RACE 622 :Study Designs & Measurements in Clinical Epidemiology
Assoc.Prof.Dr.Atiporn Ingsathit
Semester 1 Academic year 2017
Doctor of Philosophy Program in Clinical Epidemiology, Master of Science Program in Medical Epidemiology Section for Clinical Epidemiology & Biostatistics
Faculty of Medicine Ramathibodi Hospital, Mahidol University
CONTENTS
1. Clinical research overview .................................................................................................... 4
Initial step of how to starting &conducting research ............................................................ 6
Validity in Research field vs. Real world ............................................................................... 7
Study design .......................................................................................................................... 9
Experimental study ................................................................................................................ 9
Observational study ............................................................................................................... 9
Descriptive study ................................................................................................................. 10
Analytic study ...................................................................................................................... 10
Measurement ....................................................................................................................... 15
Reliability and Validity .......................................................................................................... 16
Precision .............................................................................................................................. 18
Errors ................................................................................................................................... 19
The P value and Significant ................................................................................................. 21
Confidence Intervals ............................................................................................................ 22
Steps to conduct research .................................................................................................. 23
How are broad topics of research question? ...................................................................... 24
Bias ...................................................................................................................................... 24
Confounding ........................................................................................................................ 27
Health services& policy Research ...................................................................................... 28
2. Regulatory environment ...................................................................................................... 29
3. Team responsibility and dynamics ..................................................................................... 30
4. Summary ............................................................................................................................. 31
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OBJECTIVES
1) Understand the terms and definitions of clinical research and related fields of medical
research such as informatics, biostatistics and clinical epidemiology
2) Known the categories of clinical research and evidence users
3) Understand process and steps to conduct clinical research
4) Understand and can differentiate of study design and types of study designs
5) Understand and can apply Measurement in Clinical Research
6) Known and Understand about validity (internal vs external), reliability and accuracy,
power, p-value , confidence interval, precision and significant level in clinical research
7) Understand and explain type and key differences among research types in terms of
observational, experimental, descriptive and analytic researches.
8) Understand and explain steps to conduct research step by step.
9) Understand and criticise the differences of bias and confounding factors and
meaning/differences among bias types
10) Know concepts of team responsibility/duties and team dynamics when conducting
clinical research.
11) Learn and understand about regulatory environment of clinical research
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REFERENCES
1. Fletcher R, Robert Fletcher MDM, Fletcher SW. Clinical Epidemiology: The
Essentials: Wolters Kluwer Health; 2013.
2. Gordis L. Epidemiology: Elsevier Health Sciences; 2013.
3. Guyatt G, Rennie D, Meade M, Cook D. Users' Guides to the Medical Literature: A
Manual for Evidence-Based Clinical Practice, Second Edition: A Manual for
Evidence-Based Clinical Practice, Second Edition: McGraw-Hill Education; 2008.
4. Haynes RB. Clinical Epidemiology: How to Do Clinical Practice Research: Wolters
Kluwer Health; 2012.
5. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology: Wolters Kluwer
Health/Lippincott Williams & Wilkins; 2008.
SUGGESTED READING
1. Fletcher R, Robert Fletcher MDM, Fletcher SW. Clinical Epidemiology: The Essentials:
Wolters Kluwer Health; 2013.
2. Guyatt G, Rennie D, Meade M, Cook D. Users' Guides to the Medical Literature: A
Manual for Evidence-Based Clinical Practice, Second Edition: A Manual for
Evidence-Based Clinical Practice, Second Edition: McGraw-Hill Education; 2008.
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HEALTH SCIENCE RESEARCH OVERVIEW
1. CLINICAL RESEARCH OVERVIEW
Clinical research is a branch of healthcare science that determines the safety and
effectiveness of medications, devices, diagnostic products and treatment regimens intended
for human use. These may be used for prevention, treatment, diagnosis or for relieving
symptoms of a disease. In other word, the clinical research may combine the meaning of
Research (Creative work undertaken systematically to increase the stock of knowledge or
Health Science Research) plus with Health Science (Applied science dealing with health).
Clinical research is research that directly involves a particular person or group of people or
that uses materials from humans, such as their behavior or samples of their tissue. But, a
clinical trial is one type of clinical research that follows a pre-defined plan or protocol.
WHO mention about the clinical research are multidisciplinary tasks that consists of purposes
of registration, a clinical trial is any research study that prospectively assigns human
participants or groups of humans to one or more health-related interventions to evaluate the
effects on health outcomes. Interventions include but are not restricted to drugs, cells and
other biological products, surgical procedures, radiological procedures, devices, behavioral
treatments, process-of-care changes, preventive care, etc.
In some categories, they divide these clinical research into many branches of well
understand of their objectives and method of actions such as “Epidemiology (the branch of
medicine that deals with the incidence, distribution, and possible control of diseases and
other factors relating to health, or the study of how often diseases occur in different groups of
people and why(Epidemiological information is used to plan and evaluate strategies to
prevent illness and as a guide to the management of patients in whom disease has already
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developed: BMJ)”. “Biostatistics & informatics (the rigorous and objective conversion of
medical and/or biological observations into knowledge or application of statistics to a wide
range of topics in human biology/clinical field &the science of computer information systems
that involves the practice of information processing, and the engineering of information
systems and “Health services & policies(the multidisciplinary field of scientific investigation
that studies how social factors, financing systems, organizational structures and processes,
health technologies, and personal behaviors affect access to health care, the quality and cost
of health care, and ultimately our health and well-being)”.
All of these which aim to solve the health science problems, how to Measuring in
health, evaluating health services, knowing the association and causality, choosing the
appropriate study designs, concerning of confounding factors & interactions, applying
epidemiology in health policy.
Clinical Epidemiologyis the term of science that studies the patterns, causes, and
effects of health and disease conditions in defined populations. It is the cornerstone of public
health, and informs policy decisions and evidence-based practice by identifying risk factors
for disease and targets for preventive healthcare. The common measurement that we
common uses in epidemiology can be classified as epidemiologic scales and health scales,
or classified as type of study designed as observational and experimental studies, or
determined and concerned of confounding interactions, errors and bias in the research, or
studied about association and causality, etc.
In general perspective of users, we can divide types of health care professional into
4 types that are consists of evidence users (clinicians, policy maker), evidence generator
(researcher, nurse, doctors), evidence finders (student, researcher or clinicians, general
people)though evidence Ignorer, see figure 1.
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Figure 1.Types of health care professional in clinical epidemiology
Initial step of how to starting &conducting research Once we have an idea to conduct the research usually it comes from clinical
question(s) or uncertainly issue(s) or discussion(s) (step 1). Then, we emphasize the research
need such as it new and novel, reasonable to do, has a budget support, and directly answer
our clinical questions (step 2). For this point, we need to realize that if research question(s)
and requirement(s) are still needed we go on the next design step (step 3) else we stop to
conduct the research. For these important steps are consists of how to select the population
and setting, methodology, measurements, considered statistical analysis to infer the result to
population or sample, see Figure 2.
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Figure 2. 3 Steps to conduct the research.
Validity in Research field vs. Real world Validity is the extent to which a concept, conclusion or measurement is well-founded
and corresponds accurately to the real world. The word "valid" or validity is derived from the
Latin validus, meaning strong. The validity of a measurement tool (for example, a test in
education) is considered to be the degree to which the tool measures what it claims to
measure. In statistical view, we can classify the validity in 2 types as internal validity and
external validity.
“Internal validity” is the approximate truth about inferences regarding cause-effect or
causal relationships. Thus, internal validity is only relevant in studies that try to establish a
causal relationship is an inductive estimate of the degree to which conclusions about causal
relationships can be made (e.g. cause and effect), based on the measures used, the research
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setting, and the whole research design. The good experimental techniques, in which the
effect of an independent variable on a dependent variable is studied under highly controlled
conditions, usually allow for higher degrees of internal validity than, for example, single-case
designs.
“External validity” concerns the extent to which the (internally valid) results of a study
can be held to be true for other cases, for example to different people, places or times. In
other words, it is about whether findings can be validly generalized. If the same research
study was conducted in those other cases, would it get the same results?Other factors
jeopardizing external validity are 1) Reactive or interaction effect of testing, a pretest might
increase the scores on a posttest 2) Interaction effects of selection biases and the
experimental variable 3) Reactive effects of experimental arrangements, which would
preclude generalization about the effect of the experimental variable upon persons being
exposed to it in non-experimental settings 4) Multiple-treatment interference, where effects of
earlier treatments are not erasable (See figure 3).
Figure 3. Demonstrated the internal validity vs. external validity
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Study design Before knowing the types of research designs it is important to be clear about the role
and purpose of research design. We need to understand what research design is and what
it is not. We need to know where design was planned into the whole research process from
framing a question to consider exposure to study group, exposure comes before or in the
same period of outcome, or outcome lead to find an exposure, and finally analyzing and
reporting of the research outcome.
Experimental study A study design used to test cause-and-effect relationships between variables. The
classic experimental design specifies an experimental group and a control group. The
independent variable is administered to the experimental group and not to the control group,
and both groups are measured on the same dependent variable. Subsequent experimental
designs have used more groups and more measurements over longer periods. True
experiments must have control, randomization, and manipulation are involved. These
subtypes of this group consist of RCT, quasi RCT and non-RCT research etc.
Observational study The observational studies attempt to understand cause-and-effect relationships.
However, unlike experiments, the researcher is not able to control how subjects are assigned
to groups and/or which treatments each group receives.
For example, a sample survey, does not apply a treatment to survey respondents. The
researcher only observes survey responses. Therefore, a sample survey is an example of an
observational study. The observational study can categorized in 2 subgroup as analytic study
and descriptive study by present and absent of comparison group.
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Descriptive study The process of analytical is also important. In general, we categorized in two main
types of analysis which are descriptive and analytic. Both of analysis types have useful. For
Descriptive study, the descriptive analysis can analyzed in trend analysis (forecasting),
planning, and clues about cause (generate hypothesis). But it disadvantages are they cannot
do conclusions about cause of disease (only show association or correlation) and cannot
over- or misinterpretation of data (absence of clear, specific, and reproducible of case
definition)
Descriptive analysis is used to describe the basic features of the data in a study. They
provide simple summaries about the sample and the measures. Together with simple
graphics analysis, they form the basis of virtually every quantitative analysis of data
Descriptive analysis also used to present quantitative descriptions in a manageable
form. In a research study we may have lots of measures. Or we may measure a large number
of people on any measure. Descriptive statistics help us to simplify large amounts of data in
a sensible way. Each descriptive statistic reduces lots of data into a simpler summary.
Analytic study A comparative study designed to reach causal inferences about hypothesized
relationships between risk factors and outcome. Analytical studies identify and quantify
associations, test hypotheses, identify causes and determine whether an association exists
between variables, such as between an exposure and a disease. Statistical procedures are
used to determine if a relationship is likely to have occurred by chance alone. Analytical
studies usually compare two or more groups or sets of data.
Types of analytic studies consist of case-control study, cohort study, randomized-
controlled clinical trial etc. (Table 1)
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The different study designs can provide the information of the results in different
quality. Of course, the researcher always try to use the best possible design, but
sometimes this is not practical or ethically acceptable such as researcher cannot do an
experiment to expose some people to a harmful substance to see what effect it has (e.g.
RCT of Unproved AIDs vaccine to the volunteers) Therefore, we need to understand the
strengths and limitations of each type of study design, as applied to a particular research
purpose. The purposes we will consider include (1) describing the prevalence of health
problems; (2) identifying causes of health problems (etiological research), and (3)
evaluating therapy, that including treatment and prevention.
We can conclude that study design is researcher’s plan of action for answering the
research question(s). For this, we need to maximize the reliability and validity of data and
minimize possible biases and errors which can be occurred.
Types of study design, the main group of study designed is distinguishing between
observational and experimental studies. In observational studies, the researcher observes
and systematically collects information, but does not try to change or modified the people
(or animals, or reagents) that being observed. In an experiment, by contrast, the researcher
intervenes to change something (e.g., gives some patients a drug) and then observes what
happens. In an observational study there is no intervention.
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Examples of observational studies:
1) A survey of drinking habits among students;
2) A researcher who joins a biker gang to study their lifestyle (note, as long as the
researcher does not try to change their behavior, it's an observational study);
3) Taking blood samples to measure blood alcohol levels during Monday morning
lectures (yes, you are intervening to take the blood, but you are not trying to
change the blood alcohol level: it's just a measurement).
Examples of experiments studies:
1) Plying a law student with beer to see whether lawyers argue better when drunk;
2) Encouraging bikers in one group to stop smoking those funny-looking cigarettes
to see whether they get less belligerent;
3) Warning one group of students that you are going to take blood alcohol levels
next Monday to test for alcohol, and comparing their levels to another group that
you did not warn.
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Table 1: Type of research studies and advantages, disadvantages of studies
Purpose/Design of studies Advantages Disadvantages
1) Cross-Sectional Study 1.1 Study about the characteristics of a
population at one point in time (like a photo “snap shot”)
1.2 No comparison group 1.3 Population use all members of a small,
defined group or a sample from a large group
1.4 Results are estimates of the prevalence of the population characteristic of interest
- Inexpensive, simple (no follow-up) - No exposure, No drop out
- Can establish association but not causation
- Cannot control confounder Recall bias Incidence-prevalence bias
2) Case Control study 2.1 To study rare diseases 2.2 To study multiple exposures that may be
related to a single outcome 2.3 Study Subjects Participants selected
based on outcome status
Case-subjects have outcome of interest
Control-subjects do not have outcome of interest
- Quickly and inexpensive - Feasible for rare disorder or long
follow-up - May required fewer subjects
- Recall Bias - More effect of confounder - Difficult to find control group
3) Cohort Study 3.1 Participants classified according to
exposure status and followed-up over time to ascertain outcome
3.2 Can be used to find multiple outcomes from a single exposure
3.3 Ensures temporality (exposure occurs before observed outcome)
- Can be standardized in eligible criteria & outcome assessment
- Can establish temporal association
- Usually expensive - Hard to blind - Long follow-up period for rare
disorder - Difficult to find controls and
confounders
4) Randomized Control Trial - Confounding variables can be balance by randomization
- -Blinding of subjects, medical staff and investigators are achievable
- Costly in term of time and money - Dropout or loss to follow-up are
common events - Need time for final results
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For the differentiation of study types, we can assess by use the Figure 4. The first one
step the researcher should to know that the exposure is assigned? If assigned exposure to
sample/population, it categorizes in type of experimental study (RCT, Quasi-RCT, non-RCT).
In other word, If researcher is not assigned exposure to sample/population is categorized as
observational study which can sub-categorize in descriptive and analytical study by absent
or present of comparison group. For analytical study (has comparison group), we intend to
interest on timeframe of the exposure and outcome. When exposure comes before outcome
we classified as cohort study, if outcome bring to study for exposure is classified as case-
control study. But if the exposure and outcome happen in same timeframe period we call this
type as cross sectional study, see Figure 4-5
Figure 4. Flow of differentiate of study designs by key elements
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Figure 5. Types of Study designs and their advantages
Measurement Measurement is at the core of doing research. Measurement is the assignment of
numbers to things. In almost all research, everything has to be reduced to numbers
eventually. Precision and exactness in measurement are vitally important. The measures are
what are actually used to test the hypotheses. A researcher needs good measures for both
independent and dependent variables.
Generally, the research measurement consists of two basic processes called
conceptualization and operationalization, then an advanced process called determining the
levels of measurement (nominal, ordinal, interval, ratio) , and then even more advanced
methods of measuring reliability and validity
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In meaning of validity is also inferred to accuracy. High internal validity of the results
can be considered a good approximation to the truth. Furthermore if research has high
external validity of the results, it means this result can be applied/generalized outside the
study.
Reliability and Validity For a research study to be accurate, its findings must be reliable and valid.
Reliability means that the findings would be consistently the same if the study were done over
again. It sounds easy, but think of a course exam in you PhD Class; if you scored a 85 on that
exam, don't you think you would score differently if you took if over again? Validity refers to
the truthfulness of findings; if you really measured what you think you measured, or more
precisely, what others think you measured. Again, think of a typical multiple choice exam in
college; does it really measure proficiency over the subject matter, or is it really measuring
IQ, age, test-taking skill, or study habits?
A study can be reliable but not valid, and it cannot be valid without first being
reliable. You cannot assume validity no matter how reliable your measurements are. There
are many different threats to validity as well as reliability, but an important early consideration
is to ensure you have internal validity. This means that you are using the most appropriate
research design for what you're studying (experimental, quasi-experimental, survey,
qualitative, or historical), and it also means that you have screened out spurious variables as
well as thought out the possible contamination of other variables creeping into your study.
Anything you must do to standardize or clarify your measurement instrument to reduce user
error will add to your reliability (see Figure 6).
It's also important early on to consider the time frame that is appropriate for what
you're studying. Some social and psychological phenomena (most notably those involving
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behavior or action) lend themselves to a snapshot in time. If so, your research need only be
carried out for a “short period” of time, perhaps a few weeks or a couple of months. In such
a case, your time frame is referred to as cross-sectional. Sometimes, cross-sectional research
is criticized as being unable to determine cause and effect, and a longer time frame is called
for (e.g. Diseases that have long incubation period such as CA lung and smoking habit), one
that need is called longitudinal study, which may add years onto carrying out your research.
There are many different types of longitudinal research, such as those that involve tracking a
cohort of subjects (such as schoolchildren across grade levels), or those that involve time-
series (such as tracking a third world nation's economic development over four years or so).
The general rule is to use longitudinal research the greater the number of variables you've
got operating in your study and the more confident you want to be about cause and effect.
Figure 6. Reliability and Validity of measurement
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Precision
Accuracy and precision are defined in terms of systematic and random errors or the
quality of being sharply defined or stated. The researcher usually needs the result with high
precision to reduce random error.Accuracy is the proximity of measurement results to the true
value; precision, the repeatability, or reproducibility of the measurement. (Figure 7-8)
Figure 7. Accuracy and precision
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Figure 8. Accuracy and precision
Errors Always some amount of error in every statistical analysis that researcher cannot avoid
it happen, how much can wetolerate? The process of hypothesis testing can seem to be quite
varied with a multitude of test statistics. But the general process is the same. Hypothesis
testing involves the statement of a null hypothesis, and the selection of a level of significance.
The null hypothesis is either true or false, and represents the default claim for a treatment or
procedure. For example, when examining the effectiveness of a drug, the null hypothesis
would be that the drug has no effect on a disease.
After formulating the null hypothesis and choosing a level of significance, we acquire
data for analysis through observation. Statistical calculations tell us whether or not we should
reject the null hypothesis.
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In an ideal, we would always reject the null hypothesis when it is false, and we would
not reject the null hypothesis when it is indeed true. But there are two other scenarios that are
possible, each of which will result in an error. There are two kinds of errors, the first one of
errors which by design cannot be avoided, and we must be aware that these errors exist.
These errors are named as “random error”. This error is a portion of variation in a
measurement that has no apparent connection to any other measurement or variable,
generally regarded as due to chance.
The second kind of error is “systematic error”, for this type the researcher can
minimizing it by improve the methodology of research and reduce & prevent of biases.
In statistical hypothesis testing, a type I error is the incorrect rejection of a true null
hypothesis (a "false positive"), while a type II error is the failure to reject a false null hypothesis
(a "false negative")
“Systematic error (bias)’ is a process at any stage of inference tending to produce
results that depart systematically from the true values. In general, we use the tools for
assessing random error by using P value and Confidence Intervals (CI). (See Figure9-10)
Figure 9 .The random error and systematic error by hypothesis testing
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Figure 10.The process of designing and implementing a research project
The P value and Significant A numeric representation of the degree to which random variation alone could
account for the difference observed between groups or data being compared.The probability
of a given (more extreme) finding if no association truly exists. No clearly cut-off point, it is
important not to equate p-value with significant levels. P-value can range from 0.00 to 1.0 and
calculated from research data, not select by researcher. The level of significant is what we
say it was before we calculated our p-value.
If p-value turns out to equal to or less than our level of significance, which means it is
falling in criteria region of the theoretical sampling distribution. That in turn means that our
finding is statistically significant and we can reject the null hypothesis because our p-value
indicate a sufficiently low probability that our results were produced by sampling error. For
example, usually we use level of significance is 0.05 and if we ern p-value=0.04 then our
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finding is statistically significant, and we would risk a type I error. If our level of significance
is 0.05 but our p-value is 0.06 or even 0.051, then we cannot call our finding statistically
significant and we would risk of type II error.
In some research for small sample sizes, setting the level of significance at a higher
probability, such as 0.10, might be warranted. The researcher even see some rare studies
with very large samples that set the level of significance much lover-say at 0.01 not
automatically set it at 0.05 as tradition use. If we are worried about type I error than type II
error. The researcher might opt for lower significant level. But if we more worried about type
II error, we might choose a higher significant level. The lower significance level, the less we
risk a type I error. In the other hand, the higher significance level the more we risk of type I
error but the less we risk of type II error.
Confidence Intervals Provide a plausible range within which the true association lies. The confidence
interval tells us, within the bounds of plausibility, how much greater or smaller the true effect
is likely to be. CI will help toprovide all the information in P values and more. In other word,
we can say that a confidence interval calculated for a measure of treatment effect shows the
range within which the true treatment effect is likely to lie.
Power
Power is ability of a study to detect a true difference, or probability of rejecting Ho
when Ho is fault.
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Steps to conduct research
1. Research question: should be transform your clinical research questions to research
question based on PICO. The study question, the protocol should start with a clear
and precise formulation of the research question. It is good practice to write this in
the form of a question, not a statement (Example: Why is asthma among children in
Bangkok exceptionally frequent?), When we changes relevant with main objectives
even a precise study question is often too broad for one study to answer, like “Why is
asthma among children in Bangkok exceptionally frequent?” You must therefore
break down the question into several objectives.
(Example: The objectives of this study are to determine if the excess asthma in
Bangkok is related to air pollution). Then consider question of what? when? why?
where? How?.
2. Review & literatures searching via primary data sources and secondary data sources.
3. Create study design: protocol writing
4. Perform data collection : Generate Clinical Record From (CRF)
5. Data management: Design and select database, data entry & cleaning
6. Data analysis (statistical analysis)
7. Conclusion
8. Publication and Report
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How are broad topics of research question?
For types of study in another perspective such as diagnosis, causation and risk,
therapeutic and prognosis study usually have relevant analytical research types which are
matched as follow:
Diagnosis: demonstrate that a new diagnosis test is valid and reliable preferred cross-
sectional study
Causation or Risk: determine that a agent is related to development of illness
preferred cohort or case-control study
Therapy: testing the efficacy of interventions preferred randomized controlled trial
Prognosis: determine what happen to someone with some stage of disease preferred
prospective cohort study
Bias Bias is defined as ‘‘any process at any stage of inference which tends to produce
results or conclusions that differ systematically from the truth’’ Bias can arise at three steps of
the study: during initial enrollment of the participants, during implementation of the study, and
during analysis of the findings.
The consistent deviation of analytical results from the "true" value causes by
systematic errors in a procedure. Bias is a term often confused with sampling error. Sampling
error is the natural consequence arising out of the fact that sample size is much less when
compared to the population size. The sampling error can thus be minimized by increasing
the size of the sample. The inaccuracy caused in the estimates of population parameters
attributed to bias is more systematic. The faulty design of the sampling or the mistakes
occurred during the real time survey or both causes the bias in estimates resulting in distorted
description of population.
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Bias is the opposite but most used measure for "trueness" which is the agreement of the mean
of analytical results with the true value, i.e. excluding the contribution of randomness
represented in precision. There are several components contributing to bias:
1) Selection bias is nonrandom selection of study participants leads to erroneous
conclusions or method or conduct to absence of comparability between groups being
studied.
e.g. if investigating the adverse events associated with a new drug, those with either
the best or worst outcomes may be more likely to participate in a telephone survey
about their experience with drug
Type of selection bias which are common in research e.g.
1.1 Berkson Bias (Admission bias, hospital admission bias <>
Gen population)
1.2 Ascertainment bias (incidence of diseases +/-)
1.3 Healthy worker effect (EGAT Good v.s Poor)
1.4 Volunteer Bias (Healthy or diseases sample e.g. MRI brain)
1.5 Non-Response Bias (eg. Questionnaire sexual issue,
confidential issue, not interest issues)
2) Information bias: Incorrect determination of exposure or outcome, or both.
Gathering information in different way e.g.
2.1 Observer biasmeans investigator's evaluation is impacted by
knowledge of exposure status
2.2 Recall bias (esp. case control study) is subjects with the disease are
more likely to recall the exposure of interest e.g. parents of children
with cancer recall exposure to a chemical.
2.3 Reporting bias (Self report or response bias)
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3) Measurement bias is information is gathered in a way that distorts the
information e.g.Hawthorne Effect is bias come from subjects alter their
behavior when they know they are being studied
4) Late-look bias is patients with severe disease are less likely to be studied,
because they die e.g. a group of HIV+ individuals are all asymptomatic
5) Procedure bias is mean different groups not treated the same
6) Lead-time bias is early detection and treatable looks like increase in survival
common with improved screening
7) Pygmalion effect is mean that investigator inadvertently conveys his high
expectations to subjects, who then produce the expected result.
In opposite, a "self-fulfilling prophecy" Golem Effect is the opposite: study
subjects decrease their performance to meet low expectations of
investigator.
8) Design bias is mean the control group is inappropriately non-comparable to
the intervention group
9) Publication biasis a bias with regard to what is likely to be published,
among what is available to be published
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Confounding A confounding variable is associated with the exposure and it affects outcome, but it
is not in an intermediate link in the chain of causation between exposure and outcome. In
other word, confounding factor is a factor that distorts the true relationship of the study
variables of interest by being related to the outcome of interest. e.g. Researcher purposed
effect of hot tea drinking associated with CA stomach, but they not aware for smoking habits
in sample because of smoking is also confounding factor.(Figure 11)
Figure 11. A diagram show smoking is confounding factors between hot tea drinking and CA stomach.
Because of, confounding factors is a third factor is either positively or negatively
associated with both the exposure and outcome and C they are not in the causal pathway if
not adjusted for can distort true association either towards or away from the null hypothesis.
Thus, researcher can prevent this confounding by aware the priorities criteria for confounding
factors, factors have clinically/scientifically sensible, must be a one of risk factor, cannot be
an intervening factor, this factors must be associated with the exposure in the population
(imbalance distribution) and In analysis, the crude estimate has shown not equally to adjusted
estimation.
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Health services& policy Research
Health service & policy Research is a branch or one category in clinical research. It
is the multidisciplinary field of scientific investigation that studies how social factors, financing
systems, organizational structures and processes, health technologies, and personal
behaviors affect access to health care, the quality and cost of health care, and ultimately our
health and well-being. Its research domains are individuals, families, organizations,
institutions, communities, and populations.
(Academy for Health Services Research and Health Policy, 2000) or the research that
examines how people get access to health care, how much care costs, and what happens to
patients as a result of this care. The main goals of health services research are to identify the
most effective ways to organize, manage, finance, and deliver high quality care; reduce
medical errors; and improve patient safety.(Agency for Healthcare Research and Quality,
2002). We can use healthcare statistic to evaluation of health service and apply epidemiology
in health policy.
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2. REGULATORY ENVIRONMENT
We will learn about the regulatory of FDA, GCP (Good Clinical Practice), IRB
(Institutional Review Board) which effect to conducting of Clinical trial. We will learn more
about efficacy study, effectiveness study, and efficiency study. How are they different? in
terminology and meaning in practices.
Good Clinical Practice (GCP) is a standard for clinical studies which encompasses
the design, conduct, monitoring, termination audit, analyses, reporting and documentation of
the studies to ensure that the studies are scientifically and ethically sound.
Institutional Review Board (IRB) is independent body constituted of medical scientific
and non-scientific members, whose responsibility it is to ensure the protection of the rights,
safety, and well-being of human subjects, reviewing, approving, and providing continuing
review of trials.(Figure 10)
Figure 12. Regulatory and roles interactions to conduct research
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3. TEAM RESPONSIBILITY AND DYNAMICS
Clinical Research Team should be have a component of Principal investigator(PI), Co-
investigators, Research project manager, Clinical research coordinator (CRCs) and Clinical
research assistants (CRAs) working together as a team.
Documents needed in a clinical research The documents that need to be address and important to conducting the research
are study protocol, IRB/IEC approval document, Grant application document Instruction
sheets for investigator, Patient information sheet & Informed consent Case record form
&adverse event record form etc. (Figure 11)
Figure 13. Clinical Research Process and Flow, doccumentation and team management
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4. SUMMARY
Overall of this chapter discusses about the overall in clinical research. From the
beginning, the student will understand the types of research. The overall results of research
in aspect of validity to sample group or can apply to general population, common
measurements in terms of validity, variability, precision, power of study, confidence interval
and significant level within the study in both of clinical significant and statistically significant.
We are able to understand and bring these knowledge components to understand or
conducting the clinical research step-by-step follow 8 steps of conducting research. Further
researcher can understand and also take into account the role of participant such as project
investigator, research project, research assistant, and statisticians’ etc.as part of the research
team. In order to properly function properly in team responsibility and dynamics which
accordance with applicable international rules and standards, such as human ethic, IRB
(Institutional Review Board) FDA GCP (Good Clinical Practice) etc. to conduct the clinical
research that has a direct and standards applied to confidently. All students will learn all in
this chapter and in-depth study in the following chapter.