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CHAPTER 1
Planning Studies: From
Design to Publication
1
(A
few statistical terms commonly appearing in medical articles appear in this chapter
without having been previously defined. In case a reader encounters an unfamiliar
one, a glossary at the chapter’s end provides interim definitions pending formal defini-
tions later in this book.)
1.1 ORGANIZING A STUDY
A study must be organized sooner or later. Planning in advance from an over-
view down to details increases efficiency, reduces false starts, reduces errors,
and shortens the time spent. The lazy and least stressful way to do it, in the
sense of least work overall, is thorough planning up front.
1.2 STAGES OF SCIENTIFIC KNOWLEDGE
Stages
We gather data because we want to know something. These data are useful
only if they provide information about what we want to know. A scientist usu-
ally seeks to develop knowledge in three stages. The first stage is to
describe
a
class of scientific events. The second stage is to
explain
these events. The third
stage is to
predict
the occurrence of these events. The ability to predict an event
implies some level of understanding of the rule of nature governing the event.
The ability to predict outcomes of actions allows the scientist to make better
decisions about such actions. At best, a general scientific rule may be inferred
from repeated events of this type. Following is a brief explanation of the three
stages of gathering knowledge.
THE CAUSATIVE PROCESS IS OF INTEREST, NOT THE DATA
A process, or set of forces, generates data related to an event. It is this process,
not the data
per se,
that interests us.
Description:
the stage in which we seek to describe the data-generating process
in cases for which we have data from that process. Description would answer
questions such as: What is the range of prostate volumes for a sample of
Statistics in Medicine. DOI: http://dx.doi.org/
10.1016/B978-0-12-384864-2.00001-9
©
2012 Elsevier Inc. All rights reserved.
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CHAPTER 1
Planning Studies: From Design to Publication
urology patients? What is the difference in average volume between patients
with negative biopsy results and those with positive results?
Explanation:
the stage in which we seek to
infer
characteristics of the (over-
all) data-generating process when we have only part (usually a small part) of
the possible data. Inference would answer questions such as: For a sample
of patients with prostate problems, can we expect the average of volumes of
patients with positive biopsy results to be less than those of patients with nega-
tive biopsy results, for all men with prostate problems? Such inferences usually
take the form of tests of hypotheses.
Prediction:
the stage in which we seek to make predictions about a characteristic
of the data-generating process on the basis of newly taken related observations.
Such a prediction would answer questions such as: On the basis of a patient’s
negative digital rectal examination, prostate-specific antigen (PSA) of 9, and
prostate volume of 30 ml, what is the probability that he has prostate cancer?
Such predictions allow us to make decisions on how to treat our patients to
change the chances of an event. For example: Should I perform a biopsy on my
patient? Predictions usually take the form of a mathematical model of the rela-
tionship between the predicted (dependent) variable and the predictor (inde-
pendent) variables.
PHASE I–IV STUDIES
Stages representing increasing knowledge in medical investigations often are
categorized by
phases.
A
Phase I
investigation is devoted to discovering if a treat-
ment is safe and in gaining enough understanding of the treatment to design
formal studies. For example, a new drug is assessed to learn the level of dosage
to study and if this level is safe in its main and side effects. A
Phase II
investi-
gation is a preliminary investigation of the effectiveness of treatment. Is this
drug more effective than existing drugs? Is the evidence of effectiveness strong
enough to justify further study?
Phase III
is a large-scale verification of the early
findings, the step from “some evidence” to “proof” (or, more exactly, close
enough to proof to be accepted in general medical practice). The drug was
shown more effective on several Phase II studies of 20 or 30 patients each over
a scatter of subpopulations; now it must be shown to be more effective on,
perhaps, 10 000 patients, in a sample comprehensively representing the entire
population. In
Phase IV,
an established treatment is monitored to detect any
changes in the treatment of a population of patients that would affect its use.
Long-term toxicities must be detected. It must be determined if a microorgan-
ism being killed by a drug can evolve to become partially immune to it.
1.3
SCIENCE UNDERLYING CLINICAL DECISION
MAKING
The Scientific Method
Science
is a collection of fact and theory resting on information obtained by
using a particular method that is therefore called the scientific method. This
1.3 Science Underlying Clinical Decision Making
method is a way of obtaining information constrained by a set of criteria. The
method is required to be objective; the characteristics should be made explicit
and mean the same to every user of the information. The method should be
unbiased, free of personal or corporate agendas; the purpose is to
investigate
the truth and correctness of states and relationships, not to “prove” them. The
true scientific approach allows no preference for outcome. The method should
involve the control of variables; ideally it should eliminate as far as practicable
all sources of influence but one, so that the existence of and extent of influ-
ence of that one source is undeniable. The method should be repeatable; other
investigators should be able to repeat the experiment and obtain the same
results. The method should allow the accumulation of results; only by accumu-
lation does the information evolve from postulate to theory to fact. The scien-
tific method is the goal of good study design.
3
Jargon in Science
Jargon may be defined as technical terminology or as pretentious language. The
public generally thinks of it as the latter. To the public,
carcinoma
is jargon for
cancer,
but to the professional, technical connotation is required for scientific
accuracy. We need to differentiate between jargon for pomposity and jargon for
accuracy, using it only for the latter and not unnecessarily. The same process
occurs in statistics. Some statistical terms are used loosely and often errone-
ously by the public, who miss the technical implications. Examples are
ran-
domness, probability,
and
significance.
Users of statistics should be aware of the
technical accuracy of statistical terms and use them correctly.
Evidence
The accumulating information resulting from medical studies is evidence. Some
types of study yield more credible evidence than others. Anecdotal evidence,
often dismissed by users seeking scientific information, is the least credible, yet
is still evidence. The anecdotal information that patients with a particular dis-
ease often improve more quickly than usual when taking a certain herb may give
the rate of improvement but not the rate of failure of the treatment. It may serve
as a candle in a dark room. However, such evidence may suggest that a credible
study be done. The quality of the study improves as we pass through registries,
case-control studies, and cohort studies, to the current gold standard of credibil-
ity, the randomized controlled prospective clinical trial (RCT). (See Sections 1.5
and 1.6 for more information on types of studies.) It is incumbent on the user
of evidence to evaluate the credibility of the cumulative evidence: number of
accumulated studies, types of studies, quality of control over influencing factors,
sample sizes, and peer reviews. Evidence may be thought of as the blocks that
are combined to build the scientific edifice of theory and fact. The more solid
blocks should form the cornerstones and some blocks might well be rejected.
Evidence versus Proof
The results of a single study are seldom conclusive. We seldom see true
proof in science. As evidence accrues from similar investigations, confidence
4
CHAPTER 1
Planning Studies: From Design to Publication
increases in the correctness of the answer. The news media like to say, “The jury
is still out”. In a more accurate rendition of that analogy, the jurors come in
and lodge their judgment one at a time – with no set number of jurors.
Evidence-Based Medicine
Evidence-based medicine (EBM) melds the art and science of medicine.
Evidence-based medicine is just the ideal paradigm of health care practice, with
the added requirement that updated credible evidence associated with treat-
ment be sought, found, assessed, and incorporated into practice. It is much the
way we all think we practice, but it ensures consideration of the evidence com-
ponents. It could be looked at somewhat like an airliner cockpit check; even
though we usually mentally tick off all the items, formal guides verify that we
have not overlooked something.
One rendition of the EBM sequence might be the following: (1) we acquire
the evidence: the patient’s medical history, the clinical picture, test results, and
relevant published studies. (2) We update, assess, and evaluate the evidence,
eliminating evidence that is not credible, weighting that remaining evidence
according to its credibility, and prioritizing that remaining according to its
relevance to the case at hand. (3) We integrate the evidence of different types
and from difference sources. (4) We add non-medical aspects, for example,
cost considerations, the likelihood of patient cooperation, and the likelihood
of patient follow-up. (5) Finally, we embed the integrated totality of evidence
into a decision model.
1.4
WHY DO WE NEED STATISTICS?
Primary Objective
A primary objective of statistics is to make an inference about a population
based on a sample from that population.
Population versus Sample
The term
population
refers to all members of a defined group and the term
sam-
ple
to a subset of the population. As an example, patients in a hospital would
constitute the entire population for a study of infection control in that hospi-
tal. However, for a study of infected patients in the nation’s hospitals, the same
group of patients would be but a tiny sample. The same group can be a sample
for one question about its characteristics and a population for another question.
Objective Restated
The symbol
α
is assigned to the chance of being wrong if we decide a treatment
difference exists. We may restate this common objective of statistics as follows:
based on a sample, we conclude that a treatment difference exists in the popula-
tion if the risk of being wrong (a false positive difference) is less than an agreed
upon
α.
For example, of 50 urgent care patients with dyspepsia who are given
1.5 Concepts in Study Design
no treatment, 30 are better within an hour and of 50 given a “GI cocktail” (ant-
acid with viscous lidocaine), 36 are better within an hour. In order to decide
if the treatment is effective in this sample, in that the condition of 20% more
treated than untreated patients showed improvement for these 100 patients, we
calculate the probability that an improvement of this magnitude would have
happened by chance. The question for statistics to answer is: is it likely to work
for all patients, or was the result for this sample “luck of the draw”?
5
What Statistics Will Not Do for Us
Statistics will not make uncertainty disappear. Statistics will not give answers
without thought and effort. Statistics will not provide a credible conclusion
from poor data, that is, to use an old maxim, it won’t make a silk purse out
of a sow’s ear. It is worth keeping in mind that putting numbers into a for-
mula
will
yield an answer and the process
will not
inform the user whether the
answer is credible. The onus is on the user to apply credible data in a credible
manner to obtain a credible answer.
What Statistics Will Do for Us
There is no remedy for uncertainty, but statistics allows you to
measure and control
uncertainty.
This benefit is one of the most crucial and critically important bases
for scientific investigation. In addition, statistics can assist us to do the following:
n
n
n
n
n
Clarify our exact question
Identify the variable and the measure of that variable that will answer that
question
Verify that the planned sample size is adequate
Test our sample to see if it adequately represents the population
Answer the question asked, while limiting the risk for error in our decision.
Allowing us to follow strands of evidence obscured by myriad causes
Allowing us to mine unforeseen knowledge from a mountain of data
Providing the credibility for the evidence required in EBM
Reducing the frequency of embarrassing mistakes in medical research.
Other benefits of statistics include:
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1.5 CONCEPTS IN STUDY DESIGN
Components of a Study
A medical
study
is an experiment or gathering of data in a designed fashion
in order to answer a specific question about a population of patients. A study
design may be involved, approaching the arcane. Breaking it into the compo-
nents used in constructing a study will simplify it. The basic steps are:
n
n
Specify, clearly and unequivocally, a question to be answered about an
explicitly defined population
Identify a measurable variable capable of answering the question

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