(1) Basic Principles of Research Design
Transcript of (1) Basic Principles of Research Design
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Research fields in medicine
Biological sciences Biology of disease
Clinical sciences
Information to care forindividual patients Clinical Epidemiology Population sciences
Epidemiology Study of disease occurring in human population
Health services Study of how non-biological factorsaffect the patients
health
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Clinical epidemiology The science of making predictions about
individual patients
By counting clinical events of similarpatients
And using strong scientific methods
To ensure that the predictions areaccurate
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Purpose of clinical epidemiology
Develop and apply methods of clinical
observation that will lead to valid
conclusions by avoiding being misleadby systematic error and the play of
chance
Obtaining the kind of information
clinicians need to make good decision in
the care of patients
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Clinical epidemiology
It is clinical
it answers clinical questions
It guides clinical decision making
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Evidence-based medicine
Application of clinical epidemiology to
the care of the patient
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Basic principles
Clinical question
Variables
Things that vary and can be measured Dependent vs. independent variables Health outcomes
Numbers and probability Quantitative vs. qualitative
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Clinical question
Is the patient sick or well (abnormality)
How accurate are tests used to diagnose disease (diagnosis)
How often does a disease occur (frequency)
What factors are associated with an increased risk of disease(risk)
What are the consequences of having a disease (prognosis)
How does treatment change the course of disease (treatment)
Does an intervention on well people keep disease from arising(prevention)
What lead to disease (cause)
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Health outcomes
Events that can be studied directly in intact
humans only
Include the five Ds Disease Discomfort Dissatisfaction
Disability Death
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Numbers and probability
Clinical science depends on quantitative measures
Impressions, instincts and beliefs are only importantwhen added to a solid grounds of numericalinformation
This allows for better confirmation And estimation of error
Prediction of treatment outcomes or diseasesequence Better be expressed as a percentage
(Probability) needs to be expressedquantitatively Estimated by referring to past experience with groups of
similar patients
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Populations
All people in a defined setting with
certain defined characteristics Examples:
The general population
A hospitalized population
A population of patients with a specificdisease
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A sample
Is a subset of people in the defined population
Selected from that population It is not practical to test all the population
Clinical research is carried out on samples
A sample makes inference about the
population
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Two important points in sampling
Are the conclusions of the research
correct for the people in the sample?
If so, does the sample represent fairlythe population of interest?
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A sample is representative
Depends on how a sample was selected
Equal chance for all members vs.
misrepresentation
Computerized programs for selection of
samples
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Definition:
A process at any stage of inference tendingto produce results that depart systematically
from the true values
Any trend in the collection, analysis,interpretation, publication, or review of the
data that can lead to conclusions that aresystematically different from the truth
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Categories of bias
Selection bias
Measurement bias
Confounding bias
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Occurs when comparisons are made
between groups of patients that differ in
ways other than the main factors understudy
Example:
Examine dental caries among different age
groups
Examine perio condition without adjustmentfor smoking
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Occurs when the methods ofmeasurement are not similar amongdifferent groups of patients
Examples Examine dental caries visually vs.
radiographically
Examine the WL of Roots using differenttechniques
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Confounding bias
Occurs when two factors or processes are
associated or "travel together " and the effect
of one is confused with or distorted by the
effect of the other
Example:
TG and cholesterol levels are associated with riskfor coronary heart disease
Education and/or income with good health Folic acid vs. lower rates of colon cancer
People taking multivitamins are health consciousabout diet and exercise
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Confounding bias
A variable is not confounded if it is
directly along the path from cause to
effectA confounding variable is not necessarily
a cause itself
May be related to the suspected cause and
the effect in an instance but not related in
nature
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Selection bias is an issue in patients
selection for observation, and so it is
important in the design of a study
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Confounding bias is an issue in
analysis of the data, once the
observations have been made
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Often in the same study more than one
bias operates
A distinction must be made betweenthe potential for bias and the actual
presence of bias in a particular study
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Dealing with bias
Identification of bias
Measuring the potential effect of bias
Modifying the research design when thepotential effect on the result is big Changing the conclusions in a clinically
meaningful way when the effect is not big
enough
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Unbiased samples may misrepresent the
population because of chance
Chance is the divergence of anobservation on a sample from the true
population value
is called also random variation
Example: Tossing a coin 100 times The larger the sample size the less the
chance
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Chance vs. bias
Bias distorts the situation in one directionor another
Chance / random variation results in anobservation above the true value aslikely as one below it. The mean of many unbiased observations of
a sample approximates the true observationof the population
In small samples this may not be close to thetrue observation of the population
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Bias can be prevented by properconduction of clinical investigations
Bias can be corrected through properdata analysis
Chance cannot be eliminated
Its influence can be reduced byproper research design
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Statistics can be used to estimate the
probability of chance or random
variation Chance cant be eliminated, but its
influence can be reduced by proper
design of research
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9080
bias
chance
SphygmomanometerIntra-arterial canula
True BP BP measurement
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Truth
Validity is correspondence to the true value
measured or searched for
For an observation to be valid, it must be
neither biased nor incorrect due to chance
Types
Internal validity External validity
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Is the degree to which the results of a study
are correct forthe patients being studied
Internal
Applies to the conditions of the particular group ofpatients being observed and not to others
Is determined by how well the design, data
collection and analyses are conducted and
threatened by biases and random variation
Necessary but not sufficient by itself
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(Generalizability)
Is the degree to which the results of anobservation hold true in other settings
The answer of:
Assuming that the results are true in other settings,do they apply to my patients as well? Generalizability assumes that patients in a
study are similar to other patients
A study with high internal validity may bemisleading if its results are generalized to thewrong patients
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A B
Conclusion
sampling
Selectionbias
sample sample
population
patients
chance
External validityGeneralizability
Internal validity
Measurement & confounding
bias
?
?
All patients with condition of interest