6052 Discussion 10

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?

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.

184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9

M E D I C I N E

M edical research studies can be split into fivephases—planning, performance, documenta-
tion, analysis, and publication (1, 2). Aside from finan-
cial, 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 objec-
tives. 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 in-
volved 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 tech-
nique, and the calculation of sample size—, on the
basis of selected articles from the international litera-
ture and our own expertise. This is intended to help
the reader to classify and evaluate the results in publi-
cations. Those who plan to perform their own studies
must occupy themselves intensively with the issue of
study design.

Question to be answered
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 opera-
tionalized, 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

R E V I E W A RT I C L E

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

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

Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9 185

M E D I C I N E

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 ex-
ample, 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. mam-
mary 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 de-
termined with which method (e.g. the EORTC QLQ-
C30 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 an-
swered, 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 explor-
atory 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,

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 selec-
tion criteria. The possibility of generalizing the results
may, for example, be greatly influenced by whether
the patients come from a specialist practice, a special-
ized hospital department or from several different
practices.

The possibility of generalization may also be influ-
enced 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 per-
formed 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 influ-
ences 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

FIGURE 1 Connection between
overall population
and study
population/data

186 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9

M E D I C I N E

material properties, as well as the development of ana-
lytical and biometric procedures.

Clinical research includes interventional and non-
interventional studies. The objective of interventional
clinical studies (clinical trials) is “to study or demon-
strate 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, noninter-
ventional 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 distin-
guished 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

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 pa-
tient (the unit of analysis), the heart rate may be mea-
sured 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 grind-
ing. 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 measure-
ment.

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 question-
naires (for example, quality of life, depression, or
satisfaction).

During the validation of a measuring instrument, its
quality and practicability are evaluated using statis-
tical 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 preci-
sion) 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 mea-
sures exactly what it is supposed to measure. Thus the

FIGURE 2Portrayal of the
terms reliability
(precision) and

validity (trueness)
using a target

Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9 187

M E D I C I N E

validity provides information on the occurrence of
systematic errors (10). Whereas the precision describes
the difference (variance) between repeated measure-
ments, 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 mea-
surement 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 inter-
nal validity can be distinguished (13). External validity
means the possibility of generalizing the study results
for the study population to the target population. The in-
ternal 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 differ-
ences, for example in the investigator, measuring
instrument, analytical laboratory or recording time, it
must be established that the measurements are in agree-
ment (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 con-
verse 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 per-
formed 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

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 dif-
ferences 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 eit-
her 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 col-
leagues.

Discussion
Planning, performance, documentation, analysis, and
publication are the component parts of medical studies
(1, 2). Study design is of decisive importance in plan-
ning. 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 con-
vincing. As a consequence, the design must be precise-
ly 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

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

188 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9

M E D I C I N E

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 ani-
mals) are exposed to stress and resources (such as per-
sonnel 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.

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.

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dien. Informatik, Biometrie und Epidemiologie in Medizin und Bio-
logie 1999; 30: 141–54.

3. Altman DG, Machin D, Bryant TN, Gardner MJ: Statistics with con-
fidence. 2nd edition Bristol: BMJ Books 2000; 173.

4. DocCheck- Flexikon: Thema: Studiendesign.
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dische Grundlagen der Planung, Durchführung und Auswertung.
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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 Epidemio-
logie. Bern: Verlag Hans Huber 1997; 53–84.

8. Fletcher RH, Fletcher SW, Wagner EH, Hearting J: Klinische Epide-
miologie. 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.

11. Brüggemann L: Bewertung von Richtigkeit und Präzision bei Ana-
lysenverfahren, GIT Labor-Fachzeitschrift 2002; 2: 153–6.

12. Funk W, Dammann V, Donnevert G: Qualitatssicherung in der Ana-
lytischen Chemie: Anwendungen in der Umwelt-, Lebensmittel-
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13. Lienert GA, Raatz U: Testaufbau und Testanalyse. 2. Aufl., Wein-
heim: Psychologie Verlags Union 1998; 220–71.

14. Altman DG: Practical Statistics for Medical research. London:
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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
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lierten Diagnosestudien. Fortschr Röntgenstr 2002; 174:
1438–44.

TABLE 2

Checklist to evaluate study design

Item Content/information

Question � Is the question clearly defined?
to be answered

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

Type of study � Research on secondary data
� Research on primary data (actual trials)

– Experimental studies
– Clinical studies
– Epidemiological studies

Unit of observation � 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)

Measuring technique � Use of measuring instruments (=validation)
– Reliability
– Validity

� Measurement plan
– Time points
– Number of investigators
– Standardization of measurement conditions
– Type of scale

Calculation of � Was the sample size calculated?
sample size � If yes,what were the conditions?

– Type of test
– Level of significance
– Power
– Clinically relevant difference
– Scatter/variance

Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9 189

M E D I C I N E

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. Reha-
bilitation 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

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