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.