ABSTRACT
Secondary analyses of large datasets provide a mechanism for
researchers to address high impact questions that would otherwise be
prohibitively expensive and time-consuming to study. This paper presents a
guide to assist investigators interested in conducting secondary data analysis,
including advice on the process of successful secondary data analysis as well
as a brief summary of high-value datasets and online resources for researchers,
including the SGIM dataset compendium (www.sgim.org/go/datasets). The same basic research principles that apply
to primary data analysis apply to secondary data analysis, including the
development of a clear and clinically relevant research question, study sample,
appropriate measures, and a thoughtful analytic approach. A real-world case
description illustrates key steps: (1) define your research topic and question;
(2) select a dataset; (3) get to know your dataset; and (4) structure your
analysis and presentation of findings in a way that is clinically meaningful.
Secondary dataset analysis is a well-established methodology. Secondary
analysis is particularly valuable for junior investigators, who have limited
time and resources to demonstrate expertise and productivity.
INTRODUCTION
Secondary data analysis is
analysis of data that was collected by someone else for another primary
purpose. Increasingly, generalist researchers start their careers conducting
analyses of existing datasets, and some continue to make this the focus of
their career. Using secondary data enables one to conduct studies of
high-impact research questions with dramatically less time and resources than
required for most studies involving primary data collection. For fellows and
junior faculty who need to demonstrate productivity by completing and
publishing research in a timely manner, secondary data analysis can be a key
foundation to successfully starting a research career. Successful completion
demonstrates content and methodological expertise, and may yield useful data
for future grants. Despite these attributes, conducting high quality secondary data
research requires a distinct skill set and substantial effort. However, few
frameworks are available to guide new investigators as they conduct secondary
data analysies.
In this article we describe
key principles and skills needed to conduct successful analysis of secondary
data and provide a brief description of high-value datasets and online resources.
The primary target audience of the article is investigators with an interest
but limited prior experience in secondary data analysis, as well as mentors of
these investigators, who may find this article a useful reference and teaching
tool. While we focus on analysis of large, publicly available datasets, many of
the concepts we cover are applicable to secondary analysis of proprietary
datasets. Datasets we feature in this manuscript encompass a wide range of
measures, and thus can be useful to evaluate not only one disease in isolation,
but also its intersection with other clinical, demographic, and psychosocial
characteristics of patients.
REASONS TO CONDUCT
OR TO AVOID A SECONDARY DATA ANALYSIS
Many worthwhile studies
simply cannot be done in a reasonable timeframe and cost with primary data
collection. For example, if you wanted to examine racial and ethnic differences
in health services utilization over the last 10 years of life, you could
enroll a diverse cohort of subjects with chronic illness and wait a decade (or
longer) for them to die, or you could find a dataset that includes a diverse
sample of decedents. Even for less dramatic examples, primary data collection
can be difficult without incurring substantial costs, including time and
money—scarce resources for junior researchers in particular. Secondary
datasets, in contrast, can provide access to large sample sizes, relevant measures,
and longitudinal data, allowing junior investigators to formulate a
generalizable answer to a high impact question. For those interested in
conducting primary data collection, beginning with a secondary data analysis
may provide a “bird’s eye view” of epidemiologic trends that future primary
data studies examine in greater detail.
Secondary data analyses,
however, have disadvantages that are important to consider. In a study focused
on primary data, you can tightly control the desired study population, specify
the exact measures that you would like to assess, and examine causal
relationships (e.g., through a randomized controlled design). In secondary data
analyses, the study population and measures collected are often not exactly
what you might have chosen to collect, and the observational nature of most
secondary data makes it difficult to assess causality (although some
quasi-experimental methods, such as instrumental variable or regression
discontinuity analysis, can partially address this issue). While not unique to
secondary data analysis, another disadvantage to publicly available datasets is
the potential to be “scooped,” meaning that someone else publishes a similar
study from the same data set before you do. On the other hand, intentional replication
of a study in a different dataset can be important in that it either supports
or refutes the generalizability of the original findings. If you do find that
someone has published the same study using the same dataset, try to find a
unique angle to your study that builds on their findings.
STEPS TO CONDUCTING
A SUCCESSFUL SECONDARY DATA ANALYSIS
The same basic research
principles that apply to studies using primary data apply to secondary data
analysis, including the development of a clear research question, study sample,
appropriate measures, and a thoughtful analytic approach. For purposes of secondary
data analysis, these principles can be conceived as a series of four key steps,
described in Table 1 and the sections
below. Table 2 provides a glossary
of terms used in secondary analysis including dataset types and common sampling
terminology.
A Practical Approach to
Successful Research with Large Datasets
Glossary of Terms Used in
Secondary Dataset Analysis Research
Define your Research Topic and
Question
Case A fellow in general medicine has a strong interest in
studying palliative and end-of-life care. Building on his interest in racial
and ethnic disparities, he wants to examine disparities in use of health
services at the end of life. He is leaning toward conducting a secondary data
analysis and is not sure if he should begin with a more focused research
question or a search for a dataset.Investigators new to secondary data research
are frequently challenged by the question “which comes first, the question or
the dataset?” In general, we advocate that researchers begin by defining their
research topic or question. A good question is essential—an uninteresting study
with a huge sample size or extensively validated measures is still
uninteresting. The answer to a research question should have implications for
patient care or public policy. Imagine the possible findings and ask the
dreaded question: "so what?" If possible, select a question that will
be interesting regardless of the direction of the findings: positive or
negative. Also, determine a target audience who would find your work
interesting and useful.It is often useful to start with a thorough literature
review of the question or topic of interest. This effort both avoids
duplicating others’ work and develops ways to build upon the literature. Once
the question is established, identify datasets that are the best fit, in terms
of the patient population, sample size, and measures of the variables of
interest (including predictors, outcomes, and potential confounders). Once a
candidate dataset has been identified, we recommend being flexible and adapting
the research question to the strengths and limitations of the dataset, as long
as the question remains interesting and specific and the methods to answer it
are scientifically sound. Be creative. Some measures of interest may not have
been ascertained directly, but data may be available to construct a suitable
proxy. In some cases, you may find a dataset that initially looked promising
lacks the necessary data (or data quality) to answer research questions in your
area of interest reliably. In that case, you should be prepared to search for
an alternative dataset.A specific research question is essential to good
research. However, many researchers have a general area of interest but find it
difficult to identify specific research questions without knowing the specific
data available. In that case, combing research documentation for unexamined yet
interesting measures in your area of interest can be fruitful. Beginning with
the dataset and no focused area of interest may lead to data dredging—simply
creating cross tabulations of unexplored variables in search of significant
associations is bad science. Yet, in our experience, many good studies have
resulted from a researcher with a general topic area of interest finding a
clinically meaningful yet underutilized measure and having the insight to frame
a research question that uses that measure to answer a novel and clinically
compelling question (see references for examples).4–8 Dr. Warren Browner once exhorted,
“just because you were not smart enough to think of a research question in
advance doesn’t mean it’s not important!” [quote used with permission].
Select a Dataset
Case
Continued After a review of
available datasets that fit his topic area of interest, the fellow decides to
use data from the Surveillance Epidemiology and End Results Program linked to
Medicare claims (SEER-Medicare).The range and intricacy of large datasets can
be daunting to a junior researcher. Fortunately, several online compendia are
available to guide researchers (Table 3), including one recently
developed by this manuscript’s authors for the Society of General Internal
Medicine (SGIM) (www.sgim.org/go/datasets). The SGIM Research
Dataset Compendium was developed and is maintained by members of the SGIM
research committee. SGIM Compendium developers consulted with experts to
identify and profile high-value datasets for generalist researchers. The
Compendium includes a description of and links to over 40 high-value datasets
used for health services, clinical epidemiology, and medical education
research. The SGIM Compendium provides detailed information of use in selecting
a dataset, including sample sizes and characteristics, available measures and
how data was measured, comments from expert users, links to the dataset, and
example publications (see Box for example). A selection of datasets from
this Compendium is listed in Table 4. SGIM members can request
a one-time telephone consultation with an expert user of a large dataset (see
details on the Compendium website).
Dataset complexity, cost,
and time to acquire the data and obtain institutional review board (IRB)
approval are critical considerations for junior researchers, who are new to
secondary analysis, have few financial resources, and limited time to
demonstrate productivity. Table 4 illustrates the
complexity and cost of large datasets across a range of high value datasets
used by generalist researchers. Dataset complexity increases by number of
subjects, file structure (e.g., single versus multiple records per individual),
and complexity of the survey design. Many publicly available datasets are free,
and others can cost tens of thousands of dollars to obtain. Time to acquire the
datasets and obtain IRB board approval vary. Some datasets can be downloaded
from the web, others require multiple layers of permission and security, and in
some cases data must be analyzed in a central data processing center. If the
project requires linking new data to an existing database, this linkage will
add to the time needed to complete the project and probably require enhanced
data security. One advantage of most secondary studies using publicly available
datasets is the rapid time to IRB approval. Many publicly available large
datasets contain de-identified data and are therefore eligible for expedited
review or exempt status. If you can download the dataset from the web, it is
probably exempt, but your local IRB must make this determination.Linking
datasets can be a powerful method for examining an issue by providing multiple
perspectives of patient experience. Many datasets, including SEER, for example,
can be linked to the Area Resource File to examine regional variation in
practice patterns. However, linking datasets together increases the complexity
and cost of data management. A new researcher might consider first conducting a
study only on the initial database, and then conducting their next study using
the linked database. For some new investigators, this approach can
progressively advance programming skills and build confidence while
demonstrating productivity.
Online Compendia of
Secondary Datasets
Examples of High Value
Datasets
Get to Know your Dataset
Case
Continued The fellow’s primary
mentor encourages him to closely examine the accuracy of the primary predictor
for his study—race and ethnicity—as reported in SEER-Medicare. The fellow has a
breakthrough when he finds an entire issue of the journal Medical Care
dedicated to SEER-Medicare, including a whole chapter on the accuracy of coding
of sociodemographic factors.9In an analysis of primary data you
select the patients to be studied and choose the study measures. This process
gives you a close familiarity with study subjects, and how and what data were
collected, that is invaluable in assessing the validity of their measures, the
potential bias in measuring associations between predictors and outcome
variables (internal validity), and the generalizability of their findings to
target populations (external validity). The importance of this familiarity with
the strengths and weaknesses of the dataset cannot be overemphasized. Secondary
data research requires considerable effort to obtain the same level of
familiarity with the data. Therefore, knowing your data in detail is critical.
Practically, this objective requires scouring online documentation and
technical survey manuals, searching PubMed for validation studies, and closely
reading previous studies using your dataset, to answer the following types of
questions: Who collected the data, and for what purpose? How did subjects get
into your dataset? How were they followed? Do your measures capture what you
think they capture?We strongly recommend taking advantage of help offered by
the dataset managers, typically described on the dataset’s website. For
example, the Research Data Assistance Center (ResDAC) is a dedicated resource
for researchers using data from the Centers for Medicare and Medicaid Services
(CMS).Assessing the validity of your measures is one of the central challenges
of large dataset research. For large survey datasets, a good first step in
assessing the validity of your measures is to read the questions as they were
asked in the survey. Some questions simply have face validity. Others,
unfortunately, were collected in a way that makes the measure meaningless,
problematic, or open to a range of interpretations. These ambiguities can occur
in how the question was asked or in how the data were recorded into response
categories.Another essential step is to search the online documentation and
published literature for previous validation studies. A PubMed search using the
dataset name or measure name/type and the publication type “validation studies”
is a good starting point. The key question for a validity study relates to how
and why the question was asked and data were collected (e.g., self-report,
chart abstraction, physical measurements, billing claims) in relationship to a
gold standard. For example, if you are using claims data you should recognize that
the primary purpose of those data was not for research, but for reimbursement.
Consequently, claims data are limited by the scope of services that are
reimbursable and the accuracy of coding by clinicians completing encounter
forms for billing or by coders in the claims departments of hospitals and
clinics. Some clinical measures can be assessed by asking subjects if they have
the condition of interest, such as self reported diagnosis of hypertension.
Self-reported data may be adequate for some research questions (e.g., does a
diagnosis of hypertension lead people to exercise more?), but inadequate for
others (e.g., the prevalence of hypertension among people with diabetes). Even
measured data, such as blood pressure, have limitations in that methods of measurement
for a study may differ from methods used to diagnose a disorder in the
clinician’s office. In the National Health and Nutrition Examination Survey,
for example, subject’s blood pressure is based on the average of several
measures in a single visit. This differs from the standard clinical practice of
measuring blood pressure at separate office visits before diagnosing
hypertension. Rarely do available measures capture exactly what you are trying
to study. In our experience measures in existing datasets are often good enough
to answer the research question, with proper interpretation to account for what
the measures actually assesses and how they differ from the underlying
constructs.Finally, we suggest paying close attention to the completeness of
measures, and evaluating whether missing data are random or non-random (the
latter might result in bias, whereas the former is generally acceptable).
Statistical approaches to missing data are beyond the scope of this paper, and
most statisticians can help you address this problem appropriately. However,
pay close attention to “skip patterns”; some data are missing simply because
the survey item is only asked of a subset for which it applies. For example, in
the Health and Retirement Study the question about need for assistance with
toileting is only asked of subjects who respond that they have difficulty using
the toilet. If you were unaware of this skip pattern and attempted to study
assistance with toileting, you would be distressed to find over three-quarters
of respondents had missing responses for this question (because they reported
no difficulty using the toilet).Fellows and other trainees usually do their own
computer programming. Although this may be daunting, we encourage this practice
so fellows can get a close feel for the data and become more skilled in
statistical analysis. Datasets, however, range in complexity (Table 4). In our experience,
fellows who have completed introductory training in SAS, STATA, SPSS, or other
similar statistical software have been highly successful analyzing datasets of
moderate complexity without the on-going assistance of a statistical programmer.
However, if you do have a programmer who will do much of the coding, be closely
involved and review all data cleaning and statistical output as if you had
programmed it yourself. Close attention can reveal all sorts of patterns,
problems, and opportunities with the data that are obscured by focusing only on
the final outputs prepared by a statistical programmer. Programmers and
statisticians are not clinicians; they will often not recognize when the values
of variables or patterns of missingness don’t make sense. If estimates seem
implausible or do not match previously published estimates, then the analytic
plan, statistical code, and measures should be carefully rechecked.Keep in mind
that “the perfect may be the enemy of the good.” No one expects perfect
measures (this is also true for primary data collection). The closer you are to
the data, the more you see the warts—don’t be discouraged by this. The measures
need to pass the sniff test, in other words have clinical validity based
primarily on judgement that they make sense clinically or scientifically, but
also supported where possible by validation procedures, reference to auditing
procedures, or in other studies that have independently validated the measures
of interest.
Structure your Analysis and
Presentation of Findings in a Way that Is Clinically Meaningful
Case
continued The fellow finds that
Blacks are less likely to receive chemotherapy in the last 2 weeks of life
(Blacks 4%, Whites 6%, p < 0.001). He debates the meaning of this
statistically significant 2% absolute difference.Often, the main challenge for
investigators who are new to secondary data analysis is carefully structuring
the analysis and presentation of findings in a way that tells a meaningful
story. Based on what you’ve found, what is the story that you want your target
audience to understand? When appropriate, it can be useful to conduct carefully
planned sensitivity analysis to evaluate the robustness of your primary
findings. A sensitivity analysis assesses the effect of variation in
assumptions on the outcome of interest. For example, if 10% of subjects did not
answer a “yes” or “no” question, you could conduct sensitivity analyses to
estimate the effects of excluding missing responses, or categorizing them as
all “yes” or all “no.” Because large datasets may contain multiple measures of
interests, co-variates, and outcomes, a frequent temptation is to present huge
tables with multiple rows and columns. This is a mistake. These tables can be
challenging to sort through, and the clinical importance of the story resulting
from the analysis can be lost. In our experience, a thoughtful figure often
captures the take-home message in a way that is more interpretable and
memorable to readers than rows of data tables.You should keep careful track of
subjects you decide to exclude from the analysis and why. Editors, reviewers,
and readers will want to know this information. The best way to keep track is
to construct a flow diagram from the original denominator to the final
sample.Don’t confuse statistical significance with clinical importance in large
datasets. Due to large sample sizes, associations may be statistically
significant but not clinically meaningful. Be mindful of what is meaningful
from a clinical or policy perspective. One concern that frequently arises at
this stage in large database research is the acceptability of “exploratory”
analyses, or the practice of examining associations between multiple factors of
interest. On the one hand, exploratory analyses risk finding a significant
association by chance alone from testing multiple associations (a
false-positive result). On the other hand, the critical issue is not a
statistical one, but rather whether the issue is important.10 Exploratory analyses are
acceptable if done in a thoughtful way that serves an a priori hypothesis, but
not if merely data dredging looking for associations.We recommend consulting
with a statistician when using data from a complex survey design (see
Table 2) or developing a
conceptually advanced study design, for example, using longitudinal data,
multilevel modeling with clustered data, or surivival analysis. The value of
input (even if informal) from a statistician or other advisor with substantial
methodological expertise cannot be overstated.
CONCLUSIONS
Case
Conclusion Two years after he
began the project the fellow completes the analysis and publishes the paper in
a peer-reviewed journal. A 2-year timeline from inception to publication is
typical for large database research. Academic potential is commonly assessed by
the ability to see a study through to publication in a peer-reviewed journal.
This timeline allows a fellow who began a secondary analysis at the start of a
2-year training program to search for a job with an article under review or in
press.In conclusion, secondary dataset research has tremendous advantages,
including the ability to assess outcomes that would be difficult or impossible
to study using primary data collection, such as those involving exceptionally
long follow-up times or rare outcomes. For junior investigators, the potential
for a shorter time to publication may help secure a job or career development
funding. Some of the time “saved” by not collecting data yourself, however,
needs to be “spent” becoming familiar with the dataset in intimate detail.
Ultimately, the same factors that apply to successful primary data analysis
apply to secondary data analysis, including the development of a clear research
question, study sample, appropriate measures, and a thoughtful analytic
approach.
Acknowledgments
Contributors The authors would like to thank Sei Lee, MD, Mark Freidberg,
MD, MPP, and J. Michael McWilliams, MD, PhD, for their input on portions of
this manuscript.
Grant
Support Dr. Smith is supported by a Research Supplement to Promote
Diversity in Health Related Research from the National Institute on Aging
(R01AG028481), the National Center for Research Resources UCSF-CTSI (UL1
RR024131), and the National Palliative Care Research Center. Dr. Steinman is
supported by the National Institute on Aging and the American Federation for
Aging Research (K23 AG030999). An unrestricted grant from the Society of
General Internal Medicine (SGIM) supported development of the SGIM Research Dataset
Compendium.
Prior
Presentations An earlier version of
this work was presented as a workshop at the Annual Meeting of the Society of
General Internal Medicine in Minneapolis, MN, April 2010.
Open
Access This article is distributed under the terms of the Creative
Commons Attribution Noncommercial License which permits any noncommercial use,
distribution, and reproduction in any medium, provided the original author(s)
and source are credited.
Conflict of Interest None
disclosed.
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