles discussed the implications of their selection criteria on
the study findings [27,48,49].
Framework of Checklist
A six-step checklist was developed to assist reviewers and
researchers (Table 2). Elements of the checklist are presented in
the order that researchers are anticipated to consider them as
they design and implement a study. Items in the first category are
to identify cancer-specific clinical characteristics that may influence the data source selection and the inclusion/exclusion
criteria for the study sample. The second category, selection of
an appropriate data source, is then followed by a review of the
literature to identify potential selection criteria or algorithms
that may be used or adapted for the study. Items in the fourth
category summarize factors to consider in developing a custom
algorithm, while those in the fifth category detail considerations
in the validation of such algorithm. Finally, the checklist includes
recommendations for reporting the selection criteria or algorithm. The entire checklist is presented in Table 2. Additional
oncology resources can be found at http://www.ispor.org/Oncolo
gyORResources/SearchOcologyResources.aspx.
How Should the Checklist be Used?
The checklist, focused on the four general types of secondary
data identified from our literature review, is intended to assist
decision makers in evaluating the quality of published studies
and to provide researchers a stepwise list of methodological
issues for consideration when developing case ascertainment
criteria and reporting study findings in oncology research.
Because the purpose of the checklist is to guide the creation of
appropriate inclusion and exclusion criteria to properly identify
patients with specific tumor characteristics, it should be used to
complement other checklists and guidelines related to secondary
data research, such as the ISPOR checklist on retrospective
database studies [299] and the ISPOR Good Research Practices
for Comparative Effectiveness Research [300–302].
Description of Checklist Elements
Step 1: Identify Clinical Aspects of Cancer
Step 1 of the checklist emphasizes the importance of understanding the biology, natural history, and etiology of the specific
cancer of interest prior to establishing the inclusion/exclusion
criteria of the study protocol. Knowledge of the natural history of
the disease will facilitate an understanding of the patient experience, specifically key clinical characteristics and the expected
duration of the window of observation at each stage of the
disease. Knowledge of biology and/or etiology will identify clinical subtypes, in addition to cancer stage, in which patient
prognosis may differ.
Biomarkers are anatomic, physiologic, biochemical, or
molecular parameters associated with the presence and severity
of specific disease states. They may be diagnostic, perhaps
before the cancer is detectable by conventional methods, prognostic, forecasting how aggressive the disease process will be, or
predictive, identifying which patient will respond to which drug
[303–306]. Failure to differentiate these subtypes can be problematic when evaluating clinical outcomes (e.g., mortality, progression, and symptom sets) or when comparing the toxicity
profiles of therapy. For example, a retrospective study of outcomes among patients with metastatic colorectal cancer will
likely need to address the issue of how to differentiate patients
with colorectal cancer with KRAS wild-type gene (a gene that
encodes the protein GTPase KRAS) from patients with the KRAS
mutated gene, as the response to therapeutic agents may differ
between these two groups. Similarly, studies evaluating outcomes or treatment burden in women with breast cancer may
need to be able to differentiate women who are human epidermal growth factor receptor 2 positive [307] from those who
are not.
It is also recommended that treatment guidelines in use during
the study period are reviewed. The National Comprehensive Cancer
Network, for example, publishes guidelines for treatment of cancer
by site. It is also important to understand past treatment practices
to the extent they impact the study window as well as the extent to
which real-world practices deviate from guidelines.
The researcher should also have a good understanding of how
the specific cancer type is coded in oncology practice and
whether those codes are clinically specific enough to identify
the pertinent population. For example, International Classification
of Diseases, Ninth Revision, Clinical Modification diagnosis codes,
which are used in the United States, lack the specificity of
International Classification of Diseases, Tenth Revision codes. They
cannot be used alone, for example, to identify patients with
non–small cell lung cancer because the code does not
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Table 2 – Checklist for ascertaining cohorts in oncology using secondary data.
differentiate types of lung cancer. While there is far greater
specificity to body site for solid tumors in International Classification of Diseases, Tenth Revision, Clinical Modification and greater
detail as to clinical type for neoplasms of lymphatic and hematopoietic tissue, specificity issues remain in International Classification of Diseases, Tenth Revision, Clinical Modification. The
researcher should consult a nosologist familiar with both coding
practice rules and real-world conventions because a descriptive
code listing alone may not suffice. The researcher should also
determine whether in situ codes are appropriate for inclusion
because these are not clinically relevant for all cancers.
Step 1 is an iterative process. Study objectives and/or the
target population will determine the initial inclusion/exclusion
criteria, but inherent limitations of the data chosen for the study
often require revision of either study objectives and/or the target
population to ensure the validity of the study.
Table 3 – Data type strengths and limitations, by data type.
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Step 2: Select an Appropriate Data Source
The second step is to choose an appropriate data source given the
cancer of interest, the study objectives, the potential methodological constraints, and the characteristics associated with candidate data sources. Specifically, the researcher must determine
whether a given data source has enough clinical detail to
accurately identify the cancer population of interest, is sufficiently comprehensive, and has adequate duration of follow-up
to address the research question. Table 3 provides an overview of
different types of secondary data and summarizes the strengths
and limitations of each data type.
While medical records (paper, EHRs) are often considered the
gold standard for accurately identifying a cancer-specific sample,
they are used infrequently because of resource and budget
constraints as well as data accessibility. As a compromise,
registries are frequently used to establish the gold standard.
Information captured in a registry, however, is usually limited
to the time at or immediately following entry in the registry. For
example, the SEER registry currently reports only the first course
of treatment. Hence, this data characteristic limits its ability to
construct a sample of treatment-resistant patients (e.g., men
with hormone-refractory prostate cancer) or to capture all
patients with advanced disease, especially those who initially
presented with early stage disease. Unless linked to another
source, registries alone may also not capture detailed information on long-term follow-up, compromising the ability to measure longer term outcomes other than mortality. In addition, while
registries collect information on disease staging, they may be
missing the data elements needed to identify some cancer
subtypes; of special concern are subtypes determined by genetic
dispositions. Researchers should have a strong understanding of
how the registry data are captured, collected, and coded, including a history of changes in registry methodology over the time
period of the study. These should include familiarity with the
coding and staging system used in the registry (e.g., International
Classification of Diseases for Oncology, 3rd Edition), the population source and geographic coverage for the registry, and the
validity of linkage algorithms if the registry was linked to another
data source, such as the SEER-Medicare data.
Although the checklist developed by our working group is
focused on the ascertainment of study cohorts, the nature of
cancer requires that this be done in conjunction with assessing
the researcher’s ability to fully observe the patient both before
and after the pertinent index event. Cancer may be chronic,
recurrent, and/or associated with high mortality. As such, it may
be important to use a data source with both sufficient long-term
follow-up and accurate mortality information. Because cancer
patients receive care in inpatient, outpatient, and multiple office
settings, a data source that does not uniformly capture information from each of these settings may be insufficient. This is
especially problematic if the outcomes of interests are costs or
resource utilization. Cancer patients may also experience job loss
or transition to short- or long-term disability, either of which can
result in their periodic termination from select databases.
Researchers may therefore not be able to fully observe previous
episodes of care (left censoring). This can result in misassignment of the primary tumor type, failure to distinguish between
incident cases and prevalent or recurrent cases, and/or their
ability to profile care or measure study outcomes during followup (right censoring).
For researchers interested in claims data, one of the primary
concerns is whether the data capture sufficient clinical detail to
identify the cancer of interest, especially if this includes cancer
types with poor code specificity, or those strongly associated with
predictive/prognostic biomarkers. Supplemental laboratory or
histology data in claims data sets may be incomplete, especially
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if the study requires repeated measures such as prostate specific
antigen measurements in prostate cancer or complete blood cell
counts and polymerase chain reaction results in leukemia. For
claims data sources tied to employment-based insurance, data
for patients who have a change in employment status or go on
long-term disability may not be fully captured. Moreover, mortality data are usually limited in commercial claims data sets for
patients dying at home. Researchers should specifically inquire
about the availability of date of death before purchasing a claims
data set, as providers of commercial data are actively working to
improve their capture of date of death. While death proxy
algorithms exist for claims data, they have not been validated
and have only been used in studies of patients with end-stage
disease [308,309]. Information on race and ethnicity is not always
available in claims data; these should also be verified prior to
choosing a data set. Finally, reimbursement factors may impact
either data completeness or how specific data are manifest in
claims data. For example, researchers using SEER-Medicare data
are recommended to exclude patients who enrolled in a health
maintenance organization because claims data from these plans
tend to be incomplete.
EMR data offer good clinical detail, including laboratory and
histology, both at the time of diagnosis and during follow-up.
These data, however, must be programmatically parsed and
extracted from EHRs into database format. Moreover, there are
frequently multiple sources of EMR data, each unique to the site
of care (hospital, oncology clinic, primary care), even in countries
with national health insurance. Accordingly, these databases
may suffer from significant amounts of missing data or coding
inconsistencies, although it should be noted that, in our experience, having a research nurse review the original EHR is a viable
strategy to ameliorate this problem. Hospital discharge data,
especially those culled from information technology systems
designed to document clinician care orders, may also be
restricted in their ability to track patients across sites of service.
These data generally are able to track a patient’s care within the
reporting hospital and can capture outpatient clinic data only in
those financially integrated health care networks that include
data linkages across sites of service.
Step 3: Review Published Criteria or Algorithms of Case
Ascertainment
This step recommends a review of the literature to identify
previously published, cancer-specific, selection criteria. Even if
it is determined that the published algorithms are not appropriate to identify the cancer of interest, elements in these criteria
can still help highlight the full complement of pertinent diagnostic or procedure codes, the impact of varying clean periods,
and/or the necessity of considering other variables in the inclusion/exclusion logic.
Researchers considering the use of an existing algorithm
should examine the comparability of the data source. Algorithms
may not be generalizable across data sources if coding systems
differ across populations or if the sample used for validation is
not a representative cross-section. This concern is especially
pertinent for studies using algorithms published in one country
that are then adapted for use in another country. For example,
while the Current Procedural Terminology codes are the standard
source to identify specific procedures or services in the United
States, they are not a universally accepted coding system worldwide. Therefore, studies using a US-based algorithm to identify
an incident cohort of cancer patients from the National Health
Insurance claims in Taiwan will need to find the claims code in
Taiwan that correspond to the Current Procedural Terminology
codes used in the published algorithm. Researchers should also
be mindful about the differential time periods between the time
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the algorithm was developed and that of their own studies.
Changes in practice over time or newly added codes for emerging
technologies may limit the validity of an algorithm used in a
study period that differs from the period of the original
algorithm.
Step 4: Develop the Custom Criteria/Algorithm
If researchers conclude from step 3 that none of the existing
algorithms suits the purpose of their study, a decision needs to be
made whether to develop a new algorithm or to modify the study
objective and target population to better fit the currently available algorithm (i.e., return to step 1). An important consideration
in developing custom algorithms is whether the data source
contains a component that distinguishes between physicians’
definitive versus differential diagnosis. Differential diagnoses,
for example, may be recorded in claims data as ‘‘working’’ or
‘‘rule-out’’ diagnosis codes. Although the Center for Medicare &
Medicaid Services policies prohibit such coding schemes in outpatient settings [310], they remain common in practice. Hence, if
only one instance of the diagnosis code is used to determine a
patient’s cancer status, the researcher risks selecting patients
who do not have the cancer of interest either because they were
ultimately determined to not have cancer at all or were subsequently determined to have a cancer other than the one
under study.
Setoguchi et al. [311] tested four different algorithms for
identifying cancer patients in claims data linked to a state
cancer registry. When using just one diagnosis on a medical
claim to identify patients with one of six cancers, they found
that only 18.8% to 50.2% of such patients actually had cancer.
When they also required at least one cancer-related procedure
to occur with the cancer diagnosis, however, the percentage
accurately identified increased from 41.1% to 81.7%. Accuracy of
claims-based algorithms was greater for solid tumors than it
was for hematologic malignancies, such as lymphoma or
leukemia [311]. Similarly, Friedlin et al. [312] concluded that in
the case of pancreatic cancer, using a single International
Classification of Diseases, Ninth Revision diagnosis code was
associated with high sensitivity, but poor specificity. Hence,
while the criteria identified most of the patients with pancreatic
cancer in the database, 62% of the patients in the final sample
did not actually have the malignancy.
Information documented in the above studies, combined with
working experiences of the workgroup members, suggests that it
is essential to require more than one diagnosis code to select the
tumor of interest and/or exclude claims for diagnostic procedures
(e.g., laboratory, pathology, venipuncture, and noninterventional
radiology). Including codes for cancer-specific procedures or
therapies may further improve the accuracy. Study criteria that
require a breast cancer diagnosis code and a mastectomy or
lumpectomy procedure code is an example of a strategy that
pairs a specific diagnosis code with a second component, cancerspecific treatment. This approach is likely to maximize specificity
in selecting breast cancer patients with modest impact on
sensitivity. A review of the approaches used by Nattinger et al.
[313] provides further guidance.
Using a second component in addition to the diagnosis code,
however, is not appropriate for all studies. If the study objective is
to characterize treatment patterns, document disparities in treatment, or evaluate economic burden, the use of a treatment
procedure as one of the components to identify the sample thus
compromises the study objective. The researcher should also be
aware that some therapies apply to multiple cancers, all of which
may be on the physicians’ differential diagnosis list, thus impacting comparative analyses. Hence, requiring treatment of disease
as part of the selection criteria may not be sufficient. For
example, patients presenting with a symptom set suggestive of
leukemia may have multiple working diagnoses suggestive of
acute or chronic lymphocytic or myelogenous leukemia, some of
which are treated with a common agent. The researcher may
thus be faced with conflicting diagnosis codes and a nonspecific
therapy.
Given the challenges associated with adding a procedure code
as part of the ascertainment criteria in addressing certain study
objectives, our recommendation is that researchers should consider requiring, for example, two or more diagnoses, on nondiagnostic claims, and on different dates between 30 and 365
days apart. This algorithm may require modification depending
on the cancer type and study objective, especially when selecting
cohorts of patients with terminal cancer.
Requiring a period free of cancer-specific diagnoses codes
(known as the ‘‘clean’’ or ‘‘wash-out’’ period) also helps differentiate incident (new) cases from prevalent or recurrent cases.
Distinguishing between incident and recurrent cancer however
may be challenging in the presence of left censoring because
previous cancer episodes may not be observed. Left censoring
occurs if baseline windows of observation are inadequate, or if
data sets do not comprehensively capture cancer care for any of
the reasons detailed in step 2. Furthermore, it may be difficult to
verify whether the selected cancer is the primary tumor and not
a secondary metastatic tumor site. Linkage to a cancer registry
can mitigate this problem, although not completely. In claims
data not linked to registries, breast cancer that spreads to the
lung, for example, may be incorrectly coded as an incident
primary lung cancer rather than metastatic breast cancer. While
identifying the primary tumor is not always possible in some
data sets, researchers should understand the extent of misclassification in developing custom algorithms or applying published
algorithms to make informed decisions that prevent the introduction of bias and minimize measurement error.
Step 5: Validation and Sensitivity Analyses
When developing custom algorithms, if the researcher has access
to a data source that can serve as a ‘‘gold standard’’ for
determining the cancer of interest, then the algorithm should
be validated. Ideally, the new algorithm should be validated in a
representative, cancer-specific, cross-section of patients. If that is
not feasible, it is still valuable to validate the algorithm in a
subsample. If validation is attempted, researchers should subsequently report their findings, clearly stating the population
used for validation as well as the performance (sensitivity,
specificity, positive predictive value [PPV], and negative predictive
value) of the custom algorithm [314]. If validation is not feasible,
sensitivity analyses should be conducted to assess the impact of
alternate selection algorithms on study findings. For example,
when selecting an incident cohort, the researcher may wish to
test ‘‘clean’’ or ‘‘wash-out’’ periods of varying lengths or explore
the impact of requiring three diagnoses instead of two to identify
the study cohort. To benchmark the performance of custom
algorithms, researchers may wish to include, as part of the
sensitivity analysis, the published algorithm that is closest to
the custom algorithm in development.
In general, there are few published algorithms for a given type
of cancer. Breast cancer is the lone exception. Most of the
algorithms used to identify breast cancer rely on matching to
gold standard. The reported sensitivity of these algorithms varies
from 46% to 99.5%, with the accompanying PPV suggesting that
between 34% and 93% of the population the algorithm identified
as having breast cancer truly had the disease [315–317].
Regression-based algorithms were somewhat less variable, with
sensitivity ranging from 76.2% to 90%, specificity from 90% to
99.3%, and the PPV from 36.3% to 93% [318–319]. It should be
VA L U E I N H E A LT H 1 6 ( 2 0 1 3 ) 6 5 5 – 6 6 9
noted that sensitivity and specificity are characteristics of the
‘‘test,’’ that is, the algorithm being validated. In contrast, the PPV
and the negative predictive value are influenced by the prevalence of disease in the population that is being tested. Hence,
researchers implementing algorithms in different populations
may not achieve similar predictive values.
Step 6: Report the Selection Criteria/Algorithm
Accurate and detailed reporting of the sample selection criteria is
essential. A schematic detailing each step of inclusion and
exclusion criteria and the change in sample size following each
data step is highly recommended. It provides the opportunity for
a critical evaluation of methods for case ascertainment, ensures
that study findings can be replicated, and enhances comprehension of study findings. Data sources should be described fully,
including the name of databases and data files used, number of
lives covered, geographic regions included, types of insurance
captured (e.g., fee for service, capitated), and source of information (e.g., health plan, payer, employer, hospital, community
oncology clinics, and registry or other consortium). General
descriptions referring to the data as ‘‘administrative’’ or ‘‘proprietary database’’ should be avoided. Without providing specific
details on the database, readers may question the quality of the
data and validity of the study findings.
Researchers should also provide detailed descriptions of the
specific billing codes used, the number and types (i.e., diagnosis
vs. procedure) of claims that were required, and any relevant
time periods between claims or between diagnosis and treatment
that were implemented as part of inclusion/exclusion criteria.
Any change in coding over time should also be clearly documented. Finally, researchers should present findings from the
validation and/or sensitivity analysis including an assessment of
the algorithm’s capacity to accurately capture the target population and the biases possibly associated with certain inclusion/
exclusion criteria. This will ensure that readers understand the
context in which the study has been defined and the implication
and/or generalizability of study findings.
Conclusions
Using secondary data in the analysis of cancer treatment and
outcomes is of increasing importance. There is a growing need
for clinical outcomes, health economic data, and patientreported outcomes data from nonresearch settings that can be
used to complement the data from randomized controlled trials.
In addition, these data sources can be used to explore the offlabel use of oncology products to both facilitate future development and enhance comprehension of the value equation for
patients. As such, it is essential that the research conducted with
secondary data be of the highest quality.
This article addresses the first issue—how to ascertain a
specific cancer patient population—in a series of technical issues
that researchers must address when using secondary data for
oncology health services research. In addition to identifying an
appropriate patient population, algorithms are needed to identify
disease stage, line of therapy, and other clinical status indicators
such as performance status. While some research has already
been conducted in these areas [320,321], checklists such as the
one presented here are needed for these types of algorithms as
well. Beyond algorithm development, areas for future work in
enhancing oncology data assets include the incorporation of
more mortality data, biomarker data, and patient-reported outcomes. All these components are needed to fully utilize the vast
resource that secondary data provide to oncology researchers.
663
This article provides a step-by-step checklist of issues to
consider, along with recommendations, for drawing a cancerspecific sample in a variety of secondary data sources. It is
important to note that we are not asking researchers to include
in the article every element on the checklist. We do, however,
believe that all articles should contain a detailed description of
the sample selection criteria, the rationale for the criteria, the
validity of the algorithm used to implement the criteria, and the
implications of such algorithm on study results.
We hope that this checklist will facilitate the standardization
of reporting and reviewing of future publications in oncology
health services research using secondary data.
Source of financial support: No funding was received in
support of this study.
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