Description
Weighing the Evidence
When conducting original research, the final step researchers must complete is weighing the evidence and interpreting the meanings of their data, statistics, and analyses. This is the culmination of the research process in which all of the research methods and designs can be synthesized into a meaningful conclusion. In this stage, researchers should formulate explanations for what their data indicates, determine whether the data answers their initial research question, identify areas of uncertainty, and consider directions for further research.
In this Discussion, you focus on one of the research articles that you identified for Part 2 of the Course Project (Literature Review). You then explore the process of how the researchers generated conclusions based on their data, consider other possible interpretations of their data, and formulate ideas for further research.
To prepare:
- Review this week’s Learning Resources, focusing on how researchers find meaning in their data and generate sound conclusions. Pay particular attention to Table 2 in the article, “Study Design in Medical Research.”
- Revisit the 5 articles that you identified in Part 2 of the Course Project. Select one to consider for the purpose of this Discussion.
- Read sections of the chosen article where the data is presented, analyzed, and interpreted for meaning. What reasoning process did the researchers use to formulate their conclusions? What explanation did they give to support their conclusions? Were there any weaknesses in their analysis or conclusions?
- Consider possible alternate conclusions that the researchers could have drawn based on their data.
- Examine the findings that the article presents and consider how well they addressed the researcher’s initial question(s). What additional research could be done to build on these findings and gain a fuller understanding of the question?
By Day 3
Post an APA citation and brief summary of the research article that you selected. Describe the data and the results of any statistical tests or analyses presented in the article. Explain how the researchers formulated their conclusion, any weaknesses in their analysis or conclusions, and offer at least one alternate interpretation of their data. Propose at least one additional research study that could be done to further investigate this research topic.
MEDICINE
REVIEW ARTICLE
Study Design in Medical Research
Part 2 of a Series on the Evaluation of Scientific Publications
Bernd Röhrig, Jean-Baptist du Prel, Maria Blettner
SUMMARY
Background: The scientific value and informativeness of
a medical study are determined to a major extent by the
study design. Errors in study design cannot be corrected
afterwards. Various aspects of study design are discussed
in this article.
Methods: Six essential considerations in the planning and
evaluation of medical research studies are presented and
discussed in the light of selected scientific articles from
the international literature as well as the authors’ own
scientific expertise with regard to study design.
Results: The six main considerations for study design are
the question to be answered, the study population, the unit
of analysis, the type of study, the measuring technique, and
the calculation of sample size.
Conclusions: This article is intended to give the reader
guidance in evaluating the design of studies in medical
research. This should enable the reader to categorize
medical studies better and to assess their scientific quality
more accurately.
Dtsch Arztebl Int 2009; 106(11): 184–9
DOI: 10.3238/arztebl.2009.0184
Key words: study design, quality, study, study type,
measuring technique
M
edical research studies can be split into five
phases—planning, performance, documentation, analysis, and publication (1, 2). Aside from financial, organizational, logistical and personnel questions,
scientific study design is the most important aspect of
study planning. The significance of study design for
subsequent quality, the relability of the conclusions,
and the ability to publish a study are often underestimated
(1). Long before the volunteers are recruited, the study
design has set the points for fulfilling the study objectives. In contrast to errors in the statistical evaluation,
errors in design cannot be corrected after the study has
been completed. This is why the study design must be
laid down carefully before starting and specified in the
study protocol.
The term “study design” is not used consistently in
the scientific literature. The term is often restricted to
the use of a suitable type of study. However, the term
can also mean the overall plan for all procedures involved in the study. If a study is properly planned, the
factors which distort or bias the result of a test procedure
can be minimized (3, 4). We will use the term in a
comprehensive sense in the present article. This will
deal with the following six aspects of study design:
the question to be answered, the study population, the
type of study, the unit of analysis, the measuring technique, and the calculation of sample size—, on the
basis of selected articles from the international literature and our own expertise. This is intended to help
the reader to classify and evaluate the results in publications. Those who plan to perform their own studies
must occupy themselves intensively with the issue of
study design.
Question to be answered
Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI),
Johannes Gutenberg-Universität Mainz: Dr. rer. nat. Röhrig, Prof. Dr. rer. nat.
Maria Blettner
Zentrum Präventive Pädiatrie, Zentrum für Kinder- und Jugendmedizin,
Johannes Gutenberg-Universität Mainz: Dr. med. du Prel, M.P.H
184
The question to be answered by the research is of
decisive importance for study planning. The research
worker must be clear about the objectives. He must
think very carefully about the question(s) to be
answered by the study. This question must be operationalized, meaning that it must be converted into a
measurable and evaluable form. This demands an
adequate design and suitable measurement parameters.
A distinction must be made between the main questions
to be answered and secondary questions. The result of
the study should be that open questions are answered
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
MEDICINE
and possibly that new hypotheses are generated. The
following questions are important: Why? Who?
What? How? When? Where? How many? The question
to be answered also implies the target group and
should therefore be very precisely formulated. For example, the question should not be “What is the quality
of life?”, but must specify the group of patients (e.g.
age), the area (e.g. Germany), the disease (e.g. mammary carcinoma), the condition (e.g. tumor stage 3),
perhaps also the intervention (e.g. after surgery), and
what endpoint (in this case, quality of life) is to be determined with which method (e.g. the EORTC QLQC30 questionnaire) at what point in time. Scientific
questions are often not only purely descriptive, but also
include comparisons, for example, between two
groups, or before and after the intervention. For example,
it may be interesting to compare the quality of life of
breast cancer patients with women of the same age
without cancer.
The research worker specifies the question to be answered, and whether the study is to be evaluated in a
descriptive, exploratory or confirmatory manner.
Whereas in a descriptive study the units of analysis
are to be described by the recorded variables (e.g.
blood parameters or diagnosis), the aim in an exploratory analysis is to recognize connections between
variables, to evaluate these and to formulate new
hypotheses. On the other hand, confirmatory analyses
are planned to provide statistical proofs by testing
specified study hypotheses.
The question to be answered also determines the type
and extent of the data to be recorded. This specifies
which data are to be recorded at which point in time.
In this case, less is often more. Data irrelevant to the
question(s) to be answered should not be collected for
the moment. If too many variables are recorded at too
many time points, this can lead to low participation
rates, high dropout rates, and poor compliance from
the volunteers. The experience is then that not all data
are evaluated.
The question to be answered and the strategy for
evaluation must be specified in the study protocol before
the study is started.
Study population
The question to be answered by the study implies that
there is a target group for whom this is to be clarified.
Nevertheless, the research worker is not primarily
interested in the observed study population, but in
whether the results can be transferred to the target
population. Accordingly, statistical test procedures
must be used to generalize the results from the sample
for the whole population (figure 1).
The sample can be highly representative of the
study population if it is properly selected. This can be
attained with defined and selective inclusion and
exclusion criteria, such as sex, age, and tumor stage.
Study participants may be selected randomly, for
example, by random selection through the residents’
registration office, or consecutively, for example,
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
FIGURE 1
Connection between
overall population
and study
population/data
all patients in a clinical department in the course of
one year.
With a selective sample, a statement can only be
made about a population corresponding to these selection criteria. The possibility of generalizing the results
may, for example, be greatly influenced by whether
the patients come from a specialist practice, a specialized hospital department or from several different
practices.
The possibility of generalization may also be influenced by the decision to perform the study at a single
institution or site, or at several (multicenter study).
The advantages of a multicenter study are that the
required number of patients can be reached within a
shorter period and that the results can more readily be
generalized, as they are from different treatment centers.
This raises the external validity.
Type of study
Before the study type is specified, the research worker
must be clear about the category of research. There is
a distinction in principle between research on primary
data and research on secondary data.
Research on primary data means performing the
actual scientific studies, recording the primary study
data. This is intended to answer scientific questions
and to gain new knowledge.
In contrast, research on secondary results involves
the analysis of studies which have already been performed and published. This may include (renewed)
analysis of recorded data, perhaps from a register,
from population statistics, or from studies. Another
objective may be to win a comprehensive overview of
the current state of research and to come to appropriate
conclusions. In secondary data research, a distinction
is made between narrative reviews, systematic reviews,
and meta-analyses.
The underlying question to be answered also influences the selection of the type of study. In primary
research, experimental, clinical and epidemiological
research are distinguished.
Experimental research includes applied studies,
such as animal experiments, cell studies, biochemical
and physiological investigations, and studies on
185
MEDICINE
Portrayal of the
terms reliability
(precision) and
validity (trueness)
using a target
FIGURE 2
material properties, as well as the development of analytical and biometric procedures.
Clinical research includes interventional and noninterventional studies. The objective of interventional
clinical studies (clinical trials) is “to study or demonstrate the clinical or pharmacological activities of
drugs” and “to provide convincing evidence of the
safety or efficacy of drugs” (AMG, German Drugs Act
§4) (5). In clinical studies, patients are randomly
assigned to treatment groups. In contrast, noninterventional clinical studies are observational studies, in
which patients are given an individually specified
treatment (6, 7).
Epidemiological research studies the distribution
and changes with time of the frequency of diseases
and of their causes. Experimental studies are distinguished from observational studies (7, 8). Interventional
studies (such as vaccination, addition of food
additives, fluoride addition to drinking water) are of
experimental character. Examples of observational
epidemiological studies include cohort studies, case
control studies, cross-sectional studies, and ecological
studies.
A subsequent article will discuss the different study
types in detail.
Unit of analysis
The unit of analysis (investigational unit) must be
specified before starting a medical study. In a typical
clinical study, the patient is the unit of analysis.
However, the unit of analysis may also be a technical
model, hereditary information, a cell, a cellular structure,
an organ, an organ system, a single test individual
(animal or man), or specified subgroup or the population
186
of a region or of a country. In systematic reviews, the
unit of analysis is a single study. The sample then
includes the total of all units of analysis. The interesting
information or data (observations, variables,
characteristics) are recorded for the statistical units.
For example, if the heart is being investigated in a patient (the unit of analysis), the heart rate may be measured as a characteristic of performance.
The selection of the unit of analysis influences the
interpretation of the study results. It is therefore
important for statistical reasons to know whether the
units of analysis are dependent or independent of each
other with respect to the outcome parameter. This
distinction is not always easy. For example, if the
teeth of test persons are the unit of analysis, it must be
clarified whether these are independent with respect
to the question to be answered (i.e. from different test
persons) or dependent (i.e. from the same test person).
Teeth in the mouth of a single test person are generally
dependent, as specific factors, such as nutrition and
teeth cleaning habits, act on all teeth in the mouth in
the same way. On the other hand, extracted teeth are
generally independent study objects, as there are no
longer any shared factors which influence them. This
is particularly the case when the teeth are subject to
additional preparation, for example, cutting or grinding. On the other hand, if the observations are on tooth
characteristics developed before extraction, these
characteristics must be regarded as dependent.
Measuring technique
The term “measuring technique” includes the use of
measuring instruments and the method of measurement.
Use of measuring instruments
Measuring instruments include instruments which
specifically record measuring data (such as blood
pressure or laboratory parameters), as well as data
collection with standardized or self-designed questionnaires (for example, quality of life, depression, or
satisfaction).
During the validation of a measuring instrument, its
quality and practicability are evaluated using statistical parameters. Unfortunately, the nomenclature is not
fully standardized and also depends on the special area
(for example, chemical analysis, psychological studies
with questionnaires, or diagnostic studies). It is
always the case that a measuring instrument of high
quality should be of high precision and validity.
Precision describes the extent to which a measuring
technique consistently provides the same results if the
measurement is repeated (9). The reliability (or precision) provides information on the precision or the
occurrence of random errors. If the precision is low,
the correlation coefficients are low, measurements are
imprecise and a larger sample size is needed (9). On
the other hand, the validity (accuracy of the mean or
trueness) of a measuring instrument is high if it measures exactly what it is supposed to measure. Thus the
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
MEDICINE
validity provides information on the occurrence of
systematic errors (10). Whereas the precision describes
the difference (variance) between repeated measurements, the validity reflects the difference between the
measured and true parameter (10). Figure 2 portrays
the terms, using a target as a model.
Reliability and validity are subsumed in the term
accuracy (11, 12). The accuracy is only high when
both the precision and the validity are high. Table 1
summarizes the important terms to validate a measurement method.
The problem is not only that the measurements may
be invalid or false, but also that the measurements
may lead to erroneous conclusions. External and internal validity can be distinguished (13). External validity
means the possibility of generalizing the study results
for the study population to the target population. The internal validity is the validity of a result for the actual
question to be answered. This can be optimized by
detailed planning, defined inclusion and exclusion
criteria, and reduction of external interfering factors.
Measurement plan
The measurement plan describes the number and time
points of the measurements to be performed. To obtain
comparable and objective measurements, the
measurement conditions must be standardized. For
example, clinical study measurements such as blood
pressure must always be performed at the same time,
in the same room, in the same position, with the same
instrument, and by the same person. If there are differences, for example in the investigator, measuring
instrument, analytical laboratory or recording time, it
must be established that the measurements are in agreement (10, 13).
The type of scale used for the recorded parameter is
also of decisive importance. Putting it simply, metric
scales are superior to ordinal scales, which are superior
to nominal scales. The type of scale is so important, as
both descriptive statistics and statistical test procedures
depend on it. Transformation from a higher to a lower
scale type is in principle possible, although the converse is impossible. For example, the hemoglobin
content may be determined with a metric scale (e.g. as
g/dL). It can then be transformed to an ordinal scale
(e.g. low, normal and high hemoglobin status), but not
conversely.
Calculation of sample size
Whatever the study design, a calculation must be performed before the start of the study to estimate the
necessary number of units of analysis (for example,
patients) to answer the main study question (14–16).
This requires calculation of sample size, exploiting
knowledge of the expected effect (for example, the
clinically relevant difference) and its scatter (for
example, standard deviation). These may be determined
in preliminary studies or from published information.
It is generally true that a large sample is required to
discover a small difference. The sample must also be
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
TABLE 1
Summary of important terms to validate
a measurement method
Term
Concept
Reliability
Precision
Validity
Trueness
Accuracy of the mean
Accuracy
Accuracy
Reliability and validity
large if the scatter of the outcome parameter is large in
the study groups. Sample size planning helps to ensure
that the study is large enough, but not excessively large.
The sample size is often restricted by the available
time and/or by the budget. This is not in accordance with
good scientific practice. If the sample is small, the
power will also be low, bringing the risk that real differences will not be identified (16, 17). There are both
ethical problems—stress to patients, possibly random
allocation of therapy—and economic problems—
financial, structural, and with regard to personnel—
which make it difficult to justify a study which is either too large or not large enough (16–19). The research
worker has to consider whether alternative procedures
might be possible, such as increasing the time available,
the personnel or the funding, or whether a multicenter
study should be performed in collaboration with colleagues.
Discussion
Planning, performance, documentation, analysis, and
publication are the component parts of medical studies
(1, 2). Study design is of decisive importance in planning. This not only lays down the statistical analysis,
but also ultimately the reliability of the conclusions
and the significance and implementation of the study
results (2). A six point checklist can be used for the
rapid evaluation of the study design (table 2).
According to Sackett, about two thirds of 56 typical
errors in studies are connected to errors in design and
performance (20). This cannot be corrected once the
data have been collected. This makes the study less convincing. As a consequence, the design must be precisely planned before starting the study and this must be
laid down in the study protocol. This requires a great
deal of time.
In the final analysis, studies with poor design are
unethical. Test persons (or animals) are subjected to
unnecessary stress and research capacity is wasted
(21, 22). Medical studies must consider both individual
ethics (protection of the individual) and collective
ethics (benefit for society) (22). The size of medical
studies is often too small, so that the power is also too
small (23). For this reason, a real difference—for
example, between the activity of two therapies—is
either unidentified or only described imprecisely (24).
Low power is the result if the study is too small, the
187
MEDICINE
TABLE 2
Checklist to evaluate study design
Item
Content/information
Question
to be answered
Is the question clearly defined?
Study population
Information on
– recruitment (type, area, time)
– sociodemographic information on test persons
(for example, age, sex, illness)
– inclusion and exclusion criteria
– period of follow-up observation
Research on secondary data
Research on primary data (actual trials)
– Experimental studies
– Clinical studies
– Epidemiological studies
Type of study
Technical model (for example, a prosthesis, material in
dentistry, a blood sample)
Hereditary information
Cell
Cell system
Organ (for example, heart or lung)
Organ system (for example, cardiovascular system)
Single test subject (animal or man)
Selected patient group (for example, hospital group,
risk group)
Population (for example, from a region)
Unit of observation
Measuring technique
Calculation of
sample size
Use of measuring instruments (=validation)
– Reliability
– Validity
Measurement plan
– Time points
– Number of investigators
– Standardization of measurement conditions
– Type of scale
Was the sample size calculated?
If yes,what were the conditions?
– Type of test
– Level of significance
– Power
– Clinically relevant difference
– Scatter/variance
Only adequately planned studies give results which
can be published in high quality journals. Planning
errors and inadequacies can no longer be corrected once
the study has been completed. It is therefore advisable
to consult an experienced biometrician during the
planning phase of the study (1, 16, 17, 18).
Conflict of interest statement
The authors declare that no conflict of interest exists according to the guidelines of
the International Committee of Medical Journal Editors.
Manuscript received on 30 November 2007, revised version accepted on
8 February 2008.
Translated from the original German by Rodney A. Yeates, M.A., Ph.D.
REFERENCES
1. Altman DG, Gore SM, Gardner MJ, Pocock SJ: Statistical guidelines
for contributers to medical journals. BMJ 1983; 286: 1489–93.
2. Schäfer H, Berger J, Biebler K-E et al.: Empfehlungen für die Erstellung von Studienprotokollen (Studienplänen) für klinische Studien. Informatik, Biometrie und Epidemiologie in Medizin und Biologie 1999; 30: 141–54.
3. Altman DG, Machin D, Bryant TN, Gardner MJ: Statistics with confidence. 2nd edition Bristol: BMJ Books 2000; 173.
4. DocCheck- Flexikon: Thema: Studiendesign.
http://flexikon.doccheck.com/Studiendesign.
5. Schumacher M, Schulgen G: Methodik klinischer Studien, Methodische Grundlagen der Planung, Durchführung und Auswertung.
2. Aufl., Berlin, Heidelberg, New York: Springer 2007; 1–28
6. Moher D, Schulz KF, Altman D, for the CONSORT Group: The
CONSORT Statement: Revised Recommendations for Improving
the Quality of Reports of Parallel-Group Randomized Trials. Ann
Intern Med 2001; 134 : 657–62.
7. Beaglehole R, Bonita R, Kjellström T: Einführung in die Epidemiologie. Bern: Verlag Hans Huber 1997; 53–84.
8. Fletcher RH, Fletcher SW, Wagner EH, Hearting J: Klinische Epidemiologie. Grundlagen und Anwendung. Bern: Verlag Hans Huber
2007; 1–24 und 349–78.
9. Fleiss JL: The design and analysis of clinical experiments. New
York: John Wiley & Sons 1986: 1–32.
10. Hüttner M, Schwarting U: Grundzüge der Marktforschung. 7. Aufl.,
München: Oldenburg Verlag 2002; 1–600.
difference between the study groups is too small, or
the scatter of the measurements is too great. Sterne
demands that the quality of studies should be increased
by increasing their size and increasing the precision of
measurement (25). On the other hand, if the study is
too large, unnecessarily many test persons (or animals) are exposed to stress and resources (such as personnel or financial resources) are wasted. It is
therefore necessary to evaluate the feasibility of a study
during the planning phase by calculating the sample
size. It may be necessary to take suitable measures to
ensure that the power is adequate. The excuse that there
is not enough time or money is misplaced. The power
may be increased by reducing the heterogeneity,
improving measurement precision, or by cooperation
in multicenter studies. Much more new knowledge is
won from a single accurately performed, well designed
study of adequate size than from several inadequate
studies.
188
11. Brüggemann L: Bewertung von Richtigkeit und Präzision bei Analysenverfahren, GIT Labor-Fachzeitschrift 2002; 2: 153–6.
12. Funk W, Dammann V, Donnevert G: Qualitatssicherung in der Analytischen Chemie: Anwendungen in der Umwelt-, Lebensmittelund Werkstoffanalytik, Biotechnologie und Medizintechnik. 2.
Aufl., Weinheim, New York: Wiley-VCH 2005; 1–100.
13. Lienert GA, Raatz U: Testaufbau und Testanalyse. 2. Aufl., Weinheim: Psychologie Verlags Union 1998; 220–71.
14. Altman DG: Practical Statistics for Medical research. London:
Chapman and Hall 1991; 1–9.
15. Machin D, Campbell MJ, Fayers PM, Pinol APY: Sample Size Tables
for Clinical Studies. 2. Aufl., Oxford, London, Berlin: Blackwell
Science Ltd. 1987: 296–9.
16. Eng J: Sample size estimation: how many individuals should be
studied? Radiology 2003; 227: 309–13.
17. Halpern SD, Karlawish JHT, Berlin JA: The continuing unethical
conduct of underpowered clinical trails. JAMA 2002; 288:
358–62.
18. Krummenauer F, Kauczor H-U: Fallzahlplanung in referenzkontrollierten Diagnosestudien. Fortschr Röntgenstr 2002; 174:
1438–44.
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
MEDICINE
19. Altman DG: Statistics and ethics in medical research, misuse of
statistics is unethical. BMJ 1980; 281: 1182–4.
20. Sackett DL: Bias in analytic research. J Chronic Dis 1979; 32:
51–63.
21. May WW: The composition and function of ethical committees. J
Med Ethics 1975; 1: 23–9.
22. Palmer CR: Ethics and statistical methodology in clinical trials.
JME 1993; 19: 219–22.
23. Moher D, Dulberg CS, Wells GA: Statistical power, sample size,
and their reporting in randomized controlled trials. JAMA 1994;
272: 122–4.
24. Faller, H: Signifikanz, Effektstärke und Konfidenzintervall. Rehabilitation 2004; 43: 174–8.
25. Sterne JAC, Smith GD: Sifting the evidence—what’s wrong with
significance tests? BMJ 2001; 322: 226–31.
Corresponding author
Dr. rer. nat. Bernd Röhrig
MDK Rheinland-Pfalz, Referat Rehabilitation/Biometrie
Albiger Straße 19 d
55232 Alzey, Germany
Bernd.Roehrig@mdk-rlp.de
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
189
Literature Review Summary Table
NURS 5052/NURS 6052
Name:
Citation
Type of Study
Setting
Key
Concepts/Variables
Findings
Hierarchy of
Evidence Level
Concepts:
Prevalence of
diseases across the
world has been
changing, and with
hypertension ranking
amongst the leading
causes of death for
the past few
decades.
Hypertension has been the
leading single cause of death for
the past few decades. Contrary to
the initial misconception that
developing nations are the only
ones at risk of an increase in the
mortality rate due to hypertension,
developed nations are equally
affected. This means that the
economic impacts such as
lifestyle differences, foods
consumed, and awareness levels
determine the prevalence of
hypertension.
Although intervention programs
should be established in both
developing and developed
countries, more emphasis should
be placed on developing
countries. Their risks are more
pronounced as a result of the
poor lifestyles that people lead.
Less significant.
Important for
creating a
foundation and a
global overview of
hypertension and a
comparison against
other GBDs.
Design Type
Framework/Theory
Bromfield, S., &
Muntner, P. (2013).
High Blood Pressure:
The Leading Global
Burden of Disease
Risk Factor and the
Need for Worldwide
Prevention Programs.
Current Hypertension
Reports, 15(3), 134–
136.
http://doi.org/10.1007/s
11906-013-0340-9
Global
Type of Study:
Secondary
Design Type:
Report
Framework/Theory:
Hypertension is, and
has over time been,
the leading cause of
death around the
world. However, its
prevalence could be
caused by economic
gaps and other risk
factors such as age.
Independent
Variable:
Global burden of
disease (GBD).
Dependent Variable:
Mortality rate
Controlled Variable:
Economic factors,
age.
Although differences are
identified in the prevalence of
hypertension amongst both
developing and developed
nations, it may be necessary to
conduct studies that will lead to
customized prevention, treatment,
and control based on the
economy of a region.
© 2016 Laureate Education Inc.
1
Literature Review Summary Table
NURS 6052
Lackland, D. T. (2014).
Racial Differences in
Hypertension:
Implications for High
Blood Pressure
Management. The
American Journal of
the Medical Sciences,
348(2), 135–138.
http://doi.org/10.1097/
MAJ.000000000000030
8
Type of Study:
Secondary
Design Type:
Integrative review
Framework/Theory:
Racial disparities
are common in both
the prevalence of
diseases and
mortalities. In
hypertension, there
are various factors
associated with
racial disparities,
and that may put
some of the races at
a higher risk than
others.
The United
States.
Concepts:
Blood pressure
amongst African
Americans has been
high than in
Caucasians.
Lifestyle differs
across races in the
US.
Independent
Variable:
Prevalence of
hypertension
Dependent Variable:
Race
Controlled Variable:
Age, comorbidities.
Clinical guidelines have shown a
high prevalence of hypertension
amongst the African Americans.
Other races such as the
Caucasians face relatively lower
risks of the same, which is
evident in both mortalities, ratio of
diagnosis, and associated
comorbidities. The lifestyles and
factors associated with
hypertension also differ from one
race to another to another.
The lifestyles of different cultures
and groups needs to be
considered when developing
intervention and management
programs that should help in
addressing the hypertension due
their high influence on its
prevalence.
There is insufficient evidence in
studies to justify the differences in
factors associated with racial
disparities as well as the
differences in prevalence. This
calls for focused research based
on these gaps.
Strong evidence.
Analyzes tens of
primary studies.
Literature Review Summary Table
NURS 6052
Citation
Study
Design Type
Framework/Theory
Diaz, K. M., & Shimbo,
D. (2013). Physical
Activity and the
Prevention of
Hypertension. Current
Hypertension Reports,
15(6), 659–668.
http://doi.org/10.1007/s
11906-013-0386-8
Type of Study:
Secondary research
Setting
Global
Design Type:
Integrative review
Framework/Theory:
Prevalence has
been on the
increase and
prevention is
becoming
increasingly
significant.
Key
Concepts/Variables
Findings
Hierarchy of
Evidence Level
Concepts:
Lifestyle could be key
to achieving a
success in improving
prevention of
hypertension.
Many studies also indicate that
different people may respond
differently to various physical
activities based on their body
processes. However, all existing
literature and studies strongly
support a positive relationship
between physical activity and the
prevalence of hypertension.
Some physical activities may be
more successful than others in
the prevention and management
of hypertension.
Physical activity thus needs to be
well developed alongside other
lifestyles such as food
consumption and social lives.
Very strong
evidence. The
study explores
tens of primary
studies and
analyzes their
findings to derive
a more reliable
conclusion.
Independent
Variable:
Prevalence of
hypertension.
Dependent Variable:
Physical activity.
Controlled Variable:
Gender, age, other
risk factors.
Yang, M. H., Kang, S.
Y., Lee, J. A., Kim, Y.
S., Sung, E. J., Lee, K.Y., … Lee, S. Y. (2017).
The Effect of Lifestyle
Changes on Blood
Pressure Control
among Hypertensive
Patients. Korean
Journal of Family
Medicine, 38(4), 173–
180.
http://doi.org/10.4082/k
Type of Study:
Primary research
Design Type:
Survey
Framework/Theory:
Hypertension is
common for patients
that occasionally
seek primary care.
However, lifestyle
behaviors could be
Korea
Concepts:
Hypertension is a
lifestyle disease.
Lifestyle
modifications may be
necessary during the
period of intervention.
Independent
Variable:
Hypertension control
Dependent Variable:
Gaps thus appear on the
prescription of physical activities
on at-risk individuals and
hypertension patients, which call
for further studies.
Poor physical activity led to
weight gain. Consuming high
amounts of salt, weight gain, and
inadequate physical activity
created more risks of getting
hypertension and led to increased
period of recovery. Proper
lifestyle balance reduced the
need for medication amongst
many patients. Physicians should
thus modify the lifestyles of
patients so as to achieve better
results.
The evidence is
highly reliable.
This study will
form a critical
part of the
research.
Literature Review Summary Table
NURS 6052
jfm.2017.38.4.173
Patients’ lifestyles
(increased salt intake
and physical activity)
integrated into their
care so as to help
them achieve better
results.
Controlled Variable:
Age, sex
Babaee Beigi, M. A.,
Zibaeenezhad, M. J.,
Aghasadeghi, K.,
Jokar, A.,
Shekarforoush, S., &
Khazraei, H. (2014).
The Effect of
Educational Programs
on Hypertension
Management.
International
Cardiovascular
Research Journal, 8(3),
94–98.
Type of Study:
Primary
Design Type:
Quasi-experimental
study
Framework/Theory:
Hy
REVIEW ARTICLE
Study Design in Medical Research
Part 2 of a Series on the Evaluation of Scientific Publications
Bernd Röhrig, Jean-Baptist du Prel, Maria Blettner
SUMMARY
Background: The scientific value and informativeness of
a medical study are determined to a major extent by the
study design. Errors in study design cannot be corrected
afterwards. Various aspects of study design are discussed
in this article.
Methods: Six essential considerations in the planning and
evaluation of medical research studies are presented and
discussed in the light of selected scientific articles from
the international literature as well as the authors’ own
scientific expertise with regard to study design.
Results: The six main considerations for study design are
the question to be answered, the study population, the unit
of analysis, the type of study, the measuring technique, and
the calculation of sample size.
Conclusions: This article is intended to give the reader
guidance in evaluating the design of studies in medical
research. This should enable the reader to categorize
medical studies better and to assess their scientific quality
more accurately.
Dtsch Arztebl Int 2009; 106(11): 184–9
DOI: 10.3238/arztebl.2009.0184
Key words: study design, quality, study, study type,
measuring technique
M
edical research studies can be split into five
phases—planning, performance, documentation, analysis, and publication (1, 2). Aside from financial, organizational, logistical and personnel questions,
scientific study design is the most important aspect of
study planning. The significance of study design for
subsequent quality, the relability of the conclusions,
and the ability to publish a study are often underestimated
(1). Long before the volunteers are recruited, the study
design has set the points for fulfilling the study objectives. In contrast to errors in the statistical evaluation,
errors in design cannot be corrected after the study has
been completed. This is why the study design must be
laid down carefully before starting and specified in the
study protocol.
The term “study design” is not used consistently in
the scientific literature. The term is often restricted to
the use of a suitable type of study. However, the term
can also mean the overall plan for all procedures involved in the study. If a study is properly planned, the
factors which distort or bias the result of a test procedure
can be minimized (3, 4). We will use the term in a
comprehensive sense in the present article. This will
deal with the following six aspects of study design:
the question to be answered, the study population, the
type of study, the unit of analysis, the measuring technique, and the calculation of sample size—, on the
basis of selected articles from the international literature and our own expertise. This is intended to help
the reader to classify and evaluate the results in publications. Those who plan to perform their own studies
must occupy themselves intensively with the issue of
study design.
Question to be answered
Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI),
Johannes Gutenberg-Universität Mainz: Dr. rer. nat. Röhrig, Prof. Dr. rer. nat.
Maria Blettner
Zentrum Präventive Pädiatrie, Zentrum für Kinder- und Jugendmedizin,
Johannes Gutenberg-Universität Mainz: Dr. med. du Prel, M.P.H
184
The question to be answered by the research is of
decisive importance for study planning. The research
worker must be clear about the objectives. He must
think very carefully about the question(s) to be
answered by the study. This question must be operationalized, meaning that it must be converted into a
measurable and evaluable form. This demands an
adequate design and suitable measurement parameters.
A distinction must be made between the main questions
to be answered and secondary questions. The result of
the study should be that open questions are answered
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
MEDICINE
and possibly that new hypotheses are generated. The
following questions are important: Why? Who?
What? How? When? Where? How many? The question
to be answered also implies the target group and
should therefore be very precisely formulated. For example, the question should not be “What is the quality
of life?”, but must specify the group of patients (e.g.
age), the area (e.g. Germany), the disease (e.g. mammary carcinoma), the condition (e.g. tumor stage 3),
perhaps also the intervention (e.g. after surgery), and
what endpoint (in this case, quality of life) is to be determined with which method (e.g. the EORTC QLQC30 questionnaire) at what point in time. Scientific
questions are often not only purely descriptive, but also
include comparisons, for example, between two
groups, or before and after the intervention. For example,
it may be interesting to compare the quality of life of
breast cancer patients with women of the same age
without cancer.
The research worker specifies the question to be answered, and whether the study is to be evaluated in a
descriptive, exploratory or confirmatory manner.
Whereas in a descriptive study the units of analysis
are to be described by the recorded variables (e.g.
blood parameters or diagnosis), the aim in an exploratory analysis is to recognize connections between
variables, to evaluate these and to formulate new
hypotheses. On the other hand, confirmatory analyses
are planned to provide statistical proofs by testing
specified study hypotheses.
The question to be answered also determines the type
and extent of the data to be recorded. This specifies
which data are to be recorded at which point in time.
In this case, less is often more. Data irrelevant to the
question(s) to be answered should not be collected for
the moment. If too many variables are recorded at too
many time points, this can lead to low participation
rates, high dropout rates, and poor compliance from
the volunteers. The experience is then that not all data
are evaluated.
The question to be answered and the strategy for
evaluation must be specified in the study protocol before
the study is started.
Study population
The question to be answered by the study implies that
there is a target group for whom this is to be clarified.
Nevertheless, the research worker is not primarily
interested in the observed study population, but in
whether the results can be transferred to the target
population. Accordingly, statistical test procedures
must be used to generalize the results from the sample
for the whole population (figure 1).
The sample can be highly representative of the
study population if it is properly selected. This can be
attained with defined and selective inclusion and
exclusion criteria, such as sex, age, and tumor stage.
Study participants may be selected randomly, for
example, by random selection through the residents’
registration office, or consecutively, for example,
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
FIGURE 1
Connection between
overall population
and study
population/data
all patients in a clinical department in the course of
one year.
With a selective sample, a statement can only be
made about a population corresponding to these selection criteria. The possibility of generalizing the results
may, for example, be greatly influenced by whether
the patients come from a specialist practice, a specialized hospital department or from several different
practices.
The possibility of generalization may also be influenced by the decision to perform the study at a single
institution or site, or at several (multicenter study).
The advantages of a multicenter study are that the
required number of patients can be reached within a
shorter period and that the results can more readily be
generalized, as they are from different treatment centers.
This raises the external validity.
Type of study
Before the study type is specified, the research worker
must be clear about the category of research. There is
a distinction in principle between research on primary
data and research on secondary data.
Research on primary data means performing the
actual scientific studies, recording the primary study
data. This is intended to answer scientific questions
and to gain new knowledge.
In contrast, research on secondary results involves
the analysis of studies which have already been performed and published. This may include (renewed)
analysis of recorded data, perhaps from a register,
from population statistics, or from studies. Another
objective may be to win a comprehensive overview of
the current state of research and to come to appropriate
conclusions. In secondary data research, a distinction
is made between narrative reviews, systematic reviews,
and meta-analyses.
The underlying question to be answered also influences the selection of the type of study. In primary
research, experimental, clinical and epidemiological
research are distinguished.
Experimental research includes applied studies,
such as animal experiments, cell studies, biochemical
and physiological investigations, and studies on
185
MEDICINE
Portrayal of the
terms reliability
(precision) and
validity (trueness)
using a target
FIGURE 2
material properties, as well as the development of analytical and biometric procedures.
Clinical research includes interventional and noninterventional studies. The objective of interventional
clinical studies (clinical trials) is “to study or demonstrate the clinical or pharmacological activities of
drugs” and “to provide convincing evidence of the
safety or efficacy of drugs” (AMG, German Drugs Act
§4) (5). In clinical studies, patients are randomly
assigned to treatment groups. In contrast, noninterventional clinical studies are observational studies, in
which patients are given an individually specified
treatment (6, 7).
Epidemiological research studies the distribution
and changes with time of the frequency of diseases
and of their causes. Experimental studies are distinguished from observational studies (7, 8). Interventional
studies (such as vaccination, addition of food
additives, fluoride addition to drinking water) are of
experimental character. Examples of observational
epidemiological studies include cohort studies, case
control studies, cross-sectional studies, and ecological
studies.
A subsequent article will discuss the different study
types in detail.
Unit of analysis
The unit of analysis (investigational unit) must be
specified before starting a medical study. In a typical
clinical study, the patient is the unit of analysis.
However, the unit of analysis may also be a technical
model, hereditary information, a cell, a cellular structure,
an organ, an organ system, a single test individual
(animal or man), or specified subgroup or the population
186
of a region or of a country. In systematic reviews, the
unit of analysis is a single study. The sample then
includes the total of all units of analysis. The interesting
information or data (observations, variables,
characteristics) are recorded for the statistical units.
For example, if the heart is being investigated in a patient (the unit of analysis), the heart rate may be measured as a characteristic of performance.
The selection of the unit of analysis influences the
interpretation of the study results. It is therefore
important for statistical reasons to know whether the
units of analysis are dependent or independent of each
other with respect to the outcome parameter. This
distinction is not always easy. For example, if the
teeth of test persons are the unit of analysis, it must be
clarified whether these are independent with respect
to the question to be answered (i.e. from different test
persons) or dependent (i.e. from the same test person).
Teeth in the mouth of a single test person are generally
dependent, as specific factors, such as nutrition and
teeth cleaning habits, act on all teeth in the mouth in
the same way. On the other hand, extracted teeth are
generally independent study objects, as there are no
longer any shared factors which influence them. This
is particularly the case when the teeth are subject to
additional preparation, for example, cutting or grinding. On the other hand, if the observations are on tooth
characteristics developed before extraction, these
characteristics must be regarded as dependent.
Measuring technique
The term “measuring technique” includes the use of
measuring instruments and the method of measurement.
Use of measuring instruments
Measuring instruments include instruments which
specifically record measuring data (such as blood
pressure or laboratory parameters), as well as data
collection with standardized or self-designed questionnaires (for example, quality of life, depression, or
satisfaction).
During the validation of a measuring instrument, its
quality and practicability are evaluated using statistical parameters. Unfortunately, the nomenclature is not
fully standardized and also depends on the special area
(for example, chemical analysis, psychological studies
with questionnaires, or diagnostic studies). It is
always the case that a measuring instrument of high
quality should be of high precision and validity.
Precision describes the extent to which a measuring
technique consistently provides the same results if the
measurement is repeated (9). The reliability (or precision) provides information on the precision or the
occurrence of random errors. If the precision is low,
the correlation coefficients are low, measurements are
imprecise and a larger sample size is needed (9). On
the other hand, the validity (accuracy of the mean or
trueness) of a measuring instrument is high if it measures exactly what it is supposed to measure. Thus the
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
MEDICINE
validity provides information on the occurrence of
systematic errors (10). Whereas the precision describes
the difference (variance) between repeated measurements, the validity reflects the difference between the
measured and true parameter (10). Figure 2 portrays
the terms, using a target as a model.
Reliability and validity are subsumed in the term
accuracy (11, 12). The accuracy is only high when
both the precision and the validity are high. Table 1
summarizes the important terms to validate a measurement method.
The problem is not only that the measurements may
be invalid or false, but also that the measurements
may lead to erroneous conclusions. External and internal validity can be distinguished (13). External validity
means the possibility of generalizing the study results
for the study population to the target population. The internal validity is the validity of a result for the actual
question to be answered. This can be optimized by
detailed planning, defined inclusion and exclusion
criteria, and reduction of external interfering factors.
Measurement plan
The measurement plan describes the number and time
points of the measurements to be performed. To obtain
comparable and objective measurements, the
measurement conditions must be standardized. For
example, clinical study measurements such as blood
pressure must always be performed at the same time,
in the same room, in the same position, with the same
instrument, and by the same person. If there are differences, for example in the investigator, measuring
instrument, analytical laboratory or recording time, it
must be established that the measurements are in agreement (10, 13).
The type of scale used for the recorded parameter is
also of decisive importance. Putting it simply, metric
scales are superior to ordinal scales, which are superior
to nominal scales. The type of scale is so important, as
both descriptive statistics and statistical test procedures
depend on it. Transformation from a higher to a lower
scale type is in principle possible, although the converse is impossible. For example, the hemoglobin
content may be determined with a metric scale (e.g. as
g/dL). It can then be transformed to an ordinal scale
(e.g. low, normal and high hemoglobin status), but not
conversely.
Calculation of sample size
Whatever the study design, a calculation must be performed before the start of the study to estimate the
necessary number of units of analysis (for example,
patients) to answer the main study question (14–16).
This requires calculation of sample size, exploiting
knowledge of the expected effect (for example, the
clinically relevant difference) and its scatter (for
example, standard deviation). These may be determined
in preliminary studies or from published information.
It is generally true that a large sample is required to
discover a small difference. The sample must also be
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
TABLE 1
Summary of important terms to validate
a measurement method
Term
Concept
Reliability
Precision
Validity
Trueness
Accuracy of the mean
Accuracy
Accuracy
Reliability and validity
large if the scatter of the outcome parameter is large in
the study groups. Sample size planning helps to ensure
that the study is large enough, but not excessively large.
The sample size is often restricted by the available
time and/or by the budget. This is not in accordance with
good scientific practice. If the sample is small, the
power will also be low, bringing the risk that real differences will not be identified (16, 17). There are both
ethical problems—stress to patients, possibly random
allocation of therapy—and economic problems—
financial, structural, and with regard to personnel—
which make it difficult to justify a study which is either too large or not large enough (16–19). The research
worker has to consider whether alternative procedures
might be possible, such as increasing the time available,
the personnel or the funding, or whether a multicenter
study should be performed in collaboration with colleagues.
Discussion
Planning, performance, documentation, analysis, and
publication are the component parts of medical studies
(1, 2). Study design is of decisive importance in planning. This not only lays down the statistical analysis,
but also ultimately the reliability of the conclusions
and the significance and implementation of the study
results (2). A six point checklist can be used for the
rapid evaluation of the study design (table 2).
According to Sackett, about two thirds of 56 typical
errors in studies are connected to errors in design and
performance (20). This cannot be corrected once the
data have been collected. This makes the study less convincing. As a consequence, the design must be precisely planned before starting the study and this must be
laid down in the study protocol. This requires a great
deal of time.
In the final analysis, studies with poor design are
unethical. Test persons (or animals) are subjected to
unnecessary stress and research capacity is wasted
(21, 22). Medical studies must consider both individual
ethics (protection of the individual) and collective
ethics (benefit for society) (22). The size of medical
studies is often too small, so that the power is also too
small (23). For this reason, a real difference—for
example, between the activity of two therapies—is
either unidentified or only described imprecisely (24).
Low power is the result if the study is too small, the
187
MEDICINE
TABLE 2
Checklist to evaluate study design
Item
Content/information
Question
to be answered
Is the question clearly defined?
Study population
Information on
– recruitment (type, area, time)
– sociodemographic information on test persons
(for example, age, sex, illness)
– inclusion and exclusion criteria
– period of follow-up observation
Research on secondary data
Research on primary data (actual trials)
– Experimental studies
– Clinical studies
– Epidemiological studies
Type of study
Technical model (for example, a prosthesis, material in
dentistry, a blood sample)
Hereditary information
Cell
Cell system
Organ (for example, heart or lung)
Organ system (for example, cardiovascular system)
Single test subject (animal or man)
Selected patient group (for example, hospital group,
risk group)
Population (for example, from a region)
Unit of observation
Measuring technique
Calculation of
sample size
Use of measuring instruments (=validation)
– Reliability
– Validity
Measurement plan
– Time points
– Number of investigators
– Standardization of measurement conditions
– Type of scale
Was the sample size calculated?
If yes,what were the conditions?
– Type of test
– Level of significance
– Power
– Clinically relevant difference
– Scatter/variance
Only adequately planned studies give results which
can be published in high quality journals. Planning
errors and inadequacies can no longer be corrected once
the study has been completed. It is therefore advisable
to consult an experienced biometrician during the
planning phase of the study (1, 16, 17, 18).
Conflict of interest statement
The authors declare that no conflict of interest exists according to the guidelines of
the International Committee of Medical Journal Editors.
Manuscript received on 30 November 2007, revised version accepted on
8 February 2008.
Translated from the original German by Rodney A. Yeates, M.A., Ph.D.
REFERENCES
1. Altman DG, Gore SM, Gardner MJ, Pocock SJ: Statistical guidelines
for contributers to medical journals. BMJ 1983; 286: 1489–93.
2. Schäfer H, Berger J, Biebler K-E et al.: Empfehlungen für die Erstellung von Studienprotokollen (Studienplänen) für klinische Studien. Informatik, Biometrie und Epidemiologie in Medizin und Biologie 1999; 30: 141–54.
3. Altman DG, Machin D, Bryant TN, Gardner MJ: Statistics with confidence. 2nd edition Bristol: BMJ Books 2000; 173.
4. DocCheck- Flexikon: Thema: Studiendesign.
http://flexikon.doccheck.com/Studiendesign.
5. Schumacher M, Schulgen G: Methodik klinischer Studien, Methodische Grundlagen der Planung, Durchführung und Auswertung.
2. Aufl., Berlin, Heidelberg, New York: Springer 2007; 1–28
6. Moher D, Schulz KF, Altman D, for the CONSORT Group: The
CONSORT Statement: Revised Recommendations for Improving
the Quality of Reports of Parallel-Group Randomized Trials. Ann
Intern Med 2001; 134 : 657–62.
7. Beaglehole R, Bonita R, Kjellström T: Einführung in die Epidemiologie. Bern: Verlag Hans Huber 1997; 53–84.
8. Fletcher RH, Fletcher SW, Wagner EH, Hearting J: Klinische Epidemiologie. Grundlagen und Anwendung. Bern: Verlag Hans Huber
2007; 1–24 und 349–78.
9. Fleiss JL: The design and analysis of clinical experiments. New
York: John Wiley & Sons 1986: 1–32.
10. Hüttner M, Schwarting U: Grundzüge der Marktforschung. 7. Aufl.,
München: Oldenburg Verlag 2002; 1–600.
difference between the study groups is too small, or
the scatter of the measurements is too great. Sterne
demands that the quality of studies should be increased
by increasing their size and increasing the precision of
measurement (25). On the other hand, if the study is
too large, unnecessarily many test persons (or animals) are exposed to stress and resources (such as personnel or financial resources) are wasted. It is
therefore necessary to evaluate the feasibility of a study
during the planning phase by calculating the sample
size. It may be necessary to take suitable measures to
ensure that the power is adequate. The excuse that there
is not enough time or money is misplaced. The power
may be increased by reducing the heterogeneity,
improving measurement precision, or by cooperation
in multicenter studies. Much more new knowledge is
won from a single accurately performed, well designed
study of adequate size than from several inadequate
studies.
188
11. Brüggemann L: Bewertung von Richtigkeit und Präzision bei Analysenverfahren, GIT Labor-Fachzeitschrift 2002; 2: 153–6.
12. Funk W, Dammann V, Donnevert G: Qualitatssicherung in der Analytischen Chemie: Anwendungen in der Umwelt-, Lebensmittelund Werkstoffanalytik, Biotechnologie und Medizintechnik. 2.
Aufl., Weinheim, New York: Wiley-VCH 2005; 1–100.
13. Lienert GA, Raatz U: Testaufbau und Testanalyse. 2. Aufl., Weinheim: Psychologie Verlags Union 1998; 220–71.
14. Altman DG: Practical Statistics for Medical research. London:
Chapman and Hall 1991; 1–9.
15. Machin D, Campbell MJ, Fayers PM, Pinol APY: Sample Size Tables
for Clinical Studies. 2. Aufl., Oxford, London, Berlin: Blackwell
Science Ltd. 1987: 296–9.
16. Eng J: Sample size estimation: how many individuals should be
studied? Radiology 2003; 227: 309–13.
17. Halpern SD, Karlawish JHT, Berlin JA: The continuing unethical
conduct of underpowered clinical trails. JAMA 2002; 288:
358–62.
18. Krummenauer F, Kauczor H-U: Fallzahlplanung in referenzkontrollierten Diagnosestudien. Fortschr Röntgenstr 2002; 174:
1438–44.
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
MEDICINE
19. Altman DG: Statistics and ethics in medical research, misuse of
statistics is unethical. BMJ 1980; 281: 1182–4.
20. Sackett DL: Bias in analytic research. J Chronic Dis 1979; 32:
51–63.
21. May WW: The composition and function of ethical committees. J
Med Ethics 1975; 1: 23–9.
22. Palmer CR: Ethics and statistical methodology in clinical trials.
JME 1993; 19: 219–22.
23. Moher D, Dulberg CS, Wells GA: Statistical power, sample size,
and their reporting in randomized controlled trials. JAMA 1994;
272: 122–4.
24. Faller, H: Signifikanz, Effektstärke und Konfidenzintervall. Rehabilitation 2004; 43: 174–8.
25. Sterne JAC, Smith GD: Sifting the evidence—what’s wrong with
significance tests? BMJ 2001; 322: 226–31.
Corresponding author
Dr. rer. nat. Bernd Röhrig
MDK Rheinland-Pfalz, Referat Rehabilitation/Biometrie
Albiger Straße 19 d
55232 Alzey, Germany
Bernd.Roehrig@mdk-rlp.de
⏐ Dtsch Arztebl Int 2009; 106(11): 184–9
Deutsches Ärzteblatt International⏐
189
Literature Review Summary Table
NURS 5052/NURS 6052
Name:
Citation
Type of Study
Setting
Key
Concepts/Variables
Findings
Hierarchy of
Evidence Level
Concepts:
Prevalence of
diseases across the
world has been
changing, and with
hypertension ranking
amongst the leading
causes of death for
the past few
decades.
Hypertension has been the
leading single cause of death for
the past few decades. Contrary to
the initial misconception that
developing nations are the only
ones at risk of an increase in the
mortality rate due to hypertension,
developed nations are equally
affected. This means that the
economic impacts such as
lifestyle differences, foods
consumed, and awareness levels
determine the prevalence of
hypertension.
Although intervention programs
should be established in both
developing and developed
countries, more emphasis should
be placed on developing
countries. Their risks are more
pronounced as a result of the
poor lifestyles that people lead.
Less significant.
Important for
creating a
foundation and a
global overview of
hypertension and a
comparison against
other GBDs.
Design Type
Framework/Theory
Bromfield, S., &
Muntner, P. (2013).
High Blood Pressure:
The Leading Global
Burden of Disease
Risk Factor and the
Need for Worldwide
Prevention Programs.
Current Hypertension
Reports, 15(3), 134–
136.
http://doi.org/10.1007/s
11906-013-0340-9
Global
Type of Study:
Secondary
Design Type:
Report
Framework/Theory:
Hypertension is, and
has over time been,
the leading cause of
death around the
world. However, its
prevalence could be
caused by economic
gaps and other risk
factors such as age.
Independent
Variable:
Global burden of
disease (GBD).
Dependent Variable:
Mortality rate
Controlled Variable:
Economic factors,
age.
Although differences are
identified in the prevalence of
hypertension amongst both
developing and developed
nations, it may be necessary to
conduct studies that will lead to
customized prevention, treatment,
and control based on the
economy of a region.
© 2016 Laureate Education Inc.
1
Literature Review Summary Table
NURS 6052
Lackland, D. T. (2014).
Racial Differences in
Hypertension:
Implications for High
Blood Pressure
Management. The
American Journal of
the Medical Sciences,
348(2), 135–138.
http://doi.org/10.1097/
MAJ.000000000000030
8
Type of Study:
Secondary
Design Type:
Integrative review
Framework/Theory:
Racial disparities
are common in both
the prevalence of
diseases and
mortalities. In
hypertension, there
are various factors
associated with
racial disparities,
and that may put
some of the races at
a higher risk than
others.
The United
States.
Concepts:
Blood pressure
amongst African
Americans has been
high than in
Caucasians.
Lifestyle differs
across races in the
US.
Independent
Variable:
Prevalence of
hypertension
Dependent Variable:
Race
Controlled Variable:
Age, comorbidities.
Clinical guidelines have shown a
high prevalence of hypertension
amongst the African Americans.
Other races such as the
Caucasians face relatively lower
risks of the same, which is
evident in both mortalities, ratio of
diagnosis, and associated
comorbidities. The lifestyles and
factors associated with
hypertension also differ from one
race to another to another.
The lifestyles of different cultures
and groups needs to be
considered when developing
intervention and management
programs that should help in
addressing the hypertension due
their high influence on its
prevalence.
There is insufficient evidence in
studies to justify the differences in
factors associated with racial
disparities as well as the
differences in prevalence. This
calls for focused research based
on these gaps.
Strong evidence.
Analyzes tens of
primary studies.
Literature Review Summary Table
NURS 6052
Citation
Study
Design Type
Framework/Theory
Diaz, K. M., & Shimbo,
D. (2013). Physical
Activity and the
Prevention of
Hypertension. Current
Hypertension Reports,
15(6), 659–668.
http://doi.org/10.1007/s
11906-013-0386-8
Type of Study:
Secondary research
Setting
Global
Design Type:
Integrative review
Framework/Theory:
Prevalence has
been on the
increase and
prevention is
becoming
increasingly
significant.
Key
Concepts/Variables
Findings
Hierarchy of
Evidence Level
Concepts:
Lifestyle could be key
to achieving a
success in improving
prevention of
hypertension.
Many studies also indicate that
different people may respond
differently to various physical
activities based on their body
processes. However, all existing
literature and studies strongly
support a positive relationship
between physical activity and the
prevalence of hypertension.
Some physical activities may be
more successful than others in
the prevention and management
of hypertension.
Physical activity thus needs to be
well developed alongside other
lifestyles such as food
consumption and social lives.
Very strong
evidence. The
study explores
tens of primary
studies and
analyzes their
findings to derive
a more reliable
conclusion.
Independent
Variable:
Prevalence of
hypertension.
Dependent Variable:
Physical activity.
Controlled Variable:
Gender, age, other
risk factors.
Yang, M. H., Kang, S.
Y., Lee, J. A., Kim, Y.
S., Sung, E. J., Lee, K.Y., … Lee, S. Y. (2017).
The Effect of Lifestyle
Changes on Blood
Pressure Control
among Hypertensive
Patients. Korean
Journal of Family
Medicine, 38(4), 173–
180.
http://doi.org/10.4082/k
Type of Study:
Primary research
Design Type:
Survey
Framework/Theory:
Hypertension is
common for patients
that occasionally
seek primary care.
However, lifestyle
behaviors could be
Korea
Concepts:
Hypertension is a
lifestyle disease.
Lifestyle
modifications may be
necessary during the
period of intervention.
Independent
Variable:
Hypertension control
Dependent Variable:
Gaps thus appear on the
prescription of physical activities
on at-risk individuals and
hypertension patients, which call
for further studies.
Poor physical activity led to
weight gain. Consuming high
amounts of salt, weight gain, and
inadequate physical activity
created more risks of getting
hypertension and led to increased
period of recovery. Proper
lifestyle balance reduced the
need for medication amongst
many patients. Physicians should
thus modify the lifestyles of
patients so as to achieve better
results.
The evidence is
highly reliable.
This study will
form a critical
part of the
research.
Literature Review Summary Table
NURS 6052
jfm.2017.38.4.173
Patients’ lifestyles
(increased salt intake
and physical activity)
integrated into their
care so as to help
them achieve better
results.
Controlled Variable:
Age, sex
Babaee Beigi, M. A.,
Zibaeenezhad, M. J.,
Aghasadeghi, K.,
Jokar, A.,
Shekarforoush, S., &
Khazraei, H. (2014).
The Effect of
Educational Programs
on Hypertension
Management.
International
Cardiovascular
Research Journal, 8(3),
94–98.
Type of Study:
Primary
Design Type:
Quasi-experimental
study
Framework/Theory:
Hy
