Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_612_Библиотеки_им_академика_М_И_Перельмана
.pdf
For those starting in research or expanding into new areas of
inquiry, this rigorous question development process highlights the
value of a research team—experience with other methods,
perspectives on ethics and feasibility, and an overarching focus on
clinical and research alignment.
SELECTING THE BEST DESIGN
Just as the surgeon trains to master options for reconstruction, the
researcher builds a skillset of research methodologies. For a specific
research idea, there are several methods that can be applied with
tailoring of the research question. Other research proposals are best
achieved with a specific method. For example, exploring the
mediators of inequities in access to breast reconstruction could be
done with interviews of patients considering reconstruction
(qualitative), analysis after change to policy (causal inference), and
with comparison between groups (traditional quantitative
approaches). The choice among methods requires matching the
research team’s skill (established or evolving) with the aspect of the
problem that is most urgent. Similarly, if a researcher starts reading a
study on a topic and it does not use the design they would have
used, they should review the choices the team made as well as the
alternative aspects of the problem their envisioned design would
have elucidated.
Once familiar with a methodology, a surgeon-scientist can apply it
to other research questions. It is possible to work in reverse and try
to design a project using a specific methodology, but we recommend
early researchers keep their options open to find high-impact
research opportunities and select the design for the question rather
than vice versa. This is especially powerful because it encourages a
researcher to keep a running list of possible research questions that
can be revisited and reviewed for feasibility across the course of a
career. Even for a surgeon without plans to conduct research,
working knowledge of a method can enable careful consideration of
published results. A busy clinical surgeon familiar with the principles
of research design can vet the new evidence around a new
technique before changing practice. In the same way peer-reviewed
https://t.me/med1917
publications and presentations can disseminate changes to practice;
they can introduce a researcher to a new methodology that facilitates
addressing a problem from a different angle. In this way, even an
experienced researcher can use a new project to master another
method, as part of a collaborative team.
Qualitative Methods
Quantitative methods are the most common in medical and surgical
research. However, researchers must consider all methodologies to
explore novel questions. Qualitative research is underused, but it
can also be combined with quantitative data through mixed methods
approaches.37 For example, qualitative and quantitative methods
have both been successfully applied to a range of clinical problems
in medicine and plastic surgery. Qualitative approaches yield rich, indepth stakeholder experiences. However, surgical researchers and
reviewers are often most comfortable with quantitative approaches.
Although qualitative undertakings can be time consuming, it is
important to recognize their strengths. There is increased attention to
developed patient-reported outcome measures (eg, BREAST-Q
32
and WOUND-Q38), and clinicians and researchers are starting to
prospectively collect these measures. Although these are now
available as scales, they were developed through interviews, pilot
groups, and psychometric validation. Lane and colleagues utilized
the BREAST-Q, BODY-Q, and patient health questionnaire-9 to
investigate patient satisfaction and quality of life after genderaffirming mastectomy. These patient-reported outcome measures
allowed for assessing the resultant improvement of body image,
chest appearance, and mood.32 In the absence of these established
tools, or if a researcher wanted to further understand the
perioperative experience for these patients, a qualitative approach to
explore the emotions around this area could be considered with
active listening or semi-structured interviews.
Quantitative Methods
Cohort
https://t.me/med1917
Cohort studies can be either prospective or retrospective in their
data collection. Traditional data sources at the national level are the
National Surgical Quality Improvement Program (NSQIP) and the
Tracking Operations & Outcomes for Plastic Surgeons (TOPS).
Although each is maintained by a national surgical society, TOPS
(the American Society of Plastic Surgeons) contains variables of
specific interest to plastic surgeons that are not captured in NSQIP
(the American College of Surgeons).39-41 These data sources should
be considered early, as an inherent need for patient, surgeon, facility,
or community data that are not captured in these datasets may
require the design of a new prospective dataset. Cohort studies are
traditionally longitudinal, in that a subject is enrolled or monitored
over time. Beyond the limitations of data collected retrospectively,
this can be challenged by loss to follow-up. Additionally, if an
outcome is rare or not clearly coded in these datasets, it often
cannot be captured sufficiently through preexisting data. Although
this realization is often the start of multi-institutional research
collaboratives, not every researcher may be positioned to launch
such an endeavor at the time of reaching that conclusion. A cohort
study that is prospective, or collecting data as patients are enrolled,
can capture detailed data of interest to researchers but requires a
time-intensive and costly research infrastructure.
Case-Control
Case-control designs are best suited for rare outcomes. In
epidemiological research, this traditionally starts with rare cancers
that may develop decades after exposure. This then identifies the
cases, who are then compared with controls without the outcome.
These results are presented as odds ratios with descriptive summary
statistics. For example, researchers interested in the surgical site
infection risk of immediate implant-based breast reconstruction
reviewed 10 years of cases to identify all cases with postoperative
surgical site infections and match them to control cases. For a rare
complication, this method allowed for identification of obesity,
hypertension, and neoadjuvant therapy as risk factors. When done to
explore exposures not captured in real time in the electronic medical
https://t.me/med1917
record, this approach is subject to recall bias. That is, patients who
experienced a complication may remember more details of their
postoperative course than patients who experienced an uneventful
recovery.
Cross-Sectional
For questions of prevalence or volume, cross-sectional study
designs can be useful. These are also among the most
straightforward designs for analysis. There are several examples in
the literature, ranging from measurement of the imposter
phenomenon in academic plastic surgery to the financial toxicity
among female patients with breast cancer undergoing
reconstruction.
42,43
Although these descriptive studies can be
valuable, they should be designed to identify targets for further
investigation. Consider, for example, the many descriptive studies of
inequitable outcomes. These cross-sectional studies are limited in
that they cannot infer causality, but they can benchmark outcomes or
suggest prevalence of the problem. Similarly, many emerging studies
of price transparency and CMS payment systems are cross-sectional
in design.
11,44
Randomized Controlled Trial
These trials are the standard in biomedical research. Although
perhaps most often associated with drug development and testing,
these trials have been implemented to evaluate causality of technical
and systemic changes. For instance, the hernia research group at
the Cleveland Clinic designed a randomized controlled trial (RCT) to
investigate the role of transfascial fixation sutures in large ventral
hernia repairs. Patients who met inclusion criteria were randomized
by a member of the research team intraoperatively. Patients received
either 8 transfascial sutures or 0 transfascial sutures (with 8 sham
incisions).45 Although this study demonstrates use of a randomized
trial to investigate a technical approach to surgery with a traditional
placebo arm, it also highlights the challenges of conducting them.
This research group was unique in that it was already in place with a
prospective data collection infrastructure. Conducting an RCT,
https://t.me/med1917
whether at one institution or across multiple institutions (known as a
multi-institutional trial), requires significant investment of time and
resources as well as buy-in from those who are being told their
decisions are being determined by a random allocation. This was
illustrated with the National Institutes of Health–funded Wrist and
Radius Injury Surgical Trial (WRIST), in which patients with unstable
distal radius fractures were randomized to percutaneous pinning,
external fixation, or internal fixation.
46
Furthermore, the upfront design should consider the unmeasured
features of the institutions or surgeons. For example, the 0 or 8
suture approaches of the hernia group at the Cleveland Clinic may
not reflect the number of fixating sutures used by other hernia
surgeons. In this choice of intervention (no more than 8 sutures), the
trial design limited generalizability to other surgeons who use more
fixating suture. In contrast, the approach in WRIST includes a range
of surgical approaches to unstable distal radius fracture. Similarly,
exclusion criteria can also limit generalizability to other patients. A
trial that excludes patients with prior radiation will not answer the
question of the best approach for those patients.
By using randomization that balances both measured and
unmeasured confounders, RCT results infer causality. Although most
often considered to imply randomization at the level of the patient,
RCTs can randomize treatment at the patient, surgeon, hospital, or
neighborhood level. This can be accomplished with cluster
randomization. One excellent application of a cluster randomized
trial divided the master list of abandoned houses in Philadelphia into
63 clusters. Each cluster of houses was then randomized between
three study arms: full remediation, trash cleanup only, and no
intervention to evaluate gun violence and subjective safety. There
was a decrease in gun violations and gun assaults in the full
remediation arm.47 Similarly to geographic clusters, clustered
randomization can occur at the hospital or surgeon level.
RCTs are often performed in ideal conditions; that is, a drug or
treatment may be provided at no cost or participants’ travel may be
covered. Researchers interested in the “real-world” impact of an
intervention may also consider the pragmatic trial. Key principles of a
https://t.me/med1917
pragmatic RCT are enrolling population close to practice population,
community sites rather than research sites, appropriate comparison
arm (standard of care rather than placebo), and relevant patientreported outcomes and clinical outcomes.
48
Causal Inference
When randomization is not feasible, there is growing attention to
quasi-experimental designs to consider causality from observational
data.49 As the healthcare system continues to become more
complex, controlling allocation to receipt of a surgical technique,
coverage for a reconstructive procedure, or postoperative resources
is not always feasible. A researcher cannot petition to reallocate
states to Medicaid expansion to study the causal relationship
between this policy and reconstructive outcomes.50 However,
researchers can use the “natural experiment” of expansion to
compare rates of breast cancer screening and reconstruction
between the states with and without Medicaid expansion.
15,18,51
These study designs are being introduced to surgical research from
the field of econometrics.
49
Selecting the Method
Facing complex clinical and health system problems, it can be
overwhelming to consider every possible question-method pairing.
To decide on which question-method will yield high-impact research,
we first consider what is most important to us and the field to answer.
Next, we consider which of those question-method pairings is the
most feasible. For instance, given the lack of all-payer database, it is
not currently possible to assess the national outcomes of uninsured
hand trauma patients. However, ensuring those patients have
access to reconstructive surgery requires a viable safety net. This
further underscores the importance of creating a conceptual model
early. Even if one question or approach is not yet feasible, there are
often other angles. A researcher must consider what aspect of the
identified problem most bothers them to find a motivating and highimpact question.
https://t.me/med1917
ANALYTIC PLAN
The best analytic plans are based on conceptual models. Even when
designing an analysis of a retrospective dataset, the researcher
should understand the potential for interactions, the role of
mediators, and possible confounders. For some projects the
researcher is designing an analysis with statistical experts, but
especially early on the researcher or research fellow is also
performing the analysis. At this point, with a clearly defined research
question and study design, the conceptual model is more detailed
than the models created earlier in the process. It now may include
specific variables and their relationship to desired primary and
secondary outcomes.
A conceptual model is often sketched out with pen and paper and
redrawn multiple times. This is because additional relationships will
emerge as a literature review proceeds. It can also allow for visual
representation of potential confounders or other factors linked to the
independent variable that may enhance, weaken, or reverse the
association between the independent and dependent variables. A
confounder must meet three criteria: it must cause the outcome, be
associated with the independent variable, and not be the connection
between the two. One of the most common forms of confounding in
surgical outcomes research is confounding by indication. This is
seen when the indication for a given treatment can also impact the
outcome.52 For instance, severe tissue loss may require more
intensive reconstruction with longer length of stay. The degree of
tissue loss not only leads to the choice of intervention
(reconstruction) but is also linked to longer lengths of stay regardless
of intervention. Of the study designs, RCTs are often suggested to
control for measured (eg, tissue loss extent) and unmeasured (eg,
lack of family support at home) confounders. However, researchers
will also come across statistical methods including propensity scores
and logistic regression to adjust for these confounders. Although all
methods have limitations, the investigator must recognize potential
confounders and discuss them thoroughly.
Although much of the planning process can feel broad, the analytic
plan should be granular in detail. The details of this plan will differ
https://t.me/med1917
based on the chosen study design. For cross-sectional studies,
descriptive statistics are sufficient. For a national study, multilevel
modeling approaches may be necessary. Mixed methods
approaches can vary in combining quantitative and qualitative
findings or proceeding sequentially. However, we recommend a
standard analytic plan format that anticipates the key elements
necessary to initiate the project.
Population
Research questions are often written with a broad description of the
population of interest. The analytic plan narrows that broad group
into a series of criteria. For example, instead of “women with breast
cancer,” a study population might be patients aged 18 to 65 years
referred for immediate reconstruction evaluation at a tertiary
academic center. The population for this breast cancer
reconstruction access project could range from the residents of a
community to a national comparison across cancer centers. This
granularity asks the researcher to define the population, the
sampling method, and the inclusion/exclusion criteria. In other
words, what designation will be used to determine cancer center
status? Are centers excluded if the designation was only granted for
a portion of the years of the study? This step also requires
recognizing the predetermined inclusion or exclusion criteria of
existing datasets.
Data Source
Existing data sources often determine population for retrospective
work. That is, Medicare claims data narrow the patient population to
Medicare-eligible patients receiving care from plastic surgeons
participating in Medicare for procedures reimbursed by Medicare. If
the desired study population is those 18 to 65 years old and only
Medicare claims are available, the project is not feasible. However, it
could also direct the team to explore options for commercial
insurance claims databases. Many prospective studies are launched
to fill gaps in existing data sources. That is, if the population of
interest is not well represented for the outcome or procedure of
interest, a larger prospective study is indicated.
https://t.me/med1917
If a dataset is being created, whether collected prospectively or
combined from existing databases, this data source section should
include the proposed data dictionary. This includes variable names
(either already assigned or what they will be named), format of
variables, units of measurement, and any codes (eg, 1 = yes,
0 = no). If datasets are being downloaded from government sources
(eg, CMS), record the web address and description of the dataset for
future reference. The researcher should also download complete
data dictionaries for preexisting databases being used. It is common
for updated data to be uploaded between the time of analysis and
publication, and a project should cite the exact dataset that was
used.
For qualitative approaches, the data source section includes the
preliminary interview guide or survey. A rigorously developed semistructured interview guide is revised iteratively. Similarly, a survey is
piloted and should be validated before wide distribution. Most
institutional review boards (IRBs) will expect these research tools to
be submitted for research project approval. The research team
should work with the IRB to follow requirements for revisions and
modifications to planned research tools.
CONSIDERING LIMITATIONS AND BIAS
Many methodological strengths and critiques will become more
apparent once the analytic plan is designed. These may not be
issues with feasibility, but effective research projects consider them
early. In other words, a researcher should begin thinking about
possible reviewer critiques of the plan before the data collection is
underway. Potential limitations and bias should be considered and
discussed during the design process. The presence of limitations or
the recognition of bias should not stop a study from proceeding.
Every study design carries its own limitations. However, early
recognition of these limitations can allow for early pivots in study
design and produce stronger final manuscripts.
There are situations when these limitations will prevent the
research team from answering the study question. For example, a
proposed project to investigate the experience of patients whose
https://t.me/med1917
primary language is not English at an institution that rarely provides
care to those patients is difficult. Identifying limitations early can
facilitate improvement to study design and help find another site with
which to collaborate. For example, a project designed to investigate
barriers to breast reconstruction with proposed recruitment of
patients at a plastic surgery clinic carries a selection bias.
Recognizing this prior to the start of the project provides opportunity
to overcome this bias by recruiting from a breast oncology clinic or
even mobile mammography unit. Rather than only considering these
limitations when prompted by a reviewer, a researcher who
proactively anticipates and mitigates these critiques will lead a more
rigorous study.
There is no design free of bias, but rigorous studies should clearly
identify potential sources of bias. The internal validity of a study, its
ability to produce valid results, is threatened by random error
(chance) and systematic error (bias). This is a systematic variation
that causes the study’s estimated association (eg, risk ratio) to differ
from the true association. This contrasts with chance. There are
three categories of systematic bias: selection bias, confounding and
information bias.53 Confounders have been discussed previously.
Selection bias is most well-known as it is commonly discussed in
study enrollment. This can occur due to preexisting referral patterns
that may direct more complex cases to a study site (referral bias) or
patients who decline to participate in a study due to lack of reliable
transportation (nonrespondent bias).54 Information bias captures
measurement error leading to misclassification of exposures and
outcomes, interviewer bias, and recall bias.
TAKING THE SO WHAT? TO NOW WHAT?
A well-crafted research plan should include the anticipated
implications of findings. Similarly, the manuscript can be partially
drafted even before the analysis is complete. However, an impactful
research project should not end with publication. Findings published
in high-impact journals (as scored by impact factor) are well
regarded for the audience they could reach. However, dissemination
of finding can include sharing results not only among professional
https://t.me/med1917
Соседние файлы в папке Библиотека им академика М.И. Перельмана